Sentence Similarity
sentence-transformers
Safetensors
t5
feature-extraction
Generated from Trainer
dataset_size:3738
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
custom_code
Eval Results (legacy)
Instructions to use Bharatdeep-H/pq_cache_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Bharatdeep-H/pq_cache_7 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Bharatdeep-H/pq_cache_7", trust_remote_code=True) sentences = [ "What's the status of my portfolio?", "Show my funds portfolio", "How risky is my portfolio currently?", "How is my portfolio performing" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "jinaai/jina-embedding-b-en-v1", | |
| "architectures": [ | |
| "T5EncoderModel" | |
| ], | |
| "auto_map": { | |
| "AutoModel": "jinaai/jina-embedding-b-en-v1--modeling_t5.T5EncoderModel" | |
| }, | |
| "classifier_dropout": 0.0, | |
| "d_ff": 3072, | |
| "d_kv": 64, | |
| "d_model": 768, | |
| "decoder_start_token_id": 0, | |
| "dense_act_fn": "relu", | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "feed_forward_proj": "relu", | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "is_gated_act": false, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_decoder_layers": 12, | |
| "num_heads": 12, | |
| "num_layers": 12, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "relative_attention_max_distance": 128, | |
| "relative_attention_num_buckets": 32, | |
| "task_specific_params": { | |
| "summarization": { | |
| "early_stopping": true, | |
| "length_penalty": 2.0, | |
| "max_length": 200, | |
| "min_length": 30, | |
| "no_repeat_ngram_size": 3, | |
| "num_beams": 4, | |
| "prefix": "summarize: " | |
| }, | |
| "translation_en_to_de": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to German: " | |
| }, | |
| "translation_en_to_fr": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to French: " | |
| }, | |
| "translation_en_to_ro": { | |
| "early_stopping": true, | |
| "max_length": 300, | |
| "num_beams": 4, | |
| "prefix": "translate English to Romanian: " | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.49.0", | |
| "use_cache": true, | |
| "vocab_size": 32128 | |
| } | |