---
library_name: transformers
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
model-index:
- name: RoBERTa-Base-full-finetuned-ner-multi-label
  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. -->

# RoBERTa-Base-full-finetuned-ner-multi-label

This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0737
- F1 Micro: 0.7893
- Precision Micro: 0.8246
- Recall Micro: 0.7569

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Micro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|
| No log        | 1.0   | 105  | 0.0907          | 0.7538   | 0.8856          | 0.6562       |
| No log        | 2.0   | 210  | 0.0763          | 0.7893   | 0.8398          | 0.7444       |
| No log        | 3.0   | 315  | 0.0737          | 0.7893   | 0.8246          | 0.7569       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1