38 lines
1.8 KiB
Markdown
38 lines
1.8 KiB
Markdown
|
# Cross-Encoder for Quora Duplicate Questions Detection
|
||
|
|
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
|
||
|
|
|
||
|
|
## Training Data
|
||
|
|
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
|
||
|
|
|
||
|
|
|
||
|
|
## Usage
|
||
|
|
|
||
|
|
Pre-trained models can be used like this:
|
||
|
|
```python
|
||
|
|
from sentence_transformers import CrossEncoder
|
||
|
|
model = CrossEncoder('model_name')
|
||
|
|
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
|
||
|
|
|
||
|
|
#Convert scores to labels
|
||
|
|
label_mapping = ['contradiction', 'entailment', 'neutral']
|
||
|
|
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
|
||
|
|
```
|
||
|
|
|
||
|
|
## Usage with Transformers AutoModel
|
||
|
|
You can use the model also directly with Transformers library (without SentenceTransformers library):
|
||
|
|
```python
|
||
|
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
||
|
|
import torch
|
||
|
|
|
||
|
|
model = AutoModelForSequenceClassification.from_pretrained('model_name')
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained('model_name')
|
||
|
|
|
||
|
|
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
|
||
|
|
|
||
|
|
model.eval()
|
||
|
|
with torch.no_grad():
|
||
|
|
scores = model(**features).logits
|
||
|
|
label_mapping = ['contradiction', 'entailment', 'neutral']
|
||
|
|
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
|
||
|
|
print(labels)
|
||
|
|
```
|