---
library_name: transformers
license: cc-by-sa-4.0
base_model: nlpaueb/legal-bert-small-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: legal-bert-sentiment-small-10000
  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. -->

# legal-bert-sentiment-small-10000

This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the imdb dataset with 10000 train samples and 1200 test samples..
It achieves the following results on the evaluation set:
- Loss: 0.2959
- Accuracy: 0.885
- F1: 0.8846
- Precision: 0.8831
- Recall: 0.8861

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 313  | 0.3317          | 0.8667   | 0.8728 | 0.8306    | 0.9196 |
| 0.3554        | 2.0   | 626  | 0.2959          | 0.885    | 0.8846 | 0.8831    | 0.8861 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0