Instructions to use DSI/personal_sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DSI/personal_sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DSI/personal_sentiment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DSI/personal_sentiment") model = AutoModelForSequenceClassification.from_pretrained("DSI/personal_sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from DSI/personal_sentiment: direct link, hf CLI and curl.
- Browser
- Download file 401 Bytes
-
https://huggingface.co/DSI/personal_sentiment/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://DSI/personal_sentiment/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/DSI/personal_sentiment/resolve/main/tokenizer_config.json
401 Bytes
| {"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "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": "models/personal_sentiment", "tokenizer_file": "models/TSDAE/tokenizer.json", "tokenizer_class": "BertTokenizer"} |