|
--- |
|
license: mit |
|
base_model: xlm-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: xlm-roberta-base-finetuned-Conll2003-ner-2024_08_05 |
|
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. --> |
|
|
|
# xlm-roberta-base-finetuned-Conll2003-ner-2024_08_05 |
|
|
|
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1404 |
|
- Precision: 0.9004 |
|
- Recall: 0.9163 |
|
- F1: 0.9083 |
|
- Accuracy: 0.9780 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- 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 | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0838 | 0.3326 | 292 | 0.1220 | 0.8779 | 0.8841 | 0.8810 | 0.9730 | |
|
| 0.0807 | 0.6651 | 584 | 0.1345 | 0.8695 | 0.8934 | 0.8813 | 0.9728 | |
|
| 0.0711 | 0.9977 | 876 | 0.1336 | 0.8728 | 0.8986 | 0.8855 | 0.9733 | |
|
| 0.0467 | 1.3303 | 1168 | 0.1443 | 0.8817 | 0.9090 | 0.8951 | 0.9748 | |
|
| 0.0452 | 1.6629 | 1460 | 0.1311 | 0.8887 | 0.9138 | 0.9011 | 0.9759 | |
|
| 0.0383 | 1.9954 | 1752 | 0.1324 | 0.9021 | 0.9146 | 0.9083 | 0.9776 | |
|
| 0.026 | 2.3280 | 2044 | 0.1352 | 0.9024 | 0.9180 | 0.9101 | 0.9784 | |
|
| 0.0245 | 2.6606 | 2336 | 0.1431 | 0.9010 | 0.9172 | 0.9090 | 0.9778 | |
|
| 0.0235 | 2.9932 | 2628 | 0.1403 | 0.9004 | 0.9163 | 0.9083 | 0.9780 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.4 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
