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---
license: apache-2.0
datasets:
- squad_v2
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: sentence-similarity
---
# Albert-xxl-v1 with SQuAD 2.0 sentences
## Objective
The model was trained as cross-encoder classification model with the objective to re-rank the results in a QA pipline.
## How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("apohllo/albert-xxl-squad-sentences", num_labels=2)
tokenizer = AutoTokenizer.from_pretrained("apohllo/albert-xxl-squad-sentences")
from transformers import pipeline
# Add device=0 if you want to use GPU!
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, batch_size=16) #, device=0)
sentences = [...] # some sentences to be re-ranked, wrt to the question
question = "..." # a question to be asked against the sentences
samples = [{"text": s, "text_pair": question} for s in sentences]
results = classifier(samples)
results = [(idx, r["score"]) if r["label"] == 'LABEL_1' else (idx, 1 - r["score"])
for idx, r in enumerate(results)]
top_k = 5
keys_values = sorted(results, key=lambda e: -e[1])[:top_k]
```
## Data
The data from SQuAD 2.0 was sentence-split. The question + the sentence containing the answer was a positive example.
The question + the remaining sentence from the same Wikipedia passege were treated as hard negative examples.
The table balow reports the classification results on the validation set.
# Results
| | accuracy | F1 |
|----------------|----------|----------|
|ALBERT-xxlarge | 97.05 | 84.14 | |