Instructions to use facebook/data2vec-text-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-text-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/data2vec-text-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-text-base") model = AutoModel.from_pretrained("facebook/data2vec-text-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 714 Bytes
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"architectures": [
"Data2VecTextModel"
],
"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": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "data2vec-text",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"mask_token_id": 50264,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.17.0.dev0",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265,
"tokenizer_class": "RobertaTokenizer"
}
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