Translation
Transformers
PyTorch
t5
text2text-generation
chemistry
biology
text-generation-inference
Instructions to use AI4PD/REXzyme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PD/REXzyme with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="AI4PD/REXzyme")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("AI4PD/REXzyme") model = AutoModelForSeq2SeqLM.from_pretrained("AI4PD/REXzyme") - Notebooks
- Google Colab
- Kaggle
Upload config.json
Browse files- tokenizer_smiles/config.json +30 -0
tokenizer_smiles/config.json
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{
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"_name_or_path": "google/t5-efficient-large",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 4096,
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"d_kv": 64,
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"d_model": 1024,
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"decoder_start_token_id": 0,
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"dense_act_fn": "relu",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "relu",
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": false,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"n_positions": 512,
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"num_decoder_layers": 24,
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"num_heads": 16,
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"num_layers": 24,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.26.1",
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"use_cache": true,
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"vocab_size": 32128
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}
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