Instructions to use subhasisj/ar-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/ar-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/ar-TAPT-MLM-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM") - Notebooks
- Google Colab
- Kaggle
Training Completed
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 471689899
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0cf828a63c09f299dd51098dfea3eb08468303d17a2832755cd1ac9b4338ef80
|
| 3 |
size 471689899
|
runs/May10_20-44-29_4ebc0493976d/events.out.tfevents.1652215486.4ebc0493976d.741.0
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 sha256:0374145151bad01c40e698131744830f8b4168f07b0ce125000ea558bc2af606
|
| 3 |
+
size 5298
|