Text Classification
Transformers
Safetensors
Laya
mmbert
system-one
calibrated-decisions
rlcd
classification
routing
guardrails
moderation
commercial-use
Instructions to use convaiinnovations/laya-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use convaiinnovations/laya-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="convaiinnovations/laya-multilingual")# pip install -U transformers accelerate # Load model directly from transformers import LayaTypedDecisions model = LayaTypedDecisions.from_pretrained("convaiinnovations/laya-multilingual", device_map="auto") - Laya
How to use convaiinnovations/laya-multilingual with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
chore: point encoder at the upstream model id instead of a local build path
Browse files- rl_agent_config.json +1 -1
rl_agent_config.json
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"encoder": "
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"head_layers": 2,
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"max_len": 1024,
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"head_max_len": 256,
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"encoder": "jhu-clsp/mmBERT-base",
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"max_len": 1024,
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"head_max_len": 256,
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