Instructions to use Everlyn/transformer_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Everlyn/transformer_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Everlyn/transformer_base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Everlyn/transformer_base") model = AutoModel.from_pretrained("Everlyn/transformer_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 755 Bytes
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"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"MBartModel"
],
"attention_dropout": 0.0,
"bos_token_id": 0,
"classifier_dropout": 0.0,
"d_model": 512,
"decoder_attention_heads": 8,
"decoder_ffn_dim": 2048,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"dropout": 0.1,
"encoder_attention_heads": 8,
"encoder_ffn_dim": 2048,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"forced_eos_token_id": 2,
"init_std": 0.02,
"is_encoder_decoder": true,
"max_position_embeddings": 512,
"model_type": "mbart",
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"transformers_version": "4.26.0",
"use_cache": true,
"vocab_size": 50265
}
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