metadata
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
bert-finetuned-ner
This model is a fine-tuned version of 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