Instructions to use ck46/t5-base-hotpot-qa-qg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ck46/t5-base-hotpot-qa-qg with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ck46/t5-base-hotpot-qa-qg") model = AutoModelForSeq2SeqLM.from_pretrained("ck46/t5-base-hotpot-qa-qg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +8 -68
config.json
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{
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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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_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"summarization": {
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"early_stopping": true,
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"max_length": 32,
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"num_beams": 4,
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"prefix": ""
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"translation_en_to_fr": {
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"early_stopping": true,
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"early_stopping": true,
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"max_length": 32,
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"prefix": ""
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{
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"_name_or_path": "ck46/t5-base-hotpot-qa-qg",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 3072,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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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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"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": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_num_buckets": 32,
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"task_specific_params": {
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"translation_en_to_fr": {
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"early_stopping": true,
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"max_length": 32,
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"num_beams": 4,
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"prefix": ""
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.15.0",
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"use_cache": true,
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"vocab_size": 32102
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}
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