|
--- |
|
license: agpl-3.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- mim_gold_ner |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: XLMR-ENIS-finetuned-conll_ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: mim_gold_ner |
|
type: mim_gold_ner |
|
args: mim-gold-ner |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.8754622097322882 |
|
- name: Recall |
|
type: recall |
|
value: 0.8425622775800712 |
|
- name: F1 |
|
type: f1 |
|
value: 0.8586972290729725 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9860744627305035 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# XLMR-ENIS-finetuned-conll_ner |
|
|
|
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0713 |
|
- Precision: 0.8755 |
|
- Recall: 0.8426 |
|
- F1: 0.8587 |
|
- Accuracy: 0.9861 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0493 | 1.0 | 2904 | 0.0673 | 0.8588 | 0.8114 | 0.8344 | 0.9841 | |
|
| 0.0277 | 2.0 | 5808 | 0.0620 | 0.8735 | 0.8275 | 0.8499 | 0.9855 | |
|
| 0.0159 | 3.0 | 8712 | 0.0713 | 0.8755 | 0.8426 | 0.8587 | 0.9861 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.11.3 |
|
- Pytorch 1.9.0+cu111 |
|
- Datasets 1.12.1 |
|
- Tokenizers 0.10.3 |
|
|
