Feature Extraction
Transformers
Safetensors
English
base_encoder
scientific-retrieval
dense-passage-retrieval
dual-encoder
talk2ref
speech-to-text
sentence-embedding
SBERT
Instructions to use s8frbroy/talk2ref_query_talk_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s8frbroy/talk2ref_query_talk_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="s8frbroy/talk2ref_query_talk_encoder")# Load model directly from transformers import BaseEncoderHF model = BaseEncoderHF.from_pretrained("s8frbroy/talk2ref_query_talk_encoder", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 419 Bytes
9e7a6cb e7cf688 9e7a6cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"aggregator_type": "attn",
"architectures": [
"BaseEncoderHF"
],
"extractor_type": "mean",
"hidden_size": null,
"max_length": 512,
"model_path": "./train/output/train/SBSB/best_model_f1_0.4101_0912_1010_encoder_trans",
"model_type": "base_encoder",
"tokenizer_path": "sentence-transformers/all-MiniLM-L6-v2",
"torch_dtype": "float32",
"transformers_version": "4.44.2",
"truncate": false
}
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