---
library_name: transformers
license: apache-2.0
datasets:
- gtfintechlab/fomc_communication
- Sorour/fomc
language:
- en
metrics:
- accuracy
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
Fine-Tuned Transformer for FOMC Sentiment Classification


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This model is a fine-tuned version of [DistilBERT](https://huggingface.co/distilbert-base-uncased) for **FOMC meeting sentiment classification**. It predicts whether a sentence from U.S. Federal Open Market Committee (FOMC) statements is **Dovish**, **Hawkish**, or **Neutral**.

- **Developed by:** [Ao Chen]
- **Model type:** [Encoder-only Transformer (DistilBERT)]
- **Language(s) (NLP):** [en]
- **License:** [Apache 2.0]
- **Finetuned from model [optional]:** [distilbert-base-uncased]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "achen0525/DistilBERT_FOMC_Classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "The Committee decided to maintain the target range for the federal funds rate."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=1)

labels = ['Dovish', 'Hawkish', 'Neutral']
print(labels[pred.item()])
```

## Model Card Contact

For questions or feedback, reach out to: aochen@bu.edu