Instructions to use theta/deeper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theta/deeper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="theta/deeper")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("theta/deeper") model = AutoModelForSequenceClassification.from_pretrained("theta/deeper") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 260
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 409152629
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9d1c115385c6ab6249167faba932182c93242281e77da56abc1079a3311a5177
|
| 3 |
size 409152629
|
runs/Feb01_09-31-24_d5f7524d6b13/events.out.tfevents.1675244006.d5f7524d6b13.296.0
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:adbb9cab74d810262e732885f0d986943152b90be096a9335fd1216c6a3d1f57
|
| 3 |
+
size 9518
|