Instructions to use NTUYG/ComFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTUYG/ComFormer 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="NTUYG/ComFormer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NTUYG/ComFormer") model = AutoModelForSeq2SeqLM.from_pretrained("NTUYG/ComFormer") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
# How To Use
|
| 2 |
```PYTHON
|
| 3 |
from transformers import BartForConditionalGeneration, BartTokenizer
|
|
@@ -62,14 +74,3 @@ print(comment)
|
|
| 62 |
}
|
| 63 |
```
|
| 64 |
|
| 65 |
-
---
|
| 66 |
-
language:
|
| 67 |
-
- en
|
| 68 |
-
tags:
|
| 69 |
-
- summarization
|
| 70 |
-
license: apache-2.0
|
| 71 |
-
datasets:
|
| 72 |
-
- DeepCom
|
| 73 |
-
metrics:
|
| 74 |
-
- bleu
|
| 75 |
-
---
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- summarization
|
| 6 |
+
license: apache-2.0
|
| 7 |
+
datasets:
|
| 8 |
+
- DeepCom
|
| 9 |
+
metrics:
|
| 10 |
+
- bleu
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
# How To Use
|
| 14 |
```PYTHON
|
| 15 |
from transformers import BartForConditionalGeneration, BartTokenizer
|
|
|
|
| 74 |
}
|
| 75 |
```
|
| 76 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|