model documentation

README.md

@@ -1,13 +1,192 @@
-`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
+---
+language:
+- en
 
-- Corporate Reports 10-K & 10-Q: 2.5B tokens
-- Earnings Call Transcripts: 1.3B tokens
-- Analyst Reports: 1.1B tokens
+---
+# Model Card for FinBERT
+ 
+ 
+# Model Details
+ 
+## Model Description
+`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice.
+ 
+- **Developed by:** Yi Yang
+- **Shared by [Optional]:** Hugging Face
+- **Model type:** Fill-Mask
+- **Language(s) (NLP):** en
+- **License:** More information needed
+- **Related Models:** More information needed
+  - **Parent Model:** BERT
+- **Resources for more information:** 
+    - [GitHub Repo](https://github.com/yya518/FinBERT) 
+    - [Associated Paper](https://arxiv.org/abs/2006.08097) 
+    - [Website](https://finbert.ai/)
+ 
+# Uses
+ 
+ 
+ 
+## Direct Use
+Fill-Mask
+ 
+## Downstream Use [Optional]
+ 
+`FinBERT` can be further fine-tuned on downstream tasks. Specifically, we have fine-tuned `FinBERT` for financial sentiment analysis, ESG classification, Forward-looking statement classification and etc.
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people. 
+ 
+# Bias, Risks, and Limitations
+ 
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+ 
+ 
+## Recommendations
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+ 
+ 
+# Training Details
+ 
+## Training Data
+ 
+FinBERT is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
+ 
+- **Corporate Reports 10-K & 10-Q:** 2.5B tokens
+- **Earnings Call Transcripts:** 1.3B tokens
+- **Analyst Reports:** 1.1B tokens
+ 
+ 
+## Training Procedure
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+FinVocab is a new WordPiece vocabulary on our financial corpora using the SentencePiece library. We produce both cased and uncased versions of FinVocab, with sizes of 28,573 and 30,873 tokens respectively. This is very similar to the 28,996 and 30,522 token sizes of the original BERT cased and uncased BaseVocab.
+ 
+ 
+ 
+# Evaluation
+ 
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed
+ 
+### Factors
+ 
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+## Results 
+ 
+More information needed
+ 
+# Model Examination
+ 
+More information needed
+ 
+# Environmental Impact
+ 
+ 
+ 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **Hardware Type:** More information needed
+- **Hours used:** More information needed
+- **Cloud Provider:** More information needed
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+ 
+# Technical Specifications [optional]
+ 
+## Model Architecture and Objective
+ 
+More information needed
+ 
+## Compute Infrastructure
+ 
+More information needed
+ 
+### Hardware
+ 
+More information needed
+ 
+### Software
+ 
+More information needed
+ 
+# Citation
+ 
+ 
+ 
+**BibTeX:**
+ 
+```
+@misc{yang2020finbert,
+    title={FinBERT: A Pretrained Language Model for Financial Communications},
+    author={Yi Yang and Mark Christopher Siy UY and Allen Huang},
+    year={2020},
+    eprint={2006.08097},
+    archivePrefix={arXiv},
+    }
 
-If you use the model in your academic work, please cite the following papers:
+```
+ 
+ 
+ 
+ 
+# Glossary [optional]
+More information needed
+ 
+# More Information [optional]
+ 
+Please post a Github issue or contact [imyiyang@ust.hk](imyiyang@ust.hk) if you have any questions.
+ 
+# Model Card Authors [optional]
+Yi Yang in collaboration with Ezi Ozoani and the Hugging Face team.
+ 
+# Model Card Contact
+ 
+More information needed
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+ 
+<details>
+<summary> Click to expand </summary>
 
-Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
-
-Yang, Yi, Mark Christopher Siy Uy, and Allen Huang. "Finbert: A pretrained language model for financial communications." *arXiv preprint arXiv:2006.08097* (2020).
-
-`FinBERT` can be further fine-tuned on downstream tasks. Specifically, we have fine-tuned `FinBERT` for financial sentiment analysis, ESG classification, Forward-looking statement classification and etc. Visit [FinBERT.AI](https://finbert.ai/) for more details on these task-specific models and recent development of FinBERT.
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import numpy as np
+ 
+finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
+ 
+sentences = ["there is a shortage of capital, and we need extra financing", 
+             "growth is strong and we have plenty of liquidity", 
+             "there are doubts about our finances", 
+             "profits are flat"]
+ 
+inputs = tokenizer(sentences, return_tensors="pt", padding=True)
+outputs = finbert(**inputs)[0]
+ 
+labels = {0:'neutral', 1:'positive',2:'negative'}
+for idx, sent in enumerate(sentences):
+    print(sent, '----', labels[np.argmax(outputs.detach().numpy()[idx])])
+ 
+ ```
+</details>