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
| { | |
| "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 | |
| } | |