README.md

---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: roberta-large
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-large-token-classification
  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. -->

# bert-large-token-classification

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2157
- Precision: 0.4271
- Recall: 0.5155
- F1: 0.4671
- Accuracy: 0.9481

## 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: 0.001
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3642        | 1.0   | 741  | 0.2888          | 0.2211    | 0.2315 | 0.2262 | 0.9264   |
| 0.2508        | 2.0   | 1482 | 0.2862          | 0.3301    | 0.3645 | 0.3465 | 0.9217   |
| 0.1609        | 3.0   | 2223 | 0.2247          | 0.3109    | 0.4309 | 0.3612 | 0.9411   |
| 0.1404        | 4.0   | 2964 | 0.2391          | 0.3563    | 0.4669 | 0.4042 | 0.9303   |
| 0.0937        | 5.0   | 3705 | 0.2157          | 0.4271    | 0.5155 | 0.4671 | 0.9481   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1