Text Classification
sentence-transformers
Safetensors
Transformers
English
nvembed
feature-extraction
mteb
text
text-embeddings-inference
sparse-encoder
sparse
csr
custom_code
Eval Results (legacy)
Instructions to use Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77", 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] - Transformers
How to use Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Y-Research-Group/CSR-NV_Embed_v2-Classification-Banking77", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Sparse Encoder Update
Browse files
{3_CSRSparsity → 3_SparseAutoEncoder}/config.json
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3_CSRSparsity/pytorch_model.bin → 3_SparseAutoEncoder/model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:9a6471d3889c0828dbdbd53c2eabef4027554d0ac705c029496358135f94b303
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size 268648800
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "
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"transformers": "4.47.0",
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"pytorch": "2.5.1+cu12"
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},
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"prompts": {
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"Banking77Classification": "Instruct: Given a question, please describe the intent of this question. \n Question: "
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"MTOPIntentClassification": "Instruct: Given a question, please describe the intent of this question. \n Question: ",
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"TweetSentimentClassification": "Classify the sentiment of a given tweet as either positive, negative, or neutral.",
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"BiorxivClusteringP2P.v2": "Identify the main category of Biorxiv papers based on the titles and abstracts",
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"BiorxivClusteringS2S.v2": "Identify the main category of Biorxiv papers based on the titles",
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"TwentyNewsgroupsClustering": "Identify the topic or theme of the given news articles",
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"FiQA2018": {
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"query": "Given a financial question, retrieve relevant passages that answer the query"
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},
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"SciFact": {
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"query": "Given a scientific claim, retrieve documents that support or refute the claim"
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},
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"NFCorpus": {
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"query": "Given a question, retrieve relevant documents that answer the question"
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}
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},
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"default_prompt_name": null,
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"model_type": "SparseEncoder",
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"similarity_fn_name": "
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}
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{
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"__version__": {
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"sentence_transformers": "5.0.0",
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"transformers": "4.47.0",
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"pytorch": "2.5.1+cu12"
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},
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"prompts": {
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"Banking77Classification": "Instruct: Given a question, please describe the intent of this question. \n Question: "
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},
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"default_prompt_name": null,
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"model_type": "SparseEncoder",
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"similarity_fn_name": "cosine"
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}
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modules.json
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{
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"idx": 3,
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"name": "3",
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"path": "
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"type": "sentence_transformers.sparse_encoder.models.
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}
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]
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{
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"idx": 3,
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"name": "3",
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"path": "3_SparseAutoEncoder",
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"type": "sentence_transformers.sparse_encoder.models.SparseAutoEncoder"
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}
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]
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