Instructions to use hadifar/eventextraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadifar/eventextraction with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("hadifar/eventextraction") model = AutoModelForSeq2SeqLM.from_pretrained("hadifar/eventextraction") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +0 -0
- tokenizer_config.json +2 -1
tokenizer.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
CHANGED
|
@@ -104,8 +104,9 @@
|
|
| 104 |
"clean_up_tokenization_spaces": true,
|
| 105 |
"eos_token": "</s>",
|
| 106 |
"extra_ids": 100,
|
| 107 |
-
"model_max_length":
|
| 108 |
"pad_token": "<pad>",
|
|
|
|
| 109 |
"tokenizer_class": "T5Tokenizer",
|
| 110 |
"unk_token": "<unk>"
|
| 111 |
}
|
|
|
|
| 104 |
"clean_up_tokenization_spaces": true,
|
| 105 |
"eos_token": "</s>",
|
| 106 |
"extra_ids": 100,
|
| 107 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 108 |
"pad_token": "<pad>",
|
| 109 |
+
"sp_model_kwargs": {},
|
| 110 |
"tokenizer_class": "T5Tokenizer",
|
| 111 |
"unk_token": "<unk>"
|
| 112 |
}
|