metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test2
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0
- name: Recall
type: recall
value: 0
- name: F1
type: f1
value: 0
- name: Accuracy
type: accuracy
value: 0.9240631163708086
test2
This model is a fine-tuned version of bert-base-cased on the ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.4294
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9241
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 | 23 | 0.3823 | 0.0 | 0.0 | 0.0 | 0.9262 |
| No log | 2.0 | 46 | 0.4136 | 0.0 | 0.0 | 0.0 | 0.9247 |
| No log | 3.0 | 69 | 0.4294 | 0.0 | 0.0 | 0.0 | 0.9241 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1