Instructions to use Den4ikAI/rubert-tiny-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Den4ikAI/rubert-tiny-squad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Den4ikAI/rubert-tiny-squad")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Den4ikAI/rubert-tiny-squad") model = AutoModelForPreTraining.from_pretrained("Den4ikAI/rubert-tiny-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 895 Bytes
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"_name_or_path": "/gd/MyDrive/models/rubert-tiny-mlm-nli-sentence",
"architectures": [
"BertForPreTraining"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"emb_size": 312,
"finetuning_task": null,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 312,
"initializer_range": 0.02,
"intermediate_size": 600,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 2048,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 3,
"num_labels": 2,
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"pruned_heads": {},
"torch_dtype": "float32",
"torchscript": false,
"transformers_version": "4.12.3",
"type_vocab_size": 2,
"use_bfloat16": false,
"use_cache": true,
"vocab_size": 83828
}
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