Summarization
Transformers
Safetensors
PyTorch
English
t5
text2text-generation
t5-small
text-summarization
text-generation-inference
Instructions to use unnat17/Text-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unnat17/Text-Summarizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="unnat17/Text-Summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("unnat17/Text-Summarizer") model = AutoModelForSeq2SeqLM.from_pretrained("unnat17/Text-Summarizer") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer_config.json with huggingface_hub
Browse files- tokenizer_config.json +2 -1
tokenizer_config.json
CHANGED
|
@@ -7,5 +7,6 @@
|
|
| 7 |
"model_max_length": 512,
|
| 8 |
"pad_token": "<pad>",
|
| 9 |
"tokenizer_class": "T5Tokenizer",
|
| 10 |
-
"unk_token": "<unk>"
|
|
|
|
| 11 |
}
|
|
|
|
| 7 |
"model_max_length": 512,
|
| 8 |
"pad_token": "<pad>",
|
| 9 |
"tokenizer_class": "T5Tokenizer",
|
| 10 |
+
"unk_token": "<unk>",
|
| 11 |
+
"extra_special_tokens": {}
|
| 12 |
}
|