Text Classification
setfit
Safetensors
sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use vgarg/promo_prescriptive_gpt_28_02_2024_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use vgarg/promo_prescriptive_gpt_28_02_2024_v1 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("vgarg/promo_prescriptive_gpt_28_02_2024_v1") - sentence-transformers
How to use vgarg/promo_prescriptive_gpt_28_02_2024_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vgarg/promo_prescriptive_gpt_28_02_2024_v1") 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] - Notebooks
- Google Colab
- Kaggle
File size: 716 Bytes
c03eb8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"_name_or_path": "intfloat/multilingual-e5-large",
"architectures": [
"XLMRobertaModel"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "xlm-roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"output_past": true,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.37.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 250002
}
|