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# scibert-nslp-forc-subtask1

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: 0.1567
- Accuracy: 0.7386
- Precision: 0.7374
- Recall: 0.7386
- F1-weighted: 0.7342

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1-weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----------:|
| 0.2498        | 0.58  | 1500 | 0.2276          | 0.6439   | 0.6466    | 0.6439 | 0.6194      |
| 0.1576        | 1.16  | 3000 | 0.1887          | 0.6914   | 0.6907    | 0.6914 | 0.6776      |
| 0.131         | 1.73  | 4500 | 0.1668          | 0.7142   | 0.7175    | 0.7142 | 0.7058      |
| 0.071         | 2.31  | 6000 | 0.1604          | 0.7342   | 0.7305    | 0.7342 | 0.7281      |
| 0.0668        | 2.89  | 7500 | 0.1567          | 0.7386   | 0.7374    | 0.7386 | 0.7342      |


### 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", "results": []}]}
transformers safetensors bert text-classification generated_from_trainer base_model:allenai/scibert_scivocab_uncased autotrain_compatible endpoints_compatible region:us