Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use facebook/bart-large-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large-xsum 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="facebook/bart-large-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,4 +7,16 @@ language:
|
|
| 7 |
|
| 8 |
license: mit
|
| 9 |
|
| 10 |
-
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 7 |
|
| 8 |
license: mit
|
| 9 |
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
### Bart model finetuned on xsum
|
| 13 |
+
|
| 14 |
+
docs: https://huggingface.co/transformers/model_doc/bart.html
|
| 15 |
+
|
| 16 |
+
finetuning: examples/seq2seq/ (as of Aug 20, 2020)
|
| 17 |
+
|
| 18 |
+
Metrics: ROUGE > 22 on xsum.
|
| 19 |
+
|
| 20 |
+
variants: search for distilbart
|
| 21 |
+
|
| 22 |
+
paper: https://arxiv.org/abs/1910.13461
|