Instructions to use seidel/plsum-base-ptt5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seidel/plsum-base-ptt5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("seidel/plsum-base-ptt5") model = AutoModelForSeq2SeqLM.from_pretrained("seidel/plsum-base-ptt5") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -24,7 +24,7 @@ Project [github](https://github.com/aseidelo/wiki_generator/tree/cdd38918c207020
|
|
| 24 |
|
| 25 |
tokenizer = T5TokenizerFast.from_pretrained("seidel/plsum-base-ptt5")
|
| 26 |
model = T5ForConditionalGeneration.from_pretrained("seidel/plsum-base-ptt5", use_cache=False)
|
| 27 |
-
x = tokenizer([input_text], padding="max_length", max_length=
|
| 28 |
y = model.generate(**x)
|
| 29 |
print(tokenizer.batch_decode(y, skip_special_tokens=True))
|
| 30 |
|
|
|
|
| 24 |
|
| 25 |
tokenizer = T5TokenizerFast.from_pretrained("seidel/plsum-base-ptt5")
|
| 26 |
model = T5ForConditionalGeneration.from_pretrained("seidel/plsum-base-ptt5", use_cache=False)
|
| 27 |
+
x = tokenizer([input_text], padding="max_length", max_length=512, return_tensors="pt", truncation=True)
|
| 28 |
y = model.generate(**x)
|
| 29 |
print(tokenizer.batch_decode(y, skip_special_tokens=True))
|
| 30 |
|