|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: camembert-ner-finetuned-ner |
|
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. --> |
|
|
|
# camembert-ner-finetuned-ner |
|
|
|
This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0862 |
|
- Precision: 0.9925 |
|
- Recall: 0.9959 |
|
- F1: 0.9942 |
|
- Accuracy: 0.9896 |
|
|
|
## 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1364 | 1.0 | 769 | 0.0832 | 0.9828 | 0.9998 | 0.9912 | 0.9823 | |
|
| 0.0533 | 2.0 | 1538 | 0.0631 | 0.9934 | 0.9923 | 0.9928 | 0.9871 | |
|
| 0.0329 | 3.0 | 2307 | 0.0651 | 0.9912 | 0.9978 | 0.9945 | 0.9897 | |
|
| 0.021 | 4.0 | 3076 | 0.0680 | 0.9937 | 0.9952 | 0.9945 | 0.9899 | |
|
| 0.0171 | 5.0 | 3845 | 0.0628 | 0.9928 | 0.9969 | 0.9948 | 0.9906 | |
|
| 0.0115 | 6.0 | 4614 | 0.0678 | 0.9930 | 0.9963 | 0.9947 | 0.9903 | |
|
| 0.0075 | 7.0 | 5383 | 0.0854 | 0.9928 | 0.9956 | 0.9942 | 0.9896 | |
|
| 0.0045 | 8.0 | 6152 | 0.0862 | 0.9919 | 0.9948 | 0.9934 | 0.9890 | |
|
| 0.0031 | 9.0 | 6921 | 0.0839 | 0.9919 | 0.9958 | 0.9938 | 0.9896 | |
|
| 0.0028 | 10.0 | 7690 | 0.0862 | 0.9925 | 0.9959 | 0.9942 | 0.9896 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.22.2 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.5.1 |
|
- Tokenizers 0.12.1 |
|
|
