File size: 1,997 Bytes
38c7cff cbfcf7b 38c7cff cbfcf7b 38c7cff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: mit
base_model: microsoft/deberta-v3-base
datasets: stanfordnlp/imdb
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: deberta-v3-base-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-base-imdb
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [imdb](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3594
- Accuracy: 0.9577
- F1: 0.9579
- Precision: 0.9530
- Recall: 0.9629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3108 | 1.0 | 12500 | 0.2634 | 0.9530 | 0.9529 | 0.9557 | 0.9502 |
| 0.2322 | 2.0 | 25000 | 0.2629 | 0.9546 | 0.9552 | 0.9437 | 0.9670 |
| 0.1119 | 3.0 | 37500 | 0.2944 | 0.9546 | 0.9550 | 0.9467 | 0.9634 |
| 0.0292 | 4.0 | 50000 | 0.3694 | 0.9557 | 0.9564 | 0.9422 | 0.9710 |
| 0.0191 | 5.0 | 62500 | 0.3594 | 0.9577 | 0.9579 | 0.9530 | 0.9629 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|