Instructions to use hf-tiny-model-private/tiny-random-ProphetNetForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ProphetNetForConditionalGeneration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetForConditionalGeneration") model = AutoModelForSeq2SeqLM.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
| { | |
| "activation_dropout": 0.1, | |
| "activation_function": "gelu", | |
| "add_cross_attention": true, | |
| "architectures": [ | |
| "ProphetNetForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": 1, | |
| "decoder_ffn_dim": 32, | |
| "decoder_start_token_id": 0, | |
| "disable_ngram_loss": false, | |
| "dropout": 0.1, | |
| "encoder_ffn_dim": 32, | |
| "eos_token_id": 2, | |
| "eps": 0.0, | |
| "hidden_size": 16, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "max_position_embeddings": 30, | |
| "model_type": "prophetnet", | |
| "ngram": 2, | |
| "num_buckets": 32, | |
| "num_decoder_attention_heads": 4, | |
| "num_decoder_layers": 4, | |
| "num_encoder_attention_heads": 4, | |
| "num_encoder_layers": 4, | |
| "pad_token_id": 0, | |
| "relative_max_distance": 128, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.28.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |