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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- tex2log |
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- log2tex |
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- foc |
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widget: |
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- text: "translate to nl: all x1.(_explanation(x1) -> -_equal(x1))" |
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- text: "translate to fol: All chains are bad." |
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model-index: |
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- name: t5-small-text2log |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# T5 (small) fine-tuned on Text2Log |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an Text2Log dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0313 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 0.0749 | 1.0 | 21661 | 0.0509 | |
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| 0.0564 | 2.0 | 43322 | 0.0396 | |
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| 0.0494 | 3.0 | 64983 | 0.0353 | |
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| 0.0425 | 4.0 | 86644 | 0.0332 | |
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| 0.04 | 5.0 | 108305 | 0.0320 | |
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| 0.0381 | 6.0 | 129966 | 0.0313 | |
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### Usage: |
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```py |
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from transformers import AutoTokenizer, T5ForConditionalGeneration |
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MODEL_CKPT = "mrm8488/t5-small-finetuned-text2log" |
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model = T5ForConditionalGeneration.from_pretrained(MODEL_CKPT).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_CKPT) |
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def translate(text): |
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inputs = tokenizer(text, padding="longest", max_length=64, return_tensors="pt") |
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input_ids = inputs.input_ids.to(device) |
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attention_mask = inputs.attention_mask.to(device) |
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output = model.generate(input_ids, attention_mask=attention_mask, early_stopping=False, max_length=64) |
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return tokenizer.decode(output[0], skip_special_tokens=True) |
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prompt_nl_to_fol = "translate to fol: " |
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prompt_fol_to_nl = "translate to nl: " |
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example_1 = "Every killer leaves something." |
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example_2 = "all x1.(_woman(x1) -> exists x2.(_emotion(x2) & _experience(x1,x2)))" |
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print(translate(prompt_nl_to_fol + example_1)) # all x1.(_killer(x1) -> exists x2._leave(x1,x2)) |
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print(translate(prompt_fol_to_nl + example_2)) # Every woman experiences emotions. |
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``` |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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