Instructions to use EslamAhmed/customer_data_tuned_trial_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EslamAhmed/customer_data_tuned_trial_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EslamAhmed/customer_data_tuned_trial_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EslamAhmed/customer_data_tuned_trial_1") model = AutoModelForMaskedLM.from_pretrained("EslamAhmed/customer_data_tuned_trial_1") - Notebooks
- Google Colab
- Kaggle
Commit ·
4dfe851
1
Parent(s): 3776c1c
Upload DistilBertForMaskedLM
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 433421807
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7312a9159fe459222aba968e18e32c83924364113c189083d717749dba0684aa
|
| 3 |
size 433421807
|