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README.md

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+---
+language: ne
+license: apache-2.0
+tags:
+- sentiment-analysis
+- nepali
+- onnx
+- bert
+- text-classification
+datasets:
+- custom-nepali-sentiment
+metrics:
+- f1
+- accuracy
+model-index:
+- name: mohit4519/nepali-sentiment
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: Nepali Sentiment Dataset
+      type: custom
+    metrics:
+    - type: f1
+      value: 0.XX  # Replace with your actual score
+      name: Macro F1
+---
+
+# Nepali Sentiment Analysis (ONNX)
+
+This model is a fine-tuned BERT model for Nepali sentiment analysis, exported to ONNX format for optimized inference.
+
+## Model Details
+
+- **Base Model**: Shushant/nepaliBERT
+- **Task**: Sentiment Classification (3-class)
+- **Labels**: 
+  - 0: Negative
+  - 1: Positive
+  - 2: Neutral
+- **Format**: ONNX (optimized for fast inference)
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers optimum[onnxruntime]
+```
+
+### Inference
+
+```python
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model = ORTModelForSequenceClassification.from_pretrained("mohit4519/nepali-sentiment")
+tokenizer = AutoTokenizer.from_pretrained("Shushant/nepaliBERT")
+
+# Predict sentiment
+text = "यो धेरै राम्रो छ"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+prediction = torch.argmax(outputs.logits, dim=-1).item()
+
+sentiment_map = {0: 'Negative', 1: 'Positive', 2: 'Neutral'}
+print(f"Sentiment: {sentiment_map[prediction]}")
+```
+
+## Performance
+
+- **Macro F1 Score**: 0.XX (Replace with your score)
+- **Accuracy**: 0.XX (Replace with your score)
+
+## Training Data
+
+Trained on Nepali sentiment dataset containing social media text, reviews, and comments.
+
+## Limitations
+
+- Best performance on Nepali text
+- May have reduced accuracy on code-mixed or transliterated text
+- Performance varies across different domains

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 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",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.4",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.onnx

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+size 438148593

model.safetensors

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