Feature Extraction
sentence-transformers
Safetensors
Transformers
Russian
English
t5
mteb
Eval Results (legacy)
Instructions to use ViktorZver/FRIDA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ViktorZver/FRIDA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ViktorZver/FRIDA") 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 ViktorZver/FRIDA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ViktorZver/FRIDA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ViktorZver/FRIDA") model = AutoModel.from_pretrained("ViktorZver/FRIDA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from ViktorZver/FRIDA: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/ViktorZver/FRIDA/resolve/a06ba8f0608526614958118c19c47fb3e7b59606/sentence_bert_config.json
- Command line
-
hf download hf://ViktorZver/FRIDA@a06ba8f0608526614958118c19c47fb3e7b59606/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/ViktorZver/FRIDA/resolve/a06ba8f0608526614958118c19c47fb3e7b59606/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |