Upload Reddit Sentiment Analysis Hybrid Model

README.md

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+---
+license: mit
+tags:
+- sentiment-analysis
+- text-classification
+- multiclass-classification
+- sentence-transformers
+- xgboost
+- reddit
+- hybrid-model
+language:
+- en
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+widget:
+- text: "I love this product! It's amazing and works perfectly."
+  example_title: "Positive Example"
+- text: "This is terrible. I hate it so much."
+  example_title: "Negative Example"
+- text: "The weather is okay today."
+  example_title: "Neutral Example"
+---
+
+# Reddit Sentiment Analysis - Hybrid Model
+
+🎯 **Test Accuracy: 0.9966**
+
+## Model Description
+
+This hybrid sentiment analysis model combines **Sentence Transformers** for semantic embeddings with **XGBoost** for classification. Trained on Reddit comments for multiclass sentiment analysis: **Negative**, **Positive**, and **Neutral**.
+
+### Architecture
+```
+Input Text → SentenceTransformer → Embeddings (768D) →
+Feature Engineering (Length + Sentiment + POS) → XGBoost → Prediction
+```
+
+## Quick Start
+
+```python
+import pickle
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from textblob import TextBlob
+import nltk
+from huggingface_hub import hf_hub_download
+
+# Download NLTK data
+nltk.download('punkt', quiet=True)
+nltk.download('averaged_perceptron_tagger', quiet=True)
+
+# Load models
+xgb_path = hf_hub_download(repo_id="USERNAME/sentimental_analysis_updated", filename="xgboost_model.pkl")
+sentence_path = hf_hub_download(repo_id="USERNAME/sentimental_analysis_updated", filename="sentence_transformer")
+
+# Load XGBoost model
+with open(xgb_path, 'rb') as f:
+    pipeline_data = pickle.load(f)
+    xgb_model = pipeline_data['xgboost_model']
+    label_names = pipeline_data['label_names']
+
+# Load SentenceTransformer
+sentence_model = SentenceTransformer(sentence_path)
+
+def predict_sentiment(text):
+    # Extract features
+    embedding = sentence_model.encode([text])
+    comment_length = np.array([len(text.split())]).reshape(-1, 1)
+    sentiment_polarity = np.array([TextBlob(text).sentiment.polarity]).reshape(-1, 1)
+
+    # POS counts
+    try:
+        tags = nltk.pos_tag(nltk.word_tokenize(text))
+        pos_counts = np.array([[
+            sum(1 for _, tag in tags if tag.startswith('J')),  # Adjectives
+            sum(1 for _, tag in tags if tag.startswith('N')),  # Nouns
+            sum(1 for _, tag in tags if tag.startswith('V'))   # Verbs
+        ]])
+    except:
+        pos_counts = np.array([[0, 0, 0]])
+
+    # Combine features
+    features = np.hstack([embedding, comment_length, sentiment_polarity, pos_counts])
+
+    # Predict
+    prediction = xgb_model.predict(features)[0]
+    confidence = xgb_model.predict_proba(features)[0].max()
+
+    return {
+        'label': label_names[prediction],
+        'confidence': confidence,
+        'prediction_id': int(prediction)
+    }
+
+# Example usage
+result = predict_sentiment("I love this new phone! It's amazing!")
+print(f"Sentiment: {result['label']} (confidence: {result['confidence']:.3f})")
+```
+
+## Model Details
+
+- **Base Model**: `paraphrase-mpnet-base-v2`
+- **Classifier**: XGBoost with GPU acceleration
+- **Features**: 772 dimensions (768 embeddings + 4 engineered)
+- **Classes**: 0=Negative, 1=Positive, 2=Neutral
+- **Training Data**: Reddit comments
+- **Test Accuracy**: 0.9966
+
+## Training Configuration
+
+- **XGBoost Parameters**: n_estimators=300, learning_rate=0.05, max_depth=6
+- **Features**: Embeddings + Comment Length + TextBlob Sentiment + POS Counts
+- **Class Balancing**: Sample weights for imbalanced data
+- **Validation**: Stratified train/val/test split
+
+## Citation
+
+```bibtex
+@misc{reddit-sentiment-hybrid,
+  title={Reddit Sentiment Analysis - Hybrid Model},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/USERNAME/sentimental_analysis_updated}
+}
+```
+
+## License
+
+MIT License

config.json

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+{
+  "model_type": "hybrid-sentiment-classifier",
+  "architecture": "sentence-transformers + xgboost",
+  "base_model": "paraphrase-mpnet-base-v2",
+  "num_classes": 3,
+  "class_names": [
+    "Negative",
+    "Positive",
+    "Neutral"
+  ],
+  "test_accuracy": 0.9966352624495289
+}

requirements.txt

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+sentence-transformers>=2.2.0
+xgboost>=1.6.0
+scikit-learn>=1.0.0
+numpy>=1.21.0
+pandas>=1.3.0
+textblob>=0.17.0
+nltk>=3.7
+torch>=1.9.0
+huggingface-hub>=0.10.0

sentence_transformer/1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

sentence_transformer/README.md

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+---
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+pipeline_tag: sentence-similarity
+---
+
+# sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')
+model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
+
+## Citing & Authors
+
+This model was trained by [sentence-transformers](https://www.sbert.net/). 
+        
+If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
+```bibtex 
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "http://arxiv.org/abs/1908.10084",
+}
+```

sentence_transformer/config.json

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+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.2",
+  "vocab_size": 30527
+}

sentence_transformer/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.0.0",
+    "transformers": "4.53.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

sentence_transformer/model.safetensors

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sentence_transformer/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_transformer/sentence_bert_config.json

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+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

sentence_transformer/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
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+    "rstrip": false,
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

sentence_transformer/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+  "tokenizer_class": "MPNetTokenizer",
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+}

xgboost_model.pkl

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