Text Classification
Transformers
Safetensors
English
deberta-v2
reward-model
scpo
cultural-alignment
mexico
text-embeddings-inference
Instructions to use MulticulturalAlignment/scpo-reward-model-mexico with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MulticulturalAlignment/scpo-reward-model-mexico with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MulticulturalAlignment/scpo-reward-model-mexico")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MulticulturalAlignment/scpo-reward-model-mexico") model = AutoModelForSequenceClassification.from_pretrained("MulticulturalAlignment/scpo-reward-model-mexico", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checksums.json from MulticulturalAlignment/scpo-reward-model-mexico: direct link, hf CLI and curl.
- Browser
- Download file 1.6 kB
-
https://huggingface.co/MulticulturalAlignment/scpo-reward-model-mexico/resolve/main/checksums.json
- Command line
-
hf download hf://MulticulturalAlignment/scpo-reward-model-mexico/checksums.json
-
curl -L -o checksums.json https://huggingface.co/MulticulturalAlignment/scpo-reward-model-mexico/resolve/main/checksums.json
1.6 kB
| { | |
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| "README.md": "16e216bcfa769e4e3e2ad1d09b33479c563ca2b8f34fbd424bee9ab2910298bf", | |
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| "config.json": "d88248219ef56b7227b25161fe83bfc1f949d77032367525b372270ef80bef17", | |
| "model.safetensors": "cf720f4fc08e4ee3dca4a191f9171fb9e787ffd79b7c09faf80192439a4af473", | |
| "provenance.json": "d20a5dc019dcbe17685d1fe117129836a2c50267d5f658e3039158e7f8f15bb1", | |
| "release-settings.json": "0bddf225a36cb05cc0f6dd9a7092c711230cafb7d480af132039addc4c49f682", | |
| "requirements-pw-lock.txt": "abe2e55884496b23fc4206efbf563edc7cfc615ef37189851f6f442454e3e8bc", | |
| "special_tokens_map.json": "63cd5b53789b3b5ae2c3d7aa377bcba1557ff0b41dc69ce07b94350ea32291c2", | |
| "spm.model": "c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd", | |
| "tokenizer.json": "b8d055f68ab07e04ecdeacf0bf506d578d826d2682b4709c94899a2f3556f583", | |
| "tokenizer_config.json": "2c9737294d56f67d6122507ce309dfb8dd98221a73ed8315db8f9b07cf1988fa", | |
| "validation.json": "414020c6ec3edef02ee3d0ed107d1859996861b098f726f124267db06f99bd33", | |
| "reproduction/our_data_collator.py": "67c1b081e7886ed8a5747abe7612ff255f0043d8c3d59ef64bb774db12518c79", | |
| "reproduction/our_inverse_reward_trainer.py": "b41fc006ae62dfdd5553217cdb231c35d45c80dd5437f3f7c70ce006f59835a1", | |
| "reproduction/our_reward_trainer.py": "35aea8a2f4b30f51e5427b61418698d69528b868fd7b35abad7132508d092ab3", | |
| "reproduction/preferences.py": "b4d955818aeb36765a0767c9303f771884cb599e2f7783ad7255a1be7265bf43" | |
| } | |