Instructions to use JeremyLi96/Control_Token_Joint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremyLi96/Control_Token_Joint 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="JeremyLi96/Control_Token_Joint")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JeremyLi96/Control_Token_Joint") model = AutoModelForSeq2SeqLM.from_pretrained("JeremyLi96/Control_Token_Joint") - Notebooks
- Google Colab
- Kaggle
Zihao Li commited on
Update README.md
Browse files
README.md
CHANGED
|
@@ -9,4 +9,5 @@ metrics:
|
|
| 9 |
- bertscore
|
| 10 |
pipeline_tag: summarization
|
| 11 |
---
|
| 12 |
-
A customised model based on MUSS, with added special tokens and tokenization method.
|
|
|
|
|
|
| 9 |
- bertscore
|
| 10 |
pipeline_tag: summarization
|
| 11 |
---
|
| 12 |
+
A customised model based on MUSS, with added special tokens and tokenization method.\
|
| 13 |
+
The 4 control tokens are "<DEPENDENCYTREEDEPTHRATIO_1.0>", "<WORDRANKRATIO_1.0>", "<REPLACEONLYLEVENSHTEIN_1.0>", "<LENGTHRATIO_1.0>", whose values range from 0.2 to 1.5 (except for the REPLACEONLYLEVENSHTEIN maximises at 1.0).
|