---
base_model: allenai/scibert_scivocab_cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scibert_all_deep
  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. -->

# scibert_all_deep

This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8270
- Precision: 0.6648
- Recall: 0.7172
- F1: 0.6900
- Accuracy: 0.8207

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 363  | 0.5559          | 0.6191    | 0.6867 | 0.6511 | 0.8131   |
| 0.6741        | 2.0   | 726  | 0.5344          | 0.6271    | 0.7101 | 0.6660 | 0.8203   |
| 0.3917        | 3.0   | 1089 | 0.5548          | 0.6558    | 0.7064 | 0.6801 | 0.8205   |
| 0.3917        | 4.0   | 1452 | 0.5835          | 0.6717    | 0.7110 | 0.6908 | 0.8246   |
| 0.271         | 5.0   | 1815 | 0.6643          | 0.6524    | 0.7255 | 0.6870 | 0.8196   |
| 0.188         | 6.0   | 2178 | 0.7021          | 0.6724    | 0.7067 | 0.6892 | 0.8222   |
| 0.1437        | 7.0   | 2541 | 0.7594          | 0.6555    | 0.7180 | 0.6853 | 0.8191   |
| 0.1437        | 8.0   | 2904 | 0.7916          | 0.6664    | 0.7109 | 0.6879 | 0.8194   |
| 0.114         | 9.0   | 3267 | 0.8123          | 0.6582    | 0.7225 | 0.6888 | 0.8203   |
| 0.0943        | 10.0  | 3630 | 0.8270          | 0.6648    | 0.7172 | 0.6900 | 0.8207   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1