Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:86450
loss:DenoisingAutoEncoderLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SwarajSolanke-turtle/make-model-mapping-TSDAE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SwarajSolanke-turtle/make-model-mapping-TSDAE with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SwarajSolanke-turtle/make-model-mapping-TSDAE") sentences = [ "mini", "mini contryman e electric 1", "tata safari xm diesel 1956", "daewoo cielo glx petrol 1498" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from SwarajSolanke-turtle/make-model-mapping-TSDAE: direct link, hf CLI and curl.
- Browser
- Download file 350 Bytes
-
https://huggingface.co/SwarajSolanke-turtle/make-model-mapping-TSDAE/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://SwarajSolanke-turtle/make-model-mapping-TSDAE/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/SwarajSolanke-turtle/make-model-mapping-TSDAE/resolve/main/tokenizer_config.json
350 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 32, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |