Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use ITOCJ/CCRO2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ITOCJ/CCRO2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ITOCJ/CCRO2") - sentence-transformers
How to use ITOCJ/CCRO2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ITOCJ/CCRO2", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 1,183 Bytes
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"_name_or_path": "/root/.cache/torch/sentence_transformers/jinaai_jina-embeddings-v2-base-en/",
"architectures": [
"JinaBertModel"
],
"attention_probs_dropout_prob": 0.0,
"attn_implementation": "torch",
"auto_map": {
"AutoConfig": "configuration_bert.JinaBertConfig",
"AutoModel": "modeling_bert.JinaBertModel",
"AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
"AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
},
"classifier_dropout": null,
"emb_pooler": "mean",
"feed_forward_type": "geglu",
"gradient_checkpointing": false,
"hidden_act": "gelu",
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"layer_norm_eps": 1e-12,
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"model_max_length": 8192,
"model_type": "bert",
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"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "alibi",
"torch_dtype": "float32",
"transformers_version": "4.35.2",
"type_vocab_size": 2,
"use_cache": true,
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}
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