Instructions to use slconnin/distilbert-lora-classification-v2-5-quicktest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slconnin/distilbert-lora-classification-v2-5-quicktest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="slconnin/distilbert-lora-classification-v2-5-quicktest")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("slconnin/distilbert-lora-classification-v2-5-quicktest") model = AutoModelForSequenceClassification.from_pretrained("slconnin/distilbert-lora-classification-v2-5-quicktest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForSequenceClassification
Browse files- model.safetensors +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 267838720
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7c58f244c1d084e3e796402131907b4b49ffd17b84bea6b2665a1734dc40709a
|
| 3 |
size 267838720
|