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README.md

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+---
+language: "en"
+tags:
+- financial-sentiment-analysis
+- sentiment-analysis
+widget:
+- text: "growth is strong and we have plenty of liquidity"
+---
+
+`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.
+- Corporate Reports 10-K & 10-Q: 2.5B tokens
+- Earnings Call Transcripts: 1.3B tokens
+- Analyst Reports: 1.1B tokens
+
+More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
+
+This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
+
+If you use the model in your academic work, please cite the following paper:
+
+Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
+
+
+# How to use 
+You can use this model with Transformers pipeline for sentiment analysis.
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
+
+finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
+
+nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+
+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"]
+results = nlp(sentences)
+print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
+
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "id2label": {
+    "0": "Neutral",
+    "1": "Positive",
+    "2": "Negative"
+  },
+  "label2id": {
+    "Positive": 1,
+    "Negative": 2,
+    "Neutral": 0
+  },
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 512,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "type_vocab_size": 2,
+  "vocab_size": 30873
+}