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 checksums.json from MulticulturalAlignment/scpo-reward-model-chile: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/MulticulturalAlignment/scpo-reward-model-chile/resolve/main/checksums.json
- Command line
-
hf download hf://MulticulturalAlignment/scpo-reward-model-chile/checksums.json
-
curl -L -o checksums.json https://huggingface.co/MulticulturalAlignment/scpo-reward-model-chile/resolve/main/checksums.json
1.6 kB
| { | |
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| "README.md": "50443b19a62c63184571f85534cae0034998e0edafb30b26b3887ec98f871e7f", | |
| "added_tokens.json": "dc046d04c9b0ada7ae6f1dc89c465801799acdf0c9a6aab8c15a1b2d5ca4e91f", | |
| "config.json": "d88248219ef56b7227b25161fe83bfc1f949d77032367525b372270ef80bef17", | |
| "model.safetensors": "e5ae86eb89ac6cbfcd43c9f2fb7f449d1610c857fbe3f761753e4a2be0da2a9b", | |
| "provenance.json": "9abfe485e7392aadaea22b2e07dee43c42d9f61219b97688d537ecd68e6ce583", | |
| "release-settings.json": "384ff94dab4ac6d35991e5002b3105d31170511be7e281b34e55001c2d08b723", | |
| "requirements-pw-lock.txt": "abe2e55884496b23fc4206efbf563edc7cfc615ef37189851f6f442454e3e8bc", | |
| "special_tokens_map.json": "63cd5b53789b3b5ae2c3d7aa377bcba1557ff0b41dc69ce07b94350ea32291c2", | |
| "spm.model": "c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd", | |
| "tokenizer.json": "b8d055f68ab07e04ecdeacf0bf506d578d826d2682b4709c94899a2f3556f583", | |
| "tokenizer_config.json": "2c9737294d56f67d6122507ce309dfb8dd98221a73ed8315db8f9b07cf1988fa", | |
| "validation.json": "ab9884a4a737bef5a1eb94f32dcbdfee4167c3c653fe16ae2dfd05e032d5bae4", | |
| "reproduction/our_data_collator.py": "67c1b081e7886ed8a5747abe7612ff255f0043d8c3d59ef64bb774db12518c79", | |
| "reproduction/our_inverse_reward_trainer.py": "b41fc006ae62dfdd5553217cdb231c35d45c80dd5437f3f7c70ce006f59835a1", | |
| "reproduction/our_reward_trainer.py": "35aea8a2f4b30f51e5427b61418698d69528b868fd7b35abad7132508d092ab3", | |
| "reproduction/preferences.py": "574f92e9fd8ee404389e4a06254108e50620a5bf4903ca94ee322a8568cee132" | |
| } | |