Instructions to use dafqi/GPT2-Rating-Prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dafqi/GPT2-Rating-Prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dafqi/GPT2-Rating-Prediction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dafqi/GPT2-Rating-Prediction") model = AutoModelForSequenceClassification.from_pretrained("dafqi/GPT2-Rating-Prediction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from dafqi/GPT2-Rating-Prediction: direct link, hf CLI and curl.
- Browser
- Download file 538 Bytes
-
https://huggingface.co/dafqi/GPT2-Rating-Prediction/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://dafqi/GPT2-Rating-Prediction/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/dafqi/GPT2-Rating-Prediction/resolve/main/tokenizer_config.json
538 Bytes
| { | |
| "add_bos_token": false, | |
| "add_prefix_space": false, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "<|endoftext|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "max_len": 512, | |
| "model_max_length": 512, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>" | |
| } | |