Instructions to use Muennighoff/SGPT-125M-mean-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Muennighoff/SGPT-125M-mean-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Muennighoff/SGPT-125M-mean-nli") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Muennighoff/SGPT-125M-mean-nli with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Muennighoff/SGPT-125M-mean-nli") model = AutoModel.from_pretrained("Muennighoff/SGPT-125M-mean-nli") - Notebooks
- Google Colab
- Kaggle
File size: 1,071 Bytes
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"_name_or_path": "EleutherAI/gpt-neo-125M",
"activation_function": "gelu_new",
"architectures": [
"GPTNeoModel"
],
"attention_dropout": 0,
"attention_layers": [
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local",
"global",
"local"
],
"attention_types": [
[
[
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],
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]
],
"bos_token_id": 50256,
"embed_dropout": 0,
"eos_token_id": 50256,
"gradient_checkpointing": false,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": null,
"layer_norm_epsilon": 1e-05,
"max_position_embeddings": 2048,
"model_type": "gpt_neo",
"num_heads": 12,
"num_layers": 12,
"resid_dropout": 0,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"torch_dtype": "float32",
"transformers_version": "4.12.3",
"use_cache": true,
"vocab_size": 50257,
"window_size": 256
}
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