Text Classification
Transformers
Safetensors
Vietnamese
claim_verification
SemViQA
binary-classification
fact-checking
Instructions to use SemViQA/bc-xlmr-viwikifc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SemViQA/bc-xlmr-viwikifc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SemViQA/bc-xlmr-viwikifc")# Load model directly from transformers import ClaimModelForClassification model = ClaimModelForClassification.from_pretrained("SemViQA/bc-xlmr-viwikifc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +4 -1
- model.safetensors +3 -0
config.json
CHANGED
|
@@ -1,5 +1,7 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
|
|
|
|
|
|
| 3 |
"dropout": 0.3,
|
| 4 |
"id2label": {
|
| 5 |
"0": "LABEL_0",
|
|
@@ -12,5 +14,6 @@
|
|
| 12 |
"loss_type": "ce",
|
| 13 |
"model_name": "FacebookAI/xlm-roberta-large",
|
| 14 |
"model_type": "claim_verification",
|
|
|
|
| 15 |
"transformers_version": "4.47.0"
|
| 16 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"ClaimModelForClassification"
|
| 4 |
+
],
|
| 5 |
"dropout": 0.3,
|
| 6 |
"id2label": {
|
| 7 |
"0": "LABEL_0",
|
|
|
|
| 14 |
"loss_type": "ce",
|
| 15 |
"model_name": "FacebookAI/xlm-roberta-large",
|
| 16 |
"model_type": "claim_verification",
|
| 17 |
+
"torch_dtype": "float32",
|
| 18 |
"transformers_version": "4.47.0"
|
| 19 |
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f5c70e33b6e4adf55d2827f4441686241ee93124f0963e7c56a230ba01d9edff
|
| 3 |
+
size 2239617488
|