metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8089
- Precision: 0.3730
- Recall: 0.5764
- F1: 0.4529
- Accuracy: 0.7512
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 69 | 0.8052 | 0.3835 | 0.3229 | 0.3506 | 0.7630 |
No log | 2.0 | 138 | 0.7310 | 0.3635 | 0.4809 | 0.4141 | 0.7549 |
No log | 3.0 | 207 | 0.7309 | 0.3881 | 0.5208 | 0.4448 | 0.7621 |
No log | 4.0 | 276 | 0.7683 | 0.3926 | 0.5330 | 0.4521 | 0.7642 |
No log | 5.0 | 345 | 0.8089 | 0.3730 | 0.5764 | 0.4529 | 0.7512 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1