Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Polish
bert
feature-extraction
information-retrieval
text-embeddings-inference
Instructions to use sdadas/mmlw-retrieval-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdadas/mmlw-retrieval-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdadas/mmlw-retrieval-e5-small") sentences = [ "query: Jak dożyć 100 lat?", "passage: Trzeba zdrowo się odżywiać i uprawiać sport.", "passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami.", "passage: Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use sdadas/mmlw-retrieval-e5-small with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sdadas/mmlw-retrieval-e5-small") model = AutoModel.from_pretrained("sdadas/mmlw-retrieval-e5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"score": 69.97967772787767, "cmd": "training/mnr_tevatron.py --model_name_or_path=models/mmlw-e5-small --output_dir=models/mmlw-retrieval-e5-small --train_dir ir_train --q_max_len 64 --p_max_len 512 --do_train --save_strategy steps --save_steps 500 --warmup_steps 1000 --save_total_limit 1 --fp16 --per_device_train_batch_size 96 --train_n_passages 2 --learning_rate 2e-6 --max_steps 50000 --logging_steps 100 --disable_tqdm True --weight_decay 0.01 --report_to none --ir_eval_dir ir_eval '--q_prefix=query: ' '--p_prefix=passage: ' --negatives_x_device --temperature 0.01 --similarity cos_sim --positive_passage_no_shuffle --negative_passage_no_shuffle --projection_in_dim 384 --projection_out_dim 384"} |