metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- trl
- sft
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-wnut_17-full
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.6373056994818653
- name: Recall
type: recall
value: 0.34198331788693237
- name: F1
type: f1
value: 0.44511459589867314
- name: Accuracy
type: accuracy
value: 0.9476294301226967
bert-base-uncased-wnut_17-full
This model is a fine-tuned version of google-bert/bert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4356
- Precision: 0.6373
- Recall: 0.3420
- F1: 0.4451
- Accuracy: 0.9476
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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 | 1.0 | 213 | 0.2799 | 0.6045 | 0.3216 | 0.4198 | 0.9457 |
| No log | 2.0 | 426 | 0.3236 | 0.5728 | 0.3392 | 0.4261 | 0.9463 |
| 0.0468 | 3.0 | 639 | 0.3751 | 0.5924 | 0.3448 | 0.4359 | 0.9472 |
| 0.0468 | 4.0 | 852 | 0.3713 | 0.5733 | 0.3661 | 0.4468 | 0.9470 |
| 0.0105 | 5.0 | 1065 | 0.3827 | 0.5741 | 0.3735 | 0.4526 | 0.9479 |
| 0.0105 | 6.0 | 1278 | 0.4356 | 0.6373 | 0.3420 | 0.4451 | 0.9476 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1