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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("convaiinnovations/laya-multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rl_agent_config.json from convaiinnovations/laya-multilingual: direct link, hf CLI and curl.
- Browser
- Download file 472 Bytes
-
https://huggingface.co/convaiinnovations/laya-multilingual/resolve/main/rl_agent_config.json
- Command line
-
hf download hf://convaiinnovations/laya-multilingual/rl_agent_config.json
-
curl -L -o rl_agent_config.json https://huggingface.co/convaiinnovations/laya-multilingual/resolve/main/rl_agent_config.json
472 Bytes
| { | |
| "encoder": "jhu-clsp/mmBERT-base", | |
| "head_layers": 2, | |
| "max_len": 1024, | |
| "head_max_len": 256, | |
| "max_prefixes": 6, | |
| "act_costs": { | |
| "escalate": 0.5 | |
| }, | |
| "cost_wrong_act": 3.0, | |
| "amp_dtype": "bf16", | |
| "model_name": "rl-agent", | |
| "temperature": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "temperature_by_options": {}, | |
| "training": { | |
| "updates": 15987, | |
| "epochs_completed": 4, | |
| "hours": 4.97, | |
| "world_size": 1, | |
| "fine_tuned_from_checkpoint": false | |
| } | |
| } |