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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 60
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 409149557
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3d5351f3b3805b5bdf2435b4ad6fac7fae987059cbbb4d25a332acfde217521c
|
| 3 |
size 409149557
|
runs/Jan31_14-18-51_63ebe3e27592/events.out.tfevents.1675174843.63ebe3e27592.141.0
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:f9682b32209741f2fa3f5b433c0998a3add7a27b677e98ac3c096940957f70cf
|
| 3 |
+
size 5108
|