Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use krish2505/setfitmkrt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use krish2505/setfitmkrt2 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("krish2505/setfitmkrt2") - sentence-transformers
How to use krish2505/setfitmkrt2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("krish2505/setfitmkrt2") 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
Update config.json
Browse files- config.json +2 -13
config.json
CHANGED
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@@ -20,17 +20,6 @@
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.36.2",
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"vocab_size": 30527
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"undefined":"0-press release/advertisement/newspaper publication",
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"0": "0-press release/advertisement/newspaper publication",
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"1": "1-business updates/strategic announcement/clarification sought",
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"2": "2-Investor meetings/board meeting",
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"3": "3-earnings call transcript",
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"4": "4-esop/esps",
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"5": "5-violation/litigation/penalty",
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"6": "6-auditors report/result",
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"7": "7-research",
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"8": "8-resignation"
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}
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}
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.36.2",
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"vocab_size": 30527
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}
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