Text Classification
Transformers
Safetensors
English
deberta-v2
reward-model
scpo
cultural-alignment
chile
text-embeddings-inference
Instructions to use MulticulturalAlignment/scpo-reward-model-chile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MulticulturalAlignment/scpo-reward-model-chile with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MulticulturalAlignment/scpo-reward-model-chile")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MulticulturalAlignment/scpo-reward-model-chile") model = AutoModelForSequenceClassification.from_pretrained("MulticulturalAlignment/scpo-reward-model-chile", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from MulticulturalAlignment/scpo-reward-model-chile: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/MulticulturalAlignment/scpo-reward-model-chile/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://MulticulturalAlignment/scpo-reward-model-chile/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/MulticulturalAlignment/scpo-reward-model-chile/resolve/main/tokenizer_config.json
1.39 kB
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "3": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "128000": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "max_length": 512, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "sp_model_kwargs": {}, | |
| "split_by_punct": false, | |
| "stride": 0, | |
| "tokenizer_class": "DebertaV2Tokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]", | |
| "vocab_type": "spm" | |
| } | |