Instructions to use vvn/test-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vvn/test-summary with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vvn/test-summary") model = AutoModelForSeq2SeqLM.from_pretrained("vvn/test-summary") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1000
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1625533697
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fa0873964613197972c2e72dedf1c6a1c0c3891854b144350ef9e41c0e6a4194
|
| 3 |
size 1625533697
|
runs/Nov24_08-55-51_75c58a4a285f/events.out.tfevents.1669280639.75c58a4a285f.17.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:53378a248bdbbf7f2ed2852068d247cecc0b04066763383106cf1291a51d89e1
|
| 3 |
+
size 5026
|