# Paper Section Title Sequence Classifier
This is a model based on `allenai/scibert_scivocab_uncased`, fine-tuned to classify sequences of section titles from scientific papers.

The model takes a single string of section titles, separated by `[SEP]`, and predicts a class label for each section in the sequence. **It is crucial that the section sequence provided to the model preserves the ORIGINAL ORDER as it appears in the source document.**

# Model Details

- **Model Architecture**: This model uses a `BertModel` base with a custom classification head. It first extracts the embeddings corresponding to all `[SEP]` separators in the input sequence. Then, it passes these embeddings through a `TimeDistributed` layer and a linear classifier to generate a class prediction for each section title (represented by its `[SEP]` token).

- **Base Model**: `allenai/scibert_scivocab_uncased`

- **Language**: English

- **Labels**: The model can identify the following five classes:

  - `Introduction`

  - `Methods`

  - `Results`

  - `DiscussionConclusion`

  - `Closing (e.g., Acknowledgements, References)`

# How to Use

Note: This model uses a custom architecture. You must set `trust_remote_code=True` when loading it to allow Hugging Face to execute the model code defined in the repository.

```python
from transformers import AutoTokenizer, AutoModel

# 1. Specify model name
hub_model_name = "tomleung1996/section-title-sequence-classifier" 

# 2. Load the tokenizer and model from the Hub
# `trust_remote_code=True` is required because it needs to execute the custom model code from the repo
tokenizer = AutoTokenizer.from_pretrained(hub_model_name)
model = AutoModel.from_pretrained(hub_model_name, trust_remote_code=True)

# 3. Prepare the input data
# List all section titles from a paper, ensuring they are in their original order.
sections = [
    "Introduction",
    "Conceptualization of the directed collaboration network",
    "Analysis of the collaboration order based on the DCN",
    "An example",
    "Conclusions and discussion",
    "Acknowledgement",
    "References"
]

# 4. Join the list into a single string using the [SEP] token
input_text = " [SEP] ".join(sections)

# 5. Tokenize the input
inputs = tokenizer(input_text, return_tensors='pt')

# 6. Run model inference
# Note: The tokenizer must be passed to the model's forward pass
# because the model internally needs it to locate the [SEP] tokens.
outputs = model(**inputs, tokenizer=tokenizer)

# 7. Get the predictions
predictions = outputs.logits.argmax(dim=-1).squeeze()

# Get the label mapping from the model's config
pred_labels = [model.config.id2label[i.item()] for i in predictions]

# 8. Print the results
print("Input Sections:")
print(sections)
print("\nPredicted Labels:")
print(pred_labels)
# Expected output:
# ['Introduction', 'Methods', 'Methods', 'Results', 'DiscussionConclusion', 'Closing', 'Closing']
```