---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: uniBERT.SciBERT.2
  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. -->

# uniBERT.SciBERT.2

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5863
- Accuracy: (0.5884718498659517,)
- F1: (0.5835493983611322,)
- Precision: (0.5880118425320139,)
- Recall: 0.5885

## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy              | F1                     | Precision             | Recall |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:----------------------:|:---------------------:|:------:|
| 2.6736        | 1.0   | 187  | 2.1773          | (0.3847184986595174,) | (0.3652816466654799,)  | (0.3985942864108866,) | 0.3847 |
| 1.6286        | 2.0   | 374  | 1.6625          | (0.4906166219839142,) | (0.48229779243148563,) | (0.5287776487828597,) | 0.4906 |
| 1.1733        | 3.0   | 561  | 1.5601          | (0.5281501340482574,) | (0.5221085418789655,)  | (0.5430006354909301,) | 0.5282 |
| 0.8032        | 4.0   | 748  | 1.4738          | (0.5549597855227882,) | (0.5499270608655985,)  | (0.5615902558999348,) | 0.5550 |
| 0.5888        | 5.0   | 935  | 1.4584          | (0.5603217158176944,) | (0.5559524005998449,)  | (0.5684946987230237,) | 0.5603 |
| 0.4449        | 6.0   | 1122 | 1.4952          | (0.5764075067024129,) | (0.5740862941630532,)  | (0.5860221500122856,) | 0.5764 |
| 0.271         | 7.0   | 1309 | 1.5141          | (0.5777479892761395,) | (0.5724486836239684,)  | (0.5756237402682504,) | 0.5777 |
| 0.2036        | 8.0   | 1496 | 1.5745          | (0.5737265415549598,) | (0.5706283325637723,)  | (0.5784921965802793,) | 0.5737 |
| 0.1993        | 9.0   | 1683 | 1.5754          | (0.5831099195710456,) | (0.5792457295024093,)  | (0.5837479506310695,) | 0.5831 |
| 0.1485        | 10.0  | 1870 | 1.5863          | (0.5884718498659517,) | (0.5835493983611322,)  | (0.5880118425320139,) | 0.5885 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2