diff --git "a/PHI_4_B70.ipynb" "b/PHI_4_B70.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
+ "47702a85f2374aa8a048b0e777fcb24a",
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+ "26ede1a7e33f43088d50725991173176"
+ ]
+ },
+ "id": "0c24ca36-1782-4ce8-8094-6f6528dada19",
+ "outputId": "d9b96ccd-d766-4bec-84d6-cec0be94b69b"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
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+ "Downloading cut_cross_entropy-25.1.1-py3-none-any.whl (22 kB)\n",
+ "Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, xformers, datasets, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n",
+ " Attempting uninstall: protobuf\n",
+ " Found existing installation: protobuf 4.25.6\n",
+ " Uninstalling protobuf-4.25.6:\n",
+ " Successfully uninstalled protobuf-4.25.6\n",
+ " Attempting uninstall: fsspec\n",
+ " Found existing installation: fsspec 2024.10.0\n",
+ " Uninstalling fsspec-2024.10.0:\n",
+ " Successfully uninstalled fsspec-2024.10.0\n",
+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n",
+ "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0mSuccessfully installed bitsandbytes-0.45.1 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.9 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 trl-0.13.0 tyro-0.9.13 unsloth-2025.1.6 unsloth_zoo-2025.1.5 xformers-0.0.29.post1 xxhash-3.5.0\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/vnd.colab-display-data+json": {
+ "pip_warning": {
+ "packages": [
+ "google"
+ ]
+ },
+ "id": "560c500910134f55a5b1e913b1193b66"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-rfpwch0y\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-rfpwch0y\n",
+ " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: unsloth\n",
+ " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=ef0e3c478d090f4b6851862cc599ffcceec7f61086566ef4814cf3142ebdd57c\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-7ktydid7/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.6\n",
+ " Uninstalling unsloth-2025.1.6:\n",
+ " Successfully uninstalled unsloth-2025.1.6\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "50190966-b741-47c4-915e-43c81fd1413a",
+ "metadata": {
+ "id": "50190966-b741-47c4-915e-43c81fd1413a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6def36c3-55c1-4f94-c3f6-a1887505e6d7"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (3.2.0)\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (4.67.1)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from datasets) (3.17.0)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (1.26.4)\n",
+ "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
+ "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.3.8)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
+ "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.11/dist-packages (from datasets) (2.32.3)\n",
+ "Requirement already satisfied: xxhash in /usr/local/lib/python3.11/dist-packages (from datasets) (3.5.0)\n",
+ "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.70.16)\n",
+ "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n",
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.11)\n",
+ "Requirement already satisfied: huggingface-hub>=0.23.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (0.27.1)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from datasets) (24.2)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from datasets) (6.0.2)\n",
+ "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.4)\n",
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (24.3.0)\n",
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
+ "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
+ "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.23.0->datasets) (4.12.2)\n",
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.4.1)\n",
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2.3.0)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.32.2->datasets) (2024.12.14)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2024.2)\n",
+ "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->datasets) (2025.1)\n",
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n",
+ "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.7)\n",
+ "Collecting git+https://github.com/unslothai/unsloth.git\n",
+ " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-ak5vrwrk\n",
+ " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-ak5vrwrk\n",
+ " Resolved https://github.com/unslothai/unsloth.git to commit bdf0cd6033595be4e7ed23d0d002bb176d343152\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "Building wheels for collected packages: unsloth\n",
+ " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for unsloth: filename=unsloth-2025.1.7-py3-none-any.whl size=174896 sha256=5d3cf53156a67e7e061631f274ef3b3200e4062d509e7b7a5f9b5f6a53ad0299\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-vga350tr/wheels/d1/17/05/850ab10c33284a4763b0595cd8ea9d01fce6e221cac24b3c01\n",
+ "Successfully built unsloth\n",
+ "Installing collected packages: unsloth\n",
+ " Attempting uninstall: unsloth\n",
+ " Found existing installation: unsloth 2025.1.7\n",
+ " Uninstalling unsloth-2025.1.7:\n",
+ " Successfully uninstalled unsloth-2025.1.7\n",
+ "Successfully installed unsloth-2025.1.7\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install datasets tqdm\n",
+ "!pip install unsloth\n",
+ "!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "metadata": {
+ "id": "fbc9900d-28d2-4bda-9848-b572fbe778d2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
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+ ]
+ },
+ "outputId": "a7056ef9-ca32-4e0c-8794-45006cad6a25"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+ "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+ "==((====))== Unsloth 2025.1.7: Fast Llama patching. Transformers: 4.47.1.\n",
+ " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+ "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = False]\n",
+ " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
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+ "output_type": "display_data",
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+ "output_type": "display_data",
+ "data": {
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+ ],
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Unsloth: Will load kanwal-mehreen18/hindi-microsoftphi4-B70 as a legacy tokenizer.\n",
+ "Unsloth 2025.1.7 patched 40 layers with 40 QKV layers, 40 O layers and 40 MLP layers.\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "PeftModelForCausalLM(\n",
+ " (base_model): LoraModel(\n",
+ " (model): LlamaForCausalLM(\n",
+ " (model): LlamaModel(\n",
+ " (embed_tokens): Embedding(100352, 5120, padding_idx=100351)\n",
+ " (layers): ModuleList(\n",
+ " (0-39): 40 x LlamaDecoderLayer(\n",
+ " (self_attn): LlamaAttention(\n",
+ " (q_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (k_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=1280, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (v_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=1280, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=1280, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (o_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (rotary_emb): LlamaRotaryEmbedding()\n",
+ " )\n",
+ " (mlp): LlamaMLP(\n",
+ " (gate_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=17920, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (up_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=5120, out_features=17920, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=5120, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=17920, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (down_proj): lora.Linear(\n",
+ " (base_layer): Linear(in_features=17920, out_features=5120, bias=False)\n",
+ " (lora_dropout): ModuleDict(\n",
+ " (default): Identity()\n",
+ " )\n",
+ " (lora_A): ModuleDict(\n",
+ " (default): Linear(in_features=17920, out_features=16, bias=False)\n",
+ " )\n",
+ " (lora_B): ModuleDict(\n",
+ " (default): Linear(in_features=16, out_features=5120, bias=False)\n",
+ " )\n",
+ " (lora_embedding_A): ParameterDict()\n",
+ " (lora_embedding_B): ParameterDict()\n",
+ " (lora_magnitude_vector): ModuleDict()\n",
+ " )\n",
+ " (act_fn): SiLU()\n",
+ " )\n",
+ " (input_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " (post_attention_layernorm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " )\n",
+ " )\n",
+ " (norm): LlamaRMSNorm((5120,), eps=1e-05)\n",
+ " (rotary_emb): LlamaRotaryEmbedding()\n",
+ " )\n",
+ " (lm_head): Linear(in_features=5120, out_features=100352, bias=False)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
+ "source": [
+ "from unsloth import FastLanguageModel\n",
+ "import pandas as pd\n",
+ "from datasets import load_dataset\n",
+ "import os\n",
+ "import torch\n",
+ "import torch.nn.functional as F\n",
+ "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+ "from tqdm import tqdm\n",
+ "tqdm.pandas()\n",
+ "max_seq_length = 2048\n",
+ "load_in_4bit = False\n",
+ "name = \"kanwal-mehreen18/hindi-microsoftphi4-B70\"\n",
+ "model, tokenizer = FastLanguageModel.from_pretrained(model_name = name, max_seq_length = max_seq_length, load_in_4bit = load_in_4bit,)\n",
+ "model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\",], lora_alpha = 16, lora_dropout = 0, bias = \"none\", use_gradient_checkpointing = \"unsloth\", random_state = 3407, use_rslora = False, loftq_config = None,)\n",
+ "FastLanguageModel.for_inference(model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "##**SINGLE TEST CASE**"
+ ],
+ "metadata": {
+ "id": "FICHwqm5aLUV"
+ },
+ "id": "FICHwqm5aLUV"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "input_text = \"जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ###\"\n",
+ "prompt = f\"### INPUT : {input_text} RESPONSE : \"\n",
+ "message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ "inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ "outputs = model.generate(input_ids=inputs, max_new_tokens=200, use_cache=True, temperature=0.1, min_p=0.1, pad_token_id=tokenizer.eos_token_id)\n",
+ "response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+ "processed_response = response.split(\"### RESPONSE :\\nmodel\")[-1].strip()\n",
+ "print(f\"Generated Response (20 tokens):\\n{processed_response}\\n\")\n",
+ "with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ "token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ "token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ "token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ "token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ "for i, score in enumerate(scores, 1):\n",
+ " probs = F.softmax(score, dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " print(f\"Probability of 'A' at token {i}: {prob_a:.4f}\")\n",
+ " print(f\"Probability of 'B' at token {i}: {prob_b:.4f}\")\n",
+ " print(f\"Probability of 'C' at token {i}: {prob_c:.4f}\")\n",
+ " print(f\"Probability of 'D' at token {i}: {prob_d:.4f}\")"
+ ],
+ "metadata": {
+ "id": "r1dozae-gO5B",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5b52b2fe-3cf0-48b9-ec1e-a6748dd5306f"
+ },
+ "id": "r1dozae-gO5B",
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Generated Response (20 tokens):\n",
+ "<|im_start|> user <|im_sep|> ### INPUT : जोड़ें 46,911 + 653,092 ### A) 699,903 B) 700,003 C) 913,203 D) 1,122,202 ### MCQ ### RESPONSE : <|im_start|> assistant <|im_sep|> जोड़ने के लिए 46,911 और 653,092 को, हम इस प्रकार करते हैं:\n",
+ "\n",
+ "```\n",
+ " 46,911\n",
+ "+653,092\n",
+ "---------\n",
+ " 700,003\n",
+ "```\n",
+ "\n",
+ "इसलिए, सही उत्तर है B) 700,003.\n",
+ "\n",
+ "Probability of 'A' at token 1: 0.0017\n",
+ "Probability of 'B' at token 1: 0.0006\n",
+ "Probability of 'C' at token 1: 0.0002\n",
+ "Probability of 'D' at token 1: 0.0006\n",
+ "Probability of 'A' at token 2: 0.0000\n",
+ "Probability of 'B' at token 2: 0.0000\n",
+ "Probability of 'C' at token 2: 0.0000\n",
+ "Probability of 'D' at token 2: 0.0000\n",
+ "Probability of 'A' at token 3: 0.0000\n",
+ "Probability of 'B' at token 3: 0.0000\n",
+ "Probability of 'C' at token 3: 0.0000\n",
+ "Probability of 'D' at token 3: 0.0000\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE ENGLISH**"
+ ],
+ "metadata": {
+ "id": "7Al9PZfU2bhu"
+ },
+ "id": "7Al9PZfU2bhu"
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "metadata": {
+ "id": "1749c745-d1fb-430b-9469-4913bb2a6cb5",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
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+ ]
+ },
+ "outputId": "024f7792-624a-4d6b-e473-42ff957a3419"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Challenge_E.csv: 0%| | 0.00/367k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "220891f1dac54bc596757e3d705e20f3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ca900b64057b41259fcee0e089c5febd"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 70.86689419795222\n",
+ "Max 'tok' value: 199\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1172/1172 [04:35<00:00, 4.25it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 314\n",
+ "C 302\n",
+ "D 280\n",
+ "A 276\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9241\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_eng:.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC CHALLENGE HINDI**"
+ ],
+ "metadata": {
+ "id": "PubN4p-32_EC"
+ },
+ "id": "PubN4p-32_EC"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Challenge_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_c_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_c_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "mPFAiosJ3jzD",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "5609f25faca74edb86dc5969a36ff3e5",
+ "9fd635f92bad4ba3bcd19c9fd6eef97f",
+ "3afd425fa1fd45a68987e821b3641147",
+ "04821ac1fb4a444b86b9aa697c456a30",
+ "d79e52776f244f9ab9a7a2a9f24e38a4",
+ "3aeae6ae7cba48818d67c3c424897236",
+ "4f950d288c084bd38e283af3ca6f0bff",
+ "edf64c8937d845929ef160b9e9290354",
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+ "2dc1b296401341cabc972b0975f430b9",
+ "848100599f1344298c56c9e4d0670244",
+ "cc0d5f53851f4b38841742d92e79bb29",
+ "710059e3bacc43bd904c31cdfbaa08fa",
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+ "3fb5246787ab464ea04d3f87c8342c96",
+ "7e97b538d0184b469e7a90af6ff8025d",
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+ "9200deed5727466591dbca28df95dd54",
+ "5867a5d976b44c1e9b90176691eea6fd"
+ ]
+ },
+ "outputId": "fe677d66-255e-4c86-eb31-c18e7a9b2c22"
+ },
+ "id": "mPFAiosJ3jzD",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Challenge_H.csv: 0%| | 0.00/822k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5609f25faca74edb86dc5969a36ff3e5"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "cc0d5f53851f4b38841742d92e79bb29"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1172\n",
+ "Average 'tok' value: 279.59300341296927\n",
+ "Max 'tok' value: 1008\n",
+ "Output\n",
+ "B 311\n",
+ "C 310\n",
+ "D 285\n",
+ "A 266\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1172/1172 [05:03<00:00, 3.87it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 322\n",
+ "C 315\n",
+ "A 280\n",
+ "D 255\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7918\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY ENGLISH**"
+ ],
+ "metadata": {
+ "id": "cT9I3npw43AP"
+ },
+ "id": "cT9I3npw43AP"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "6vmG3Z92410E",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 393,
+ "referenced_widgets": [
+ "65792758d1f846c48cbcef2faaab52a0",
+ "868beacc84634eaea253a5f56ffe9072",
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+ "e9214369853643819118cfff5f8b246f",
+ "c2a4cc66b33e438b8bfbfbc08b6d9555",
+ "a6cada0666c4443885a7751c36b4cc5c",
+ "4310d16e3c8241949dd512b880903277",
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+ "298388a80b004d5bbf0a0629af1253ce",
+ "2ace051c24984d609f10cc97884221f6",
+ "9b9f5cb0382b4fd49d72a92c8974f5fe"
+ ]
+ },
+ "outputId": "8b3ac163-dde9-4438-c66f-32a8519f2663"
+ },
+ "id": "6vmG3Z92410E",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Easy_E.csv: 0%| | 0.00/627k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "65792758d1f846c48cbcef2faaab52a0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "23dfad18036849aeafc18a612c25b31c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 61.38552188552188\n",
+ "Max 'tok' value: 197\n",
+ "Output\n",
+ "C 633\n",
+ "A 596\n",
+ "B 585\n",
+ "D 561\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [09:02<00:00, 4.38it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 620\n",
+ "A 599\n",
+ "B 594\n",
+ "D 563\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.9764\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**ARC EASY HINDI**"
+ ],
+ "metadata": {
+ "id": "A5dtJYX05T5v"
+ },
+ "id": "A5dtJYX05T5v"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ARC_Easy_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df['Output'] = df['Output'].replace({'1': 'A', '2': 'B', '3': 'C', '4': 'D'})\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_arc_e_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_arc_e_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "aFPK7wPX5TN7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 463,
+ "referenced_widgets": [
+ "518a9099ea8141d6aa6d46b2d0216151",
+ "2d4c8ee8a79a4622b669896d6ccd7213",
+ "42c93e40be9e4633afd3c26476e00e6b",
+ "3a286d6da70d49879373eea86f33a312",
+ "2462f1d6742c4f319f627d99208de3b6",
+ "66ffdb31331341c59f049995a90c3756",
+ "6a5d5174c8304ff8a65f36e5ac81b43d",
+ "3d7f0f5643584f368fea89f8a5313516",
+ "b712de3260ae47ff8193fd02b252fb43",
+ "4bfef9118d514d328d4030816874329e",
+ "615cc002f0ea421daf06def3a177d31b",
+ "8699da19438649ac8915f81b023a72c3",
+ "9d57e782cd7a4301a94435f9484a8714",
+ "eb7ad99960e14e0f9efb49cabab16a1a",
+ "eeaddfa4bf87452a806d952ba9f74afc",
+ "9198f57fbafe4a52b77f320c9677fa54",
+ "daef0f2f6db64c12b5f21b3843dfeff1",
+ "2e577855d2bb4028b5009b5b28e9cabd",
+ "00d2cc31482345fe81feac003315a773",
+ "9fa37cf01c794c4990372046bef4fbab",
+ "72425a6003824bc7bb881fab0f611672",
+ "2d5d09d29edb4e2887e2531b1ae4e982"
+ ]
+ },
+ "outputId": "3e854d10-e2ef-448a-8859-e777d1914f70"
+ },
+ "id": "aFPK7wPX5TN7",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ARC_Easy_H.csv: 0%| | 0.00/1.41M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "518a9099ea8141d6aa6d46b2d0216151"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "8699da19438649ac8915f81b023a72c3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "2376\n",
+ "Average 'tok' value: 237.5854377104377\n",
+ "Max 'tok' value: 1016\n",
+ "Output\n",
+ "C 610\n",
+ "A 570\n",
+ "B 563\n",
+ "D 535\n",
+ "4 26\n",
+ "1 26\n",
+ "3 23\n",
+ "2 22\n",
+ "E 1\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 2376/2376 [10:03<00:00, 3.93it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 642\n",
+ "A 619\n",
+ "C 601\n",
+ "D 514\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8670\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMLU ENGLISH**"
+ ],
+ "metadata": {
+ "id": "pFRbbDbE4Qui"
+ },
+ "id": "pFRbbDbE4Qui"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "FtThThQC8hs2",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "a14564a81bd04ba1877e66400326260a",
+ "27c838b960594211afd14cdccca34436",
+ "86ad32f8cd47461f82b0c7b9a108902a",
+ "76f9505d5f7f4b85a407ad00c86da606",
+ "856eff05fc004ae1b75c380a3f3742eb",
+ "a3cf2d5bb8a54962a8f49adb7c7b7539",
+ "4ce2cd07dd1b4670a036c80263327b9e",
+ "6ac37d9e750e41de915da7a8ba4a51c2",
+ "04b43bc5ec634d03a4bb31081e81b484",
+ "afad935cd8064903b58e09cb034a05de",
+ "eaff07450d834394ac3fc762aa0588d7",
+ "908f88ac9aca4d4eb2c6a107f9b4b681",
+ "04221f9a5ffd4477bae93f25c5625b1e",
+ "4c55548d3f80464a8378ed7a5a16b499",
+ "c97b1b73fb43451a85333f9a03cdf395",
+ "3947563ede404e899d4e00ca01ef32dc",
+ "8cba8c7476fc46aab4b01bd03b62ef3d",
+ "0b36fd3072894f52964613f1fd6bc30e",
+ "d943fd53d7734ee4beaf758fb1680418",
+ "7eb448c019c34021abeb12cc98c15d74",
+ "fb995962bd644f8d964769ad8dfaf82f",
+ "d373130f6bd24203b5b5a4cad21d4b7e"
+ ]
+ },
+ "outputId": "c5546b49-c714-49da-a4bd-b13a3edbe697"
+ },
+ "id": "FtThThQC8hs2",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "MMMLU_E.csv: 0%| | 0.00/7.07M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "a14564a81bd04ba1877e66400326260a"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "908f88ac9aca4d4eb2c6a107f9b4b681"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 107.08923230309072\n",
+ "Max 'tok' value: 994\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [58:34<00:00, 4.00it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 4034\n",
+ "C 3601\n",
+ "D 3351\n",
+ "A 3056\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7594\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**MMMLU HINDI**"
+ ],
+ "metadata": {
+ "id": "FeK3WGqS85al"
+ },
+ "id": "FeK3WGqS85al"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"MMMLU_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mmmlu_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mmmlu_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "wDxU0TXK85G7",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376,
+ "referenced_widgets": [
+ "b63c34510cc443948a7c32c32c1e2abe",
+ "da8f00b197b8422c818da472ab819957",
+ "26b17e5395b9487f9226d55228b55548",
+ "d394fa7560dc4f0faa7846217907cdfa",
+ "18eaabb2f5d9428a96921ff0243acc06",
+ "772dce394e1c485ca76ac18b75e622fd",
+ "d1886a8c13dc48aab2474a26e5492512",
+ "6d870149fb3446689132017b771b493e",
+ "de85703b0c4246559b3288a6daeee04d",
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+ "8fea3cb95c9343c3945dfafa69ba8192",
+ "5f9235f6387540518d738ec5e7c7ab01",
+ "e42a800ef7ac47e48298a6da6092cc67",
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+ "e15e67386db84e658e596a43d832d9e2",
+ "9f561653bccc4578a10f47229878355d",
+ "5b33d31259734798b6e866984d3c5dac",
+ "f6158f4c71424a88ad0abb220508272a",
+ "0bcc1eeb326548d7ab1e90c059b1b54d",
+ "2f58913262c84ecd823e7b6aa196a93e",
+ "21c45ead75734943b93a82aa5ea3a568",
+ "f65bc55b231c4cd78d8bbfbb1ddf72f5"
+ ]
+ },
+ "outputId": "2ad3d178-e22c-4564-8a55-c9756d076817"
+ },
+ "id": "wDxU0TXK85G7",
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "MMMLU_H.csv: 0%| | 0.00/16.6M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b63c34510cc443948a7c32c32c1e2abe"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5f9235f6387540518d738ec5e7c7ab01"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14042\n",
+ "Average 'tok' value: 469.44601908560037\n",
+ "Max 'tok' value: 5212\n",
+ "Output\n",
+ "D 3776\n",
+ "C 3582\n",
+ "B 3462\n",
+ "A 3222\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 14042/14042 [1:04:38<00:00, 3.62it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 4297\n",
+ "A 3710\n",
+ "D 3142\n",
+ "C 2893\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.5184\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ ENG**"
+ ],
+ "metadata": {
+ "id": "4aC98L-5Gi9D"
+ },
+ "id": "4aC98L-5Gi9D"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "m7ayEga9Ghkd",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 307,
+ "referenced_widgets": [
+ "9bf55f1a6f174e75814c4788d11d70e3",
+ "aff6ad32f61945d3b6da8547e3d47a86",
+ "1a56b7a105914481b10e5f3d00d12b60",
+ "70c0d47ea2404832b2784840bd8d5dd3",
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+ "9271540562e5402492fd3388943a5319",
+ "2e99ea2b9671493da83bd5ffc7044570"
+ ]
+ },
+ "outputId": "52dcdef9-4a1f-4690-a2a3-f1467e6500a4"
+ },
+ "id": "m7ayEga9Ghkd",
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "BoolQ_E.csv: 0%| | 0.00/2.09M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9bf55f1a6f174e75814c4788d11d70e3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "46159314d52144478dfee144f18349e0"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 137.27798165137614\n",
+ "Max 'tok' value: 1161\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3270/3270 [10:14<00:00, 5.32it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 1939\n",
+ "False 1331\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8832\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**BOOLQ HINDI**"
+ ],
+ "metadata": {
+ "id": "uAhhi93PHZ40"
+ },
+ "id": "uAhhi93PHZ40"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"BoolQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "df['tok'] = df['Input'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "print(f\"Average 'tok' value: {df['tok'].mean()}\")\n",
+ "print(f\"Max 'tok' value: {df['tok'].max()}\")\n",
+ "df = df.sort_values('tok', ascending=False)\n",
+ "print(df['Output'].value_counts())\n",
+ "df = df[1:]\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one word based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('True', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('False', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " else:\n",
+ " prob_a, prob_b = 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['ANS'] = df[['A', 'B']].idxmax(axis=1)\n",
+ "df['ANS'] = df['ANS'].replace({'A': 'True', 'B': 'False'})\n",
+ "df['ANS'] = df['ANS'].astype(str)\n",
+ "df['Output'] = df['Output'].astype(str)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_boolq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_boolq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "GOHy6uE285AB",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 307,
+ "referenced_widgets": [
+ "9d0cab76a4db460caf9a1b234726fd52",
+ "7f79c11ee7e14343bd012c5240a9e54b",
+ "88ba1c83324e4a3dab020cba4ef08cfc",
+ "76e78ec4d5d0473496a149e5362df667",
+ "03b504fa5b19442a96df03b024911cf7",
+ "b6e3d6dc49864158a8d27f3e024c6fc5",
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+ "81f49dcda0474adcbbfe204f6384564c",
+ "55ff769d9c674a8d921f271a8e4cd439",
+ "a43256101e4f4803a6c746bbe5a701d4",
+ "790ab512c5214a64846e92b0120a288a",
+ "7a72fa8769584372a4bd94d3b5643437",
+ "e454a8424ba04fc2bc351f40f0fe7223",
+ "71a9d5211c89473595eaaaa009bafd7f",
+ "958077781f8542d2b816caf38040c8b7",
+ "001305723d234e1e969e89a1fd705b43",
+ "871f770e29614978954eb674aa5d2a14",
+ "07edc4a8873b4b5b92f84ab86af9010b",
+ "ace41cfced464df0a1c5da6db7d14bc0",
+ "711fa02fa7aa456b8d37f7a2784d36ef",
+ "cc7ce46c63a64597b2cc1b3972f75f8d",
+ "c26ad440649a4c7eb78adb4f77249283"
+ ]
+ },
+ "outputId": "f61290f0-dd23-4b50-b19f-ed72bd54a760"
+ },
+ "id": "GOHy6uE285AB",
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "BoolQ_H.csv: 0%| | 0.00/5.28M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9d0cab76a4db460caf9a1b234726fd52"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7a72fa8769584372a4bd94d3b5643437"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "3270\n",
+ "Average 'tok' value: 649.4024464831805\n",
+ "Max 'tok' value: 65520\n",
+ "Output\n",
+ "True 2033\n",
+ "False 1237\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 3269/3269 [14:56<00:00, 3.64it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "True 2308\n",
+ "False 961\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8397\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ ENGLISH**"
+ ],
+ "metadata": {
+ "id": "1ugA-oyeReI9"
+ },
+ "id": "1ugA-oyeReI9"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_E.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores # tuple of [batch_size, vocab_size] for each token\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_eng = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_eng:.4f}\")"
+ ],
+ "metadata": {
+ "id": "K4gKxj8ZRdYS",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 341,
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+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ContextMCQ_E.csv: 0%| | 0.00/1.63M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "699e2a1a9f0143208a308db8c5ff810f"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
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+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "197244d2cd02464da570d3af25a5dd92"
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+ },
+ "metadata": {}
+ },
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+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1000/1000 [04:14<00:00, 3.93it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "C 273\n",
+ "A 260\n",
+ "B 258\n",
+ "D 209\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.8610\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**Context MCQ HINDI**"
+ ],
+ "metadata": {
+ "id": "JVW_cii1SR3c"
+ },
+ "id": "JVW_cii1SR3c"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset = load_dataset(\"1-800-LLMs/Test-Collection\", data_files=\"ContextMCQ_H.csv\", split=\"train\")\n",
+ "df = dataset.to_pandas()\n",
+ "print(len(df))\n",
+ "print(df['Output'].value_counts())\n",
+ "responses = []\n",
+ "prob_a1_list = []\n",
+ "prob_a2_list = []\n",
+ "prob_a3_list = []\n",
+ "prob_b1_list = []\n",
+ "prob_b2_list = []\n",
+ "prob_b3_list = []\n",
+ "prob_c1_list = []\n",
+ "prob_c2_list = []\n",
+ "prob_c3_list = []\n",
+ "prob_d1_list = []\n",
+ "prob_d2_list = []\n",
+ "prob_d3_list = []\n",
+ "batch_size = 1\n",
+ "for start in tqdm(range(0, len(df), batch_size)):\n",
+ " batch_texts = df['Input'][start:start+batch_size].tolist()\n",
+ " for input_text in batch_texts:\n",
+ " prompt = f\"### INPUT : {input_text} Respond with just one letter based on these options : \"\n",
+ " message = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer.apply_chat_template(message, tokenize=True, add_generation_prompt=True, return_tensors=\"pt\").to(\"cuda\")\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(input_ids=inputs, max_new_tokens=3, use_cache=True, pad_token_id=tokenizer.eos_token_id, return_dict_in_generate=True, output_scores=True)\n",
+ " scores = outputs.scores\n",
+ " token_ids_a = tokenizer.encode('A', add_special_tokens=False)[0]\n",
+ " token_ids_b = tokenizer.encode('B', add_special_tokens=False)[0]\n",
+ " token_ids_c = tokenizer.encode('C', add_special_tokens=False)[0]\n",
+ " token_ids_d = tokenizer.encode('D', add_special_tokens=False)[0]\n",
+ " for i in range(3):\n",
+ " if i < len(scores):\n",
+ " probs = F.softmax(scores[i], dim=-1)\n",
+ " prob_a = probs[0, token_ids_a].item()\n",
+ " prob_b = probs[0, token_ids_b].item()\n",
+ " prob_c = probs[0, token_ids_c].item()\n",
+ " prob_d = probs[0, token_ids_d].item()\n",
+ " else:\n",
+ " prob_a, prob_b, prob_c, prob_d = 0.0, 0.0, 0.0, 0.0\n",
+ " if i == 0:\n",
+ " prob_a1_list.append(prob_a)\n",
+ " prob_b1_list.append(prob_b)\n",
+ " prob_c1_list.append(prob_c)\n",
+ " prob_d1_list.append(prob_d)\n",
+ " elif i == 1:\n",
+ " prob_a2_list.append(prob_a)\n",
+ " prob_b2_list.append(prob_b)\n",
+ " prob_c2_list.append(prob_c)\n",
+ " prob_d2_list.append(prob_d)\n",
+ " elif i == 2:\n",
+ " prob_a3_list.append(prob_a)\n",
+ " prob_b3_list.append(prob_b)\n",
+ " prob_c3_list.append(prob_c)\n",
+ " prob_d3_list.append(prob_d)\n",
+ "df['A1'] = prob_a1_list\n",
+ "df['A2'] = prob_a2_list\n",
+ "df['A3'] = prob_a3_list\n",
+ "df['B1'] = prob_b1_list\n",
+ "df['B2'] = prob_b2_list\n",
+ "df['B3'] = prob_b3_list\n",
+ "df['C1'] = prob_c1_list\n",
+ "df['C2'] = prob_c2_list\n",
+ "df['C3'] = prob_c3_list\n",
+ "df['D1'] = prob_d1_list\n",
+ "df['D2'] = prob_d2_list\n",
+ "df['D3'] = prob_d3_list\n",
+ "df['A'] = df['A1'] + df['A2'] + df['A3']\n",
+ "df['B'] = df['B1'] + df['B2'] + df['B3']\n",
+ "df['C'] = df['C1'] + df['C2'] + df['C3']\n",
+ "df['D'] = df['D1'] + df['D2'] + df['D3']\n",
+ "df['ANS'] = df[['A', 'B', 'C', 'D']].idxmax(axis=1)\n",
+ "print(df['ANS'].value_counts())\n",
+ "accuracy_mcq_hin = (df['Output'] == df['ANS']).mean()\n",
+ "print(f\"Accuracy: {accuracy_mcq_hin:.4f}\")"
+ ],
+ "metadata": {
+ "id": "HrB5mDcf842y",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 341,
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+ "id": "HrB5mDcf842y",
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "ContextMCQ_H.csv: 0%| | 0.00/4.14M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7da6b35ee8ff4d48ac2e1f3d14a46eaa"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c72029db08e048dcbcf3a9e1afd50599"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "1000\n",
+ "Output\n",
+ "C 280\n",
+ "B 244\n",
+ "D 241\n",
+ "A 235\n",
+ "Name: count, dtype: int64\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 1000/1000 [06:58<00:00, 2.39it/s]"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "ANS\n",
+ "B 275\n",
+ "A 259\n",
+ "C 246\n",
+ "D 220\n",
+ "Name: count, dtype: int64\n",
+ "Accuracy: 0.7580\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "#**END**"
+ ],
+ "metadata": {
+ "id": "JqYw49CH3gfX"
+ },
+ "id": "JqYw49CH3gfX"
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"BOOLQ ENGLISH : \" ,accuracy_boolq_eng)\n",
+ "print(\"BOOLQ HINDI : \" ,accuracy_boolq_hin)\n",
+ "print(\"C-MCQ ENGLISH : \" ,accuracy_mcq_eng)\n",
+ "print(\"C-MCQ HINDI : \" ,accuracy_mcq_hin)\n",
+ "print(\"MMMLU ENGLISH : \" ,accuracy_mmmlu_eng)\n",
+ "print(\"MMMLU HINDI : \" ,accuracy_mmmlu_hin)\n",
+ "print(\"ARC-E ENGLISH : \" ,accuracy_arc_e_eng)\n",
+ "print(\"ARC-E HINDI : \" ,accuracy_arc_e_hin)\n",
+ "print(\"ARC-C ENGLISH : \" ,accuracy_arc_c_eng)\n",
+ "print(\"ARC-C HINDI : \" ,accuracy_arc_c_hin)\n",
+ "avg_hin_acc = (accuracy_boolq_hin + accuracy_mcq_hin + accuracy_mmmlu_hin + accuracy_arc_e_hin + accuracy_arc_c_hin)/5\n",
+ "avg_eng_acc = (accuracy_boolq_eng + accuracy_mcq_eng + accuracy_mmmlu_eng + accuracy_arc_e_eng + accuracy_arc_c_eng)/5\n",
+ "print(\"AVG SCORE : HINDI : \" ,avg_hin_acc)\n",
+ "print(\"AVG SCORE : ENGLISH : \" ,avg_eng_acc)\n",
+ "avg_tot_acc = (avg_hin_acc + avg_eng_acc)/2\n",
+ "print(\"TOT AVG SCORE : \" ,avg_tot_acc)\n",
+ "print(\"CLICK CTRl+S and wait for 2 sec\")\n",
+ "name = name.split('/')[-1]\n",
+ "name = name + \".ipynb\"\n",
+ "print(\"1) NOTEBOOK NAME SHOULD BE : \", name)\n",
+ "print(\"2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \")\n",
+ "print(\"3) UPDATE THE GOOGLE SHEET WITH THE SCORES \")"
+ ],
+ "metadata": {
+ "id": "YI3SR_t1Vk2s",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "2a0569f0-053e-4175-ac41-67dffe486434"
+ },
+ "id": "YI3SR_t1Vk2s",
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "BOOLQ ENGLISH : 0.8831804281345565\n",
+ "BOOLQ HINDI : 0.8397063322116856\n",
+ "C-MCQ ENGLISH : 0.861\n",
+ "C-MCQ HINDI : 0.758\n",
+ "MMMLU ENGLISH : 0.7594359777809429\n",
+ "MMMLU HINDI : 0.518444666001994\n",
+ "ARC-E ENGLISH : 0.9764309764309764\n",
+ "ARC-E HINDI : 0.867003367003367\n",
+ "ARC-C ENGLISH : 0.924061433447099\n",
+ "ARC-C HINDI : 0.7918088737201365\n",
+ "AVG SCORE : HINDI : 0.7549926477874367\n",
+ "AVG SCORE : ENGLISH : 0.880821763158715\n",
+ "TOT AVG SCORE : 0.8179072054730758\n",
+ "CLICK CTRl+S and wait for 2 sec\n",
+ "1) NOTEBOOK NAME SHOULD BE : hindi-microsoftphi4-B70.ipynb\n",
+ "2) ADD THE CODE TO GITHUB @ https://github.com/1-800-SHARED-TASKS/New-Language-Adaptation/tree/main/Our-Evals/ALL-EVALS/ \n",
+ "3) UPDATE THE GOOGLE SHEET WITH THE SCORES \n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "ljShzEUtnaDH"
+ },
+ "id": "ljShzEUtnaDH",
+ "execution_count": 17,
+ "outputs": []
+ }
+ ],
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+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
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