metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- tweetner7
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: tweetner7
type: tweetner7
config: tweetner7
split: validation_2021
args: tweetner7
metrics:
- name: Precision
type: precision
value: 0.7025612778848802
- name: Recall
type: recall
value: 0.6474619289340101
- name: F1
type: f1
value: 0.6738872011623299
- name: Accuracy
type: accuracy
value: 0.8775995608952857
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the tweetner7 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4089
- Precision: 0.7026
- Recall: 0.6475
- F1: 0.6739
- Accuracy: 0.8776
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: 8
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 312 | 0.4428 | 0.7259 | 0.5860 | 0.6485 | 0.8705 |
| 0.5414 | 2.0 | 624 | 0.4090 | 0.7146 | 0.6297 | 0.6695 | 0.8775 |
| 0.5414 | 3.0 | 936 | 0.4089 | 0.7026 | 0.6475 | 0.6739 | 0.8776 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3