Instructions to use BoghdadyJR/test-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BoghdadyJR/test-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BoghdadyJR/test-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BoghdadyJR/test-ner") model = AutoModelForTokenClassification.from_pretrained("BoghdadyJR/test-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from BoghdadyJR/test-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/BoghdadyJR/test-ner/resolve/main/README.md
- Command line
-
hf download hf://BoghdadyJR/test-ner/README.md
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curl -L -o README.md https://huggingface.co/BoghdadyJR/test-ner/resolve/main/README.md
1.03 kB
metadata
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: test-ner
results: []
test-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.13.3