Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
cross-encoder
rerank
text-embeddings-inference
Instructions to use namdp-ptit/ViRanker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namdp-ptit/ViRanker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="namdp-ptit/ViRanker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("namdp-ptit/ViRanker") model = AutoModelForSequenceClassification.from_pretrained("namdp-ptit/ViRanker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +1 -1
- model.safetensors +3 -0
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
"XLMRobertaModel"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "/home/namdp/crag/checkpoint",
|
| 3 |
"architectures": [
|
| 4 |
"XLMRobertaModel"
|
| 5 |
],
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7fd4d3a7a53d8a605a60e77bb421c6b61ec59a5328ba5c414e45ca6f2f89f097
|
| 3 |
+
size 2271064456
|