Token Classification
Transformers
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("krishnareddy/hello_token_classification_model")
model = AutoModelForTokenClassification.from_pretrained("krishnareddy/hello_token_classification_model")Quick Links
hello_token_classification_model
This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2797
- Precision: 0.6046
- Recall: 0.2919
- F1: 0.3937
- Accuracy: 0.9407
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: 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 213 | 0.2924 | 0.4855 | 0.2011 | 0.2844 | 0.9363 |
| No log | 2.0 | 426 | 0.2797 | 0.6046 | 0.2919 | 0.3937 | 0.9407 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
- Downloads last month
- 2
Model tree for krishnareddy/hello_token_classification_model
Base model
distilbert/distilbert-base-uncasedDataset used to train krishnareddy/hello_token_classification_model
Evaluation results
- Precision on wnut_17test set self-reported0.605
- Recall on wnut_17test set self-reported0.292
- F1 on wnut_17test set self-reported0.394
- Accuracy on wnut_17test set self-reported0.941
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="krishnareddy/hello_token_classification_model")