Update README.md

README.md

@@ -9,13 +9,81 @@ metrics:
 - f1
 pipeline_tag: text-classification
 tags:
-- finance
 - sentiment
+- finance
 - sentiment-analysis
-- tweets
+- financial-sentiment-analysis
 - twitter
+- tweets
+- stocks
+- stock-market
+- crypto
+- cryptocurrency
 base_model: StephanAkkerman/FinTwitBERT
 ---
 
 # FinTwitBERT-sentiment
-FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets.
+
+FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses [FinTwitBERT](https://huggingface.co/StephanAkkerman/FinTwitBERT) as a base model, which has been pre-trained on 1 million financial tweets.
+This approach ensures that the FinTwitBERT-sentiment has seen enough financial tweets, which have an informal nature, compared to other financial texts, such as news headlines.
+Therefore this model performs great on informal financial texts, seen on social media.
+
+## Intended Uses
+
+FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts.
+
+## More Information
+
+For a comprehensive overview, including the training setup and analysis of the model, visit the [FinTwitBERT GitHub repository](https://github.com/TimKoornstra/FinTwitBERT).
+
+## Usage
+
+Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model and tokenizers can be converted into a pipeline for text classification.
+
+```python
+from transformers import BertForSequenceClassification, AutoTokenizer, pipeline
+
+model = BertForSequenceClassification.from_pretrained(
+    "StephanAkkerman/FinTwitBERT-sentiment",
+    num_labels=3,
+    id2label={0: "NEUTRAL", 1: "BULLISH", 2: "BEARISH"},
+    label2id={"NEUTRAL": 0, "BULLISH": 1, "BEARISH": 2},
+)
+model.config.problem_type = "single_label_classification"
+tokenizer = AutoTokenizer.from_pretrained(
+    "StephanAkkerman/FinTwitBERT-sentiment"
+)
+model.eval()
+pipeline = pipeline(
+    "text-classification", model=model, tokenizer=tokenizer
+)
+
+# Sentences we want the sentiment for
+sentence = ["I love you"]
+
+# Get the predicted sentiment
+print(pipeline(sentence))
+```
+
+## Training
+
+The model was trained with the following parameters:
+
+## Citing & Authors
+
+If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work:
+
+```bibtex
+@misc{FinTwitBERT,
+  author = {Stephan Akkerman, Tim Koornstra},
+  title = {FinTwitBERT: A Specialized Language Model for Financial Tweets},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/TimKoornstra/FinTwitBERT}}
+}
+```
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details.