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---
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: 0.0484
- F1 Micro: 0.8025
- Precision Micro: 0.8296
- Recall Micro: 0.7772
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|
| No log | 1.0 | 27 | 0.1227 | 0.6758 | 0.7555 | 0.6114 |
| No log | 2.0 | 54 | 0.0750 | 0.7087 | 0.9323 | 0.5716 |
| No log | 3.0 | 81 | 0.0628 | 0.7597 | 0.8531 | 0.6848 |
| No log | 4.0 | 108 | 0.0554 | 0.7868 | 0.8768 | 0.7136 |
| No log | 5.0 | 135 | 0.0522 | 0.7987 | 0.8228 | 0.7759 |
| No log | 6.0 | 162 | 0.0508 | 0.7967 | 0.8283 | 0.7674 |
| No log | 7.0 | 189 | 0.0493 | 0.8005 | 0.8263 | 0.7763 |
| No log | 8.0 | 216 | 0.0489 | 0.8032 | 0.8253 | 0.7822 |
| No log | 9.0 | 243 | 0.0490 | 0.8014 | 0.8171 | 0.7864 |
| No log | 10.0 | 270 | 0.0484 | 0.8025 | 0.8296 | 0.7772 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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