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# scibert-nslp-forc-subtask1-combine

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: 0.2556
- Accuracy: 0.6970
- Precision: 0.6982
- Recall: 0.6970
- F1-weighted: 0.6954

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Precision | Recall | F1-weighted |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:-----------:|
| 0.2117        | 0.48  | 2500  | 0.2313          | 0.6252   | 0.6338    | 0.6252 | 0.6029      |
| 0.1644        | 0.96  | 5000  | 0.1981          | 0.6627   | 0.6637    | 0.6627 | 0.6527      |
| 0.0979        | 1.44  | 7500  | 0.2032          | 0.6709   | 0.6777    | 0.6709 | 0.6662      |
| 0.0819        | 1.93  | 10000 | 0.1995          | 0.6749   | 0.6869    | 0.6749 | 0.6707      |
| 0.0386        | 2.41  | 12500 | 0.2125          | 0.6836   | 0.6905    | 0.6836 | 0.6816      |
| 0.0291        | 2.89  | 15000 | 0.2210          | 0.6815   | 0.6883    | 0.6815 | 0.6785      |
| 0.0099        | 3.37  | 17500 | 0.2412          | 0.6861   | 0.6927    | 0.6861 | 0.6841      |
| 0.0075        | 3.85  | 20000 | 0.2472          | 0.6878   | 0.6910    | 0.6878 | 0.6851      |
| 0.0021        | 4.33  | 22500 | 0.2515          | 0.6960   | 0.6981    | 0.6960 | 0.6944      |
| 0.0012        | 4.81  | 25000 | 0.2556          | 0.6970   | 0.6982    | 0.6970 | 0.6954      |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1

{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "scibert-nslp-forc-subtask1-combine", "results": []}]}
transformers safetensors bert text-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us