Sentence Similarity
sentence-transformers
PyTorch
Transformers
English
t5
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
prompt-retrieval
text-reranking
feature-extraction
English
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
Eval Results
{ | |
"_name_or_path": "/home2/huggingface/outputs/xl_30000_fever/checkpoint-300/", | |
"architectures": [ | |
"T5EncoderModel" | |
], | |
"d_ff": 16384, | |
"d_kv": 128, | |
"d_model": 1024, | |
"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": 24, | |
"num_heads": 32, | |
"num_layers": 24, | |
"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": "4.20.0.dev0", | |
"use_cache": true, | |
"vocab_size": 32128 | |
} | |