Instructions to use LinaSaba/distilbert-base-task-multi-label-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinaSaba/distilbert-base-task-multi-label-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LinaSaba/distilbert-base-task-multi-label-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification") model = AutoModelForSequenceClassification.from_pretrained("LinaSaba/distilbert-base-task-multi-label-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 5
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 438032501
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9342ddb4de4fdbf3ecf65424b5d2a4a2bd4d83bca3eed219b5df020b1708b2a8
|
| 3 |
size 438032501
|
runs/May17_11-01-06_40e68b36b187/events.out.tfevents.1684321409.40e68b36b187.341.0
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 sha256:027cb7bc20bc8f05089625628a349a42651a3da4b18c7aa7d2633027a94a50bb
|
| 3 |
+
size 7424
|