Instructions to use benschlagman/tapas_fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benschlagman/tapas_fine_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("table-question-answering", model="benschlagman/tapas_fine_tuning")# Load model directly from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("benschlagman/tapas_fine_tuning") model = AutoModelForTableQuestionAnswering.from_pretrained("benschlagman/tapas_fine_tuning") - Notebooks
- Google Colab
- Kaggle
Commit ·
a3e3503
1
Parent(s): 9216d26
add model
Browse files- pytorch_model.bin +1 -1
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1347091016
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:328e3bb137fef75c245ee28143a8d85b619a325c095627f1b5252cd64ae9ac20
|
| 3 |
size 1347091016
|