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
| { | |
| "__version__": { | |
| "sentence_transformers": "5.0.0", | |
| "transformers": "4.47.0", | |
| "pytorch": "2.5.1+cu12" | |
| }, | |
| "prompts": { | |
| "Banking77Classification": "Instruct: Given a question, please describe the intent of this question. \n Question: " | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SparseEncoder", | |
| "similarity_fn_name": "cosine" | |
| } |