Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
text-embeddings-inference
Instructions to use DanL/scientific-challenges-and-directions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DanL/scientific-challenges-and-directions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DanL/scientific-challenges-and-directions")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DanL/scientific-challenges-and-directions") model = AutoModelForSequenceClassification.from_pretrained("DanL/scientific-challenges-and-directions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from DanL/scientific-challenges-and-directions: direct link, hf CLI and curl.
- Browser
- Download file 388 Bytes
-
https://huggingface.co/DanL/scientific-challenges-and-directions/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://DanL/scientific-challenges-and-directions/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/DanL/scientific-challenges-and-directions/resolve/main/tokenizer_config.json
388 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |