|
--- |
|
license: apache-2.0 |
|
base_model: bert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- fursov/gec_ner_val3 |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: ner-gec-v2 |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: fursov/gec_ner_val3 |
|
type: fursov/gec_ner_val3 |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.36697832554186144 |
|
- name: Recall |
|
type: recall |
|
value: 0.23284346770931644 |
|
- name: F1 |
|
type: f1 |
|
value: 0.2849129753361379 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.941991634627572 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# ner-gec-v2 |
|
|
|
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the fursov/gec_ner_val3 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2067 |
|
- Precision: 0.3670 |
|
- Recall: 0.2328 |
|
- F1: 0.2849 |
|
- Accuracy: 0.9420 |
|
|
|
## 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: 128 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3.0 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.2324 | 1.15 | 500 | 0.2359 | 0.2070 | 0.0883 | 0.1238 | 0.9353 | |
|
| 0.1901 | 2.3 | 1000 | 0.2137 | 0.3467 | 0.2212 | 0.2701 | 0.9399 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.36.2 |
|
- Pytorch 2.1.0+cu118 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
