metadata
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: the_first_model_develop_wnut
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: 0.5735512630014858
- name: Recall
type: recall
value: 0.3577386468952734
- name: F1
type: f1
value: 0.44063926940639264
- name: Accuracy
type: accuracy
value: 0.9459621221837459
the_first_model_develop_wnut
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3410
- Precision: 0.5736
- Recall: 0.3577
- F1: 0.4406
- Accuracy: 0.9460
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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 107 | 0.2753 | 0.6224 | 0.2734 | 0.3799 | 0.9402 |
| No log | 2.0 | 214 | 0.2722 | 0.5637 | 0.3197 | 0.4080 | 0.9447 |
| No log | 3.0 | 321 | 0.3145 | 0.5789 | 0.3401 | 0.4285 | 0.9458 |
| No log | 4.0 | 428 | 0.3199 | 0.5218 | 0.3550 | 0.4225 | 0.9449 |
| 0.0914 | 5.0 | 535 | 0.3410 | 0.5736 | 0.3577 | 0.4406 | 0.9460 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.0