Merge branch 'main' of https://huggingface.co/AnReu/albert-for-math-ar-base-ft into main

README.md

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+# ALBERT for Math AR
+
+This model is further pre-trained on the Mathematics StackExchange questions and answers. It is based on Albert base v2 and uses the same tokenizer. In addition to pre-training the model was finetuned on Math Question Answer Retrieval. The sequence classification head is trained to output a relevance score if you input the question as the first segment and the answer as the second segment. You can use the relevance score to rank different answers for retrieval.
+
+## Usage
+```python
+# based on https://huggingface.co/docs/transformers/main/en/task_summary#sequence-classification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
+model = AutoModelForSequenceClassification.from_pretrained("AnReu/albert-for-math-ar-base-ft")
+
+classes = ["non relevant", "relevant"]
+
+sequence_0 = "How can I calculate x in $3x = 5$"
+sequence_1 = "Just divide by 3: $x = \\frac{5}{3}$"
+sequence_2 = "The general rule for squaring a sum is $(a+b)^2=a^2+2ab+b^2$"
+
+# The tokenizer will automatically add any model specific separators (i.e. <CLS> and <SEP>) and tokens to
+# the sequence, as well as compute the attention masks.
+irrelevant = tokenizer(sequence_0, sequence_2, return_tensors="pt")
+relevant = tokenizer(sequence_0, sequence_1, return_tensors="pt")
+
+irrelevant_classification_logits = model(**irrelevant).logits
+relevant_classification_logits = model(**relevant).logits
+
+irrelevant_results = torch.softmax(irrelevant_classification_logits, dim=1).tolist()[0]
+relevant_results = torch.softmax(relevant_classification_logits, dim=1).tolist()[0]
+
+# Should be irrelevant
+for i in range(len(classes)):
+    print(f"{classes[i]}: {int(round(irrelevant_results[i] * 100))}%")
+
+# Should be relevant
+for i in range(len(classes)):
+    print(f"{classes[i]}: {int(round(relevant_results[i] * 100))}%")
+```
+
+## Reference
+If you use this model, please consider referencing our paper:
+
+```bibtex
+@inproceedings{reusch2021tu_dbs,
+  title={TU\_DBS in the ARQMath Lab 2021, CLEF},
+  author={Reusch, Anja and Thiele, Maik and Lehner, Wolfgang},
+  year={2021},
+  organization={CLEF}
+}
+```