|
--- |
|
license: apache-2.0 |
|
base_model: google-bert/bert-large-cased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-large-cased-finetuned-ner-harem |
|
results: [] |
|
--- |
|
|
|
<!-- 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-large-cased-finetuned-ner-harem |
|
|
|
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2665 |
|
- Precision: 0.7241 |
|
- Recall: 0.7423 |
|
- F1: 0.7331 |
|
- Accuracy: 0.9611 |
|
|
|
## 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 |
|
- gradient_accumulation_steps: 8 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 0.9938 | 140 | 0.2609 | 0.5107 | 0.5626 | 0.5354 | 0.9324 | |
|
| No log | 1.9947 | 281 | 0.2057 | 0.6370 | 0.6642 | 0.6503 | 0.9517 | |
|
| No log | 2.9956 | 422 | 0.2106 | 0.6527 | 0.6642 | 0.6584 | 0.9566 | |
|
| 0.2074 | 3.9965 | 563 | 0.2342 | 0.6843 | 0.7054 | 0.6947 | 0.9571 | |
|
| 0.2074 | 4.9973 | 704 | 0.2369 | 0.7216 | 0.7290 | 0.7253 | 0.9614 | |
|
| 0.2074 | 5.9982 | 845 | 0.2334 | 0.7013 | 0.7261 | 0.7135 | 0.9574 | |
|
| 0.2074 | 6.9991 | 986 | 0.2580 | 0.7139 | 0.7570 | 0.7348 | 0.9592 | |
|
| 0.0377 | 8.0 | 1127 | 0.2658 | 0.7452 | 0.7452 | 0.7452 | 0.9607 | |
|
| 0.0377 | 8.9938 | 1267 | 0.2619 | 0.7543 | 0.7688 | 0.7615 | 0.9637 | |
|
| 0.0377 | 9.9379 | 1400 | 0.2665 | 0.7241 | 0.7423 | 0.7331 | 0.9611 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.41.1 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
|
|
