Instructions to use buscon/EducativeCS2023_bart-base-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buscon/EducativeCS2023_bart-base-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("buscon/EducativeCS2023_bart-base-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("buscon/EducativeCS2023_bart-base-summarization") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:776a08e67e7d1abcde726ade9cd6368a8b1849393733eb9e4f2d12fa0ad57064
|
| 3 |
+
size 557912964
|