Feature Extraction
sentence-transformers
ONNX
Safetensors
Laya
Transformers.js
bert
verdict
decision-model
system-one
jev
classification
calibration
Eval Results (legacy)
text-embeddings-inference
Instructions to use Manav2op/verdict-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Manav2op/verdict-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Manav2op/verdict-small") sentences = [ "query: Hi, we were billed twice for March. Please refund the duplicate today.", "passage: What does the user want? invoices, payments, refunds", "passage: What does the user want? bugs, outages, errors", "passage: What does the user want? pricing, new contracts" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Laya
How to use Manav2op/verdict-small with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Transformers.js
How to use Manav2op/verdict-small with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('feature-extraction', 'Manav2op/verdict-small'); - Notebooks
- Google Colab
- Kaggle
File size: 413 Bytes
46dae63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.base.modules.normalize.Normalize"
}
] |