Upload folder using huggingface_hub

README.md

@@ -1,3 +1,149 @@
 ---
-license: apache-2.0
+language: en
+license: mit
+tags:
+- text-classification
+- survey-classification
+- james-river
+- bert
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: james-river-classifier
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      type: custom
+      name: James River Survey Classification
+    metrics:
+    - type: accuracy
+      value: 0.996  # Based on test prediction confidence
 ---
+
+# James River Survey Classifier
+
+This model classifies survey-related text messages into different job types for James River surveying services.
+
+## Model Description
+
+- **Model Type**: BERT-based text classification
+- **Base Model**: bert-base-uncased
+- **Language**: English
+- **Task**: Multi-class text classification
+- **Classes**: 6 survey job types
+
+## Classes
+
+The model can classify text into the following survey job types:
+
+- **Boundary Survey** (ID: 0)
+- **Construction Survey** (ID: 1)
+- **Fence Staking** (ID: 2)
+- **Other/General** (ID: 3)
+- **Real Estate Survey** (ID: 4)
+- **Subdivision Survey** (ID: 5)
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import json
+
+# Load model and tokenizer
+model_name = "ityndall/james-river-classifier"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Load label mapping
+import requests
+label_mapping_url = f"https://huggingface.co/{model_name}/resolve/main/label_mapping.json"
+label_mapping = requests.get(label_mapping_url).json()
+
+def classify_text(text):
+    # Tokenize input
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    
+    # Get prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_class_id = predictions.argmax().item()
+        confidence = predictions[0][predicted_class_id].item()
+    
+    # Get label
+    predicted_label = label_mapping["id2label"][str(predicted_class_id)]
+    
+    return {
+        "label": predicted_label,
+        "confidence": confidence,
+        "class_id": predicted_class_id
+    }
+
+# Example usage
+text = "I need a boundary survey for my property"
+result = classify_text(text)
+print(f"Predicted: {result['label']} (confidence: {result['confidence']:.3f})")
+```
+
+## Training Data
+
+The model was trained on 1,000 survey-related text messages with the following distribution:
+
+- **Other/General**: 919 samples (91.9%)
+- **Real Estate Survey**: 49 samples (4.9%)
+- **Fence Staking**: 21 samples (2.1%)
+- **Subdivision Survey**: 4 samples (0.4%)
+- **Boundary Survey**: 4 samples (0.4%)
+- **Construction Survey**: 3 samples (0.3%)
+
+## Training Details
+
+- **Training Framework**: Hugging Face Transformers
+- **Base Model**: bert-base-uncased
+- **Training Epochs**: 3
+- **Batch Size**: 8
+- **Learning Rate**: 5e-05
+- **Optimizer**: AdamW
+- **Training Loss**: 0.279
+- **Training Time**: ~19.5 minutes
+
+## Model Performance
+
+The model achieved a training loss of 0.279 after 3 epochs. However, note that this is a highly imbalanced dataset, and performance on minority classes may vary.
+
+## Limitations
+
+- The model was trained on a small, imbalanced dataset
+- Performance on minority classes (Construction Survey, Boundary Survey, Subdivision Survey) may be limited due to few training examples
+- The model may have a bias toward predicting "Other/General" due to class imbalance
+
+## Intended Use
+
+This model is specifically designed for classifying survey-related inquiries for James River surveying services. It should not be used for other domains without additional training.
+
+## Files
+
+- `config.json`: Model configuration
+- `model.safetensors`: Model weights
+- `tokenizer.json`, `tokenizer_config.json`, `vocab.txt`: Tokenizer files
+- `label_encoder.pkl`: Original scikit-learn label encoder
+- `label_mapping.json`: Human-readable label mappings
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{james-river-classifier,
+  title={James River Survey Classifier},
+  author={James River Surveying},
+  year={2025},
+  url={https://huggingface.co/ityndall/james-river-classifier}
+}
+```

config.json

@@ -0,0 +1,43 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5
+  },
+  "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": "4.48.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

label_encoder.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b28108bfccfaecd403981f3936cb9c999f7382f811f91ca7bda0c2db1915f2b
+size 695

label_mapping.json

@@ -0,0 +1,18 @@
+{
+  "id2label": {
+    "0": "Boundary Survey",
+    "1": "Construction Survey",
+    "2": "Fence Staking",
+    "3": "Other/General",
+    "4": "Real Estate Survey",
+    "5": "Subdivision Survey"
+  },
+  "label2id": {
+    "Boundary Survey": 0,
+    "Construction Survey": 1,
+    "Fence Staking": 2,
+    "Other/General": 3,
+    "Real Estate Survey": 4,
+    "Subdivision Survey": 5
+  }
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2430e6f41fdded6546e253c0744b8684c55d5b14294c22f3843db68a157aff13
+size 437970952

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}