# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("bisoye/distilbert-base-cased_token_classification")
model = AutoModelForTokenClassification.from_pretrained("bisoye/distilbert-base-cased_token_classification")Quick Links
distilbert-base-cased_token_classification
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2266
- Wer: 0.0531
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
- 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 | Wer |
|---|---|---|---|---|
| 0.2092 | 1.0 | 76 | 0.2453 | 0.0549 |
| 0.0598 | 2.0 | 152 | 0.2266 | 0.0531 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for bisoye/distilbert-base-cased_token_classification
Base model
distilbert/distilbert-base-cased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bisoye/distilbert-base-cased_token_classification")