Instructions to use hfeng/bert_base_uncased_conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfeng/bert_base_uncased_conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hfeng/bert_base_uncased_conll2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hfeng/bert_base_uncased_conll2003") model = AutoModelForTokenClassification.from_pretrained("hfeng/bert_base_uncased_conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# BERT base model (uncased) fine-tuned on CoNLL-2003
|
| 2 |
+
|
| 3 |
+
This model was trained following the PyTorch token-classification example from Hugging Face: https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification.
|
| 4 |
+
|
| 5 |
+
There were no tweaks to the model or dataset.
|
| 6 |
+
|