Instructions to use bisoye/token_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bisoye/token_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bisoye/token_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bisoye/token_classification") model = AutoModelForTokenClassification.from_pretrained("bisoye/token_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bisoye/token_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/bisoye/token_classification/resolve/main/README.md
- Command line
-
hf download hf://bisoye/token_classification/README.md
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curl -L -o README.md https://huggingface.co/bisoye/token_classification/resolve/main/README.md
1.44 kB
metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: token_classification
results: []
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: 0.2146
- Wer: 0.0549
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 |
|---|---|---|---|---|
| 0.2195 | 1.0 | 76 | 0.2447 | 0.0540 |
| 0.0587 | 2.0 | 152 | 0.2146 | 0.0549 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1