Instructions to use dauvannam321/XLM_augmented_concat_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dauvannam321/XLM_augmented_concat_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dauvannam321/XLM_augmented_concat_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dauvannam321/XLM_augmented_concat_data") model = AutoModelForSequenceClassification.from_pretrained("dauvannam321/XLM_augmented_concat_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 1424
Browse files
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1112343428
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:452122f5c5e971d434e3ba603e83212efd4ebd995a5a715af44a9e740596fc82
|
| 3 |
size 1112343428
|
runs/Jun24_01-30-09_38485498d310/events.out.tfevents.1719192614.38485498d310.1016.0
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 sha256:414697eedc9a78fa35a2c2a26037668433801ce4fec46d9f82ece896616b06c2
|
| 3 |
+
size 9271
|