|
--- |
|
license: mit |
|
base_model: FacebookAI/roberta-large |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- few-nerd |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-finetuned-ner |
|
results: |
|
- task: |
|
name: Token Classification |
|
type: token-classification |
|
dataset: |
|
name: few-nerd |
|
type: few-nerd |
|
config: supervised |
|
split: validation |
|
args: supervised |
|
metrics: |
|
- name: Precision |
|
type: precision |
|
value: 0.7844853130000198 |
|
- name: Recall |
|
type: recall |
|
value: 0.8147760612215589 |
|
- name: F1 |
|
type: f1 |
|
value: 0.799343826738054 |
|
- name: Accuracy |
|
type: accuracy |
|
value: 0.9428779215112315 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# bert-finetuned-ner |
|
|
|
This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the few-nerd dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2164 |
|
- Precision: 0.7845 |
|
- Recall: 0.8148 |
|
- F1: 0.7993 |
|
- Accuracy: 0.9429 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 4 |
|
- 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 | |
|
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.1953 | 1.0 | 32942 | 0.1933 | 0.7670 | 0.7968 | 0.7816 | 0.9395 | |
|
| 0.1573 | 2.0 | 65884 | 0.2051 | 0.7850 | 0.8034 | 0.7941 | 0.9416 | |
|
| 0.1256 | 3.0 | 98826 | 0.2164 | 0.7845 | 0.8148 | 0.7993 | 0.9429 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.1+cu118 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|
