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.gitattributes

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README.md

@@ -1,3 +1,30 @@
 ---
-license: apache-2.0
+language: "en"
+tags:
+- financial-text-analysis
+- forward-looking-statement
+widget:
+- text: "We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs. "
 ---
+
+Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Management Discussion and Analysis section of annual reports of Russell 3000 firms.  
+
+**Input**: A financial text.
+
+**Output**: Specific-FLS , Non-specific FLS, or Not-FLS.
+
+# How to use 
+You can use this model with Transformers pipeline for forward-looking statement classification.
+```python
+# tested in transformers==4.18.0 
+from transformers import BertTokenizer, BertForSequenceClassification, pipeline
+
+finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-fls',num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-fls')
+nlp = pipeline("text-classification", model=finbert, tokenizer=tokenizer)
+results = nlp('We expect the age of our fleet to enhance availability and reliability due to reduced downtime for repairs.')
+print(results)  # [{'label': 'Specific FLS', 'score': 0.77278733253479}]
+
+```
+
+Visit [FinBERT.AI](https://finbert.ai/) for more details on the recent development of FinBERT.

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Not FLS",
+    "1": "Non-specific FLS",
+    "2": "Specific FLS"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Not FLS": 0,
+    "Non-specific FLS": 1,
+    "Specific FLS": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.3.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30873
+}

pytorch_model.bin

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+size 439104222

training_args.bin

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