Token Classification
Transformers
ONNX
Safetensors
English
ultrabert
information-extraction
extractive-qa
ner
span-extraction
tasksource
custom_code
Instructions to use tasksource/UltraBERT-150M-clairic-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tasksource/UltraBERT-150M-clairic-extraction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tasksource/UltraBERT-150M-clairic-extraction", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tasksource/UltraBERT-150M-clairic-extraction", trust_remote_code=True) model = AutoModelForTokenClassification.from_pretrained("tasksource/UltraBERT-150M-clairic-extraction", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 815 Bytes
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"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|endoftext|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<|endoftext|>",
"errors": "replace",
"extra_special_tokens": [
"<|endoftext|>",
"<|im_start|>",
"<|im_end|>",
"<repo_name>",
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"<filename>",
"<gh_stars>",
"<issue_start>",
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"<|sep|>",
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"is_local": false,
"local_files_only": false,
"mask_token": "<|mask|>",
"model_max_length": 32768,
"pad_token": "<|endoftext|>",
"sep_token": "<|sep|>",
"tokenizer_class": "GPT2Tokenizer",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}
|