Instructions to use p-christ/QandAClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p-christ/QandAClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="p-christ/QandAClassifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("p-christ/QandAClassifier") model = AutoModelForSequenceClassification.from_pretrained("p-christ/QandAClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add model
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "test_trainer/checkpoint-
|
| 3 |
"architectures": [
|
| 4 |
"RobertaForSequenceClassification"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "test_trainer/checkpoint-3654",
|
| 3 |
"architectures": [
|
| 4 |
"RobertaForSequenceClassification"
|
| 5 |
],
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 328524461
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7f429e5797f1e7813f6b856dfb336773a5f380a88da55e7a16a922e38ec6ee23
|
| 3 |
size 328524461
|