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# SciBERT_20K_steps

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0176
- Accuracy: 0.9941
- Precision: 0.7976
- Recall: 0.5324
- F1: 0.6386
- Hamming: 0.0059

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.31          | 0.02  | 1000  | 0.0780          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| 0.0581        | 0.03  | 2000  | 0.0503          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| 0.0488        | 0.05  | 3000  | 0.0459          | 0.9902   | 0.0       | 0.0    | 0.0    | 0.0098  |
| 0.0407        | 0.06  | 4000  | 0.0355          | 0.9911   | 0.8475    | 0.1130 | 0.1994 | 0.0089  |
| 0.0323        | 0.08  | 5000  | 0.0293          | 0.9918   | 0.8327    | 0.1961 | 0.3175 | 0.0082  |
| 0.0278        | 0.09  | 6000  | 0.0255          | 0.9924   | 0.8223    | 0.2846 | 0.4228 | 0.0076  |
| 0.0246        | 0.11  | 7000  | 0.0235          | 0.9929   | 0.8002    | 0.3609 | 0.4974 | 0.0071  |
| 0.0231        | 0.13  | 8000  | 0.0218          | 0.9933   | 0.7988    | 0.4189 | 0.5496 | 0.0067  |
| 0.0217        | 0.14  | 9000  | 0.0209          | 0.9934   | 0.7888    | 0.4444 | 0.5685 | 0.0066  |
| 0.0208        | 0.16  | 10000 | 0.0201          | 0.9935   | 0.8036    | 0.4473 | 0.5747 | 0.0065  |
| 0.0199        | 0.17  | 11000 | 0.0195          | 0.9936   | 0.7901    | 0.4751 | 0.5934 | 0.0064  |
| 0.0195        | 0.19  | 12000 | 0.0192          | 0.9938   | 0.7889    | 0.4923 | 0.6063 | 0.0062  |
| 0.019         | 0.21  | 13000 | 0.0188          | 0.9938   | 0.7999    | 0.4913 | 0.6087 | 0.0062  |
| 0.0189        | 0.22  | 14000 | 0.0184          | 0.9939   | 0.7872    | 0.5094 | 0.6186 | 0.0061  |
| 0.0188        | 0.24  | 15000 | 0.0182          | 0.9939   | 0.7920    | 0.5084 | 0.6193 | 0.0061  |
| 0.0183        | 0.25  | 16000 | 0.0180          | 0.9940   | 0.7901    | 0.5241 | 0.6301 | 0.0060  |
| 0.0181        | 0.27  | 17000 | 0.0179          | 0.9940   | 0.7897    | 0.5277 | 0.6327 | 0.0060  |
| 0.0179        | 0.28  | 18000 | 0.0177          | 0.9941   | 0.7928    | 0.5301 | 0.6353 | 0.0059  |
| 0.0179        | 0.3   | 19000 | 0.0176          | 0.9941   | 0.7953    | 0.5275 | 0.6343 | 0.0059  |
| 0.0178        | 0.32  | 20000 | 0.0176          | 0.9941   | 0.7976    | 0.5324 | 0.6386 | 0.0059  |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "SciBERT_20K_steps", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us