Upload fine-tuned FinBERT for financial news sentiment

README.md

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+# FinBERT Fine-Tuned for Financial Sentiment Analysis
+
+This repository contains a fine-tuned version of FinBERT (RoBERTa-based) for financial sentiment classification. The model predicts whether a financial news headline or sentence is **positive**, **neutral**, or **negative**.
+
+## Model Overview
+- **Base model:** FinBERT (RoBERTa)
+- **Task:** Financial sentiment classification (3 classes)
+- **Training data:** Financial news headlines and sentences
+- **Dataset source:** [Kaggle - Finance News Sentiments](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv)
+- **Output labels:**
+  - 0: Negative
+  - 1: Neutral
+  - 2: Positive
+
+## Evaluation Results
+- **Test Accuracy:** 0.7565
+- **Multiclass ROC AUC (macro-average):** 0.9096
+
+## Model Folder Structure
+```
+finbert_finetuned/
+    config.json
+    merges.txt
+    model.safetensors
+    special_tokens_map.json
+    tokenizer_config.json
+    tokenizer.json
+    vocab.json
+```
+**Note:** Only the model files are stored in `finbert_finetuned_news_sentiment/`. Scripts and datasets are kept separate and are not included in this folder or in the model upload.
+
+## How to Use the Fine-Tuned Model
+
+### 1. Load and Use the Model in Python
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# Directory of the model folder
+model_dir = "finbert_finetuned_news_sentiment"
+# read the model
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+model.eval()
+
+text = "Apple stock surges after strong earnings report."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+with torch.no_grad():
+    logits = model(**inputs).logits
+    pred = torch.argmax(logits, dim=1).item()
+
+label_map = {0: 'negative', 1: 'neutral', 2: 'positive'}
+print(f"Predicted sentiment: {label_map[pred]}")
+```
+
+## Notes
+- The model was trained and evaluated on data from the Kaggle dataset linked above.
+- The `finbert_finetuned_news_sentiment/` folder contains only the files needed for inference.
+- Scripts and datasets are not included in the model folder or in the model upload.
+- For best results, use a GPU for inference if available.
+
+## Citation
+If you use this model, please cite the original [FinBERT paper](https://arxiv.org/abs/2006.08097) and the [Kaggle dataset](https://www.kaggle.com/datasets/antobenedetti/finance-news-sentiments/data?select=dataset.csv).
+
+---
+
+**Date:** June 2025

config.json

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+{
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.05,
+  "bos_token_id": 0,
+  "classifier_dropout": 0.1,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.05,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1365a50dbfec1b19130cc5aeb82c269fc2cee87ece67e23b3d2128b87c351fdf
+size 1421499516

special_tokens_map.json

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+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}