Instructions to use Jellevdl/Bert-test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jellevdl/Bert-test-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Jellevdl/Bert-test-model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Jellevdl/Bert-test-model") model = AutoModelForQuestionAnswering.from_pretrained("Jellevdl/Bert-test-model") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 435643185
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:554e577eb76adcba9563f8be2c65e44fe4edb029a3b647eb84a7284202604230
|
| 3 |
size 435643185
|
runs/Dec06_14-00-51_cd720db1179c/events.out.tfevents.1670335257.cd720db1179c.71.0
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:a0cecd7983290c47aba27ec07cdb653bf56437da222fd0ac09380acf4f5f47e6
|
| 3 |
+
size 5034
|