Upload PatentBERT PyTorch model

Modèle BERT fine-tuné pour la classification de brevets, converti de TensorFlow vers PyTorch.

Spécifications:
- Format: SafeTensors
- Classes: Détection automatique depuis config.json
- Conversion: TensorFlow 1.15 → PyTorch via transformers

Fichiers inclus:
labels.json, README.md, tokenizer_config.json, vocab.txt, model.safetensors, config.json

Files changed (3) hide show

README.md +26 -32
config.json +1312 -1312
labels.json +1313 -1313

README.md CHANGED Viewed

@@ -1,38 +1,37 @@
 # PatentBERT - PyTorch
-BERT model specialized for patent classification using the CPC (Cooperative Patent Classification) system.
 ## 📊 Specifications
-- **Output classes**: 656
 - **Classification system**: CPC (Cooperative Patent Classification)
 - **Architecture**: BERT-base (768 hidden, 12 layers, 12 attention heads)
 - **Vocabulary**: 30,522 tokens
 - **Format**: SafeTensors
-## 🏷️ CPC Classes
-The model predicts classes according to the CPC system:
-### Main Sections
-- **A**: Human Necessities
-- **B**: Performing Operations; Transporting
-- **C**: Chemistry; Metallurgy
-- **D**: Textiles; Paper
-- **E**: Fixed Constructions
-- **F**: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
-- **G**: Physics
-- **H**: Electricity
-- **Y**: General Tagging of New Technological Developments
-### Example Classes
-- `A01`: Agriculture; forestry; animal husbandry; hunting; trapping; fishing
-- `B01`: Physical or chemical processes or apparatus in general
-- `C01`: Inorganic chemistry
-- `G06`: Computing; calculating; counting
-- `H01`: Basic electric elements
 ## 🚀 Usage
@@ -45,11 +44,6 @@ import torch
 model = BertForSequenceClassification.from_pretrained('ZoeYou/patentbert-pytorch')
 tokenizer = BertTokenizer.from_pretrained('ZoeYou/patentbert-pytorch')
-# Load labels (optional - already in config.json)
-# with open('labels.json', 'r') as f:
-#     labels_info = json.load(f)
-# id2label = labels_info['id2label']
 # Inference example
 text = "A method for producing synthetic materials with enhanced thermal properties..."
 inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
@@ -62,31 +56,31 @@ with torch.no_grad():
 predicted_class_id = predictions.argmax().item()
 confidence = predictions.max().item()
-# Use model labels
 predicted_label = model.config.id2label[str(predicted_class_id)]
-print(f"Predicted class: {predicted_label} (ID: {predicted_class_id})")
 print(f"Confidence: {confidence:.2%}")
 ```
 ## 📁 Included Files
 - `model.safetensors`: Model weights (420 MB)
-- `config.json`: Configuration with integrated CPC labels
 - `vocab.txt`: Tokenizer vocabulary
 - `tokenizer_config.json`: Tokenizer configuration
-- `labels.json`: Detailed CPC label mapping
 - `README.md`: This documentation
 ## 🔬 Performance
-This model was trained on a large patent corpus to automatically classify documents according to the CPC system, facilitating organization and search in patent databases.
 ## 📖 References
 - [Cooperative Patent Classification (CPC)](https://www.cooperativepatentclassification.org/)
 - [Original PatentBERT Paper](https://arxiv.org/abs/2103.02557)
 ## 📝 Citation

 # PatentBERT - PyTorch
+BERT model specialized for patent classification using the **real CPC (Cooperative Patent Classification) system** from the original PatentBERT training data.
 ## 📊 Specifications
+- **Output classes**: 656 (real CPC labels)
 - **Classification system**: CPC (Cooperative Patent Classification)
 - **Architecture**: BERT-base (768 hidden, 12 layers, 12 attention heads)
 - **Vocabulary**: 30,522 tokens
 - **Format**: SafeTensors
+## 🏷️ CPC Classes (Real Distribution)
+The model predicts classes according to the authentic CPC system used in PatentBERT training:
+### Main Sections (Actual Counts)
+- **A (84 classes)**: Human Necessities - Agriculture, Food, Health, Sports
+- **B (171 classes)**: Performing Operations; Transporting - Manufacturing, Transport
+- **C (88 classes)**: Chemistry; Metallurgy - Chemical processes, Materials
+- **D (40 classes)**: Textiles; Paper - Fibers, Fabrics, Paper-making
+- **E (31 classes)**: Fixed Constructions - Building, Mining, Roads
+- **F (101 classes)**: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
+- **G (81 classes)**: Physics - Optics, Acoustics, Computing, Measuring
+- **H (51 classes)**: Electricity - Electronics, Power generation, Communication
+- **Y (9 classes)**: General Tagging of New Technological Developments
+### Example Real Classes
+- `A01B`: SOIL WORKING IN AGRICULTURE OR FORESTRY
+- `B25J`: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
+- `C07D`: HETEROCYCLIC COMPOUNDS
+- `G06F`: ELECTRIC DIGITAL DATA PROCESSING
+- `H04L`: TRANSMISSION OF DIGITAL INFORMATION
 ## 🚀 Usage
 model = BertForSequenceClassification.from_pretrained('ZoeYou/patentbert-pytorch')
 tokenizer = BertTokenizer.from_pretrained('ZoeYou/patentbert-pytorch')
 # Inference example
 text = "A method for producing synthetic materials with enhanced thermal properties..."
 inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True, padding=True)
 predicted_class_id = predictions.argmax().item()
 confidence = predictions.max().item()
+# Use model labels (real CPC codes)
 predicted_label = model.config.id2label[str(predicted_class_id)]
+print(f"Predicted CPC class: {predicted_label} (ID: {predicted_class_id})")
 print(f"Confidence: {confidence:.2%}")
 ```
 ## 📁 Included Files
 - `model.safetensors`: Model weights (420 MB)
+- `config.json`: Configuration with integrated real CPC labels
 - `vocab.txt`: Tokenizer vocabulary
 - `tokenizer_config.json`: Tokenizer configuration
+- `labels.json`: Complete real CPC label mapping (656 authentic labels)
 - `README.md`: This documentation
 ## 🔬 Performance
+This model was trained on a large patent corpus to automatically classify documents according to the real CPC system, using the exact same 656 CPC codes from the original PatentBERT training data.
 ## 📖 References
 - [Cooperative Patent Classification (CPC)](https://www.cooperativepatentclassification.org/)
 - [Original PatentBERT Paper](https://arxiv.org/abs/2103.02557)
+- [Hugging Face Transformers](https://huggingface.co/transformers/)
 ## 📝 Citation

config.json CHANGED Viewed

@@ -8,1322 +8,1322 @@
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "id2label": {
-    "0": "A01",
-    "1": "A02",
-    "2": "A03",
-    "3": "A04",
-    "4": "A05",
-    "5": "A06",
-    "6": "A07",
-    "7": "A08",
-    "8": "A09",
-    "9": "A10",
-    "10": "A11",
-    "11": "A12",
-    "12": "A13",
-    "13": "A14",
-    "14": "A15",
-    "15": "A16",
-    "16": "A17",
-    "17": "A18",
-    "18": "A19",
-    "19": "A20",
-    "20": "A21",
-    "21": "A22",
-    "22": "A23",
-    "23": "A24",
-    "24": "A25",
-    "25": "A26",
-    "26": "A27",
-    "27": "A28",
-    "28": "A29",
-    "29": "A30",
-    "30": "A31",
-    "31": "A32",
-    "32": "A33",
-    "33": "A34",
-    "34": "A35",
-    "35": "A36",
-    "36": "A37",
-    "37": "A38",
-    "38": "A39",
-    "39": "A40",
-    "40": "A41",
-    "41": "A42",
-    "42": "A43",
-    "43": "A44",
-    "44": "A45",
-    "45": "A46",
-    "46": "A47",
-    "47": "A48",
-    "48": "A49",
-    "49": "A50",
-    "50": "A51",
-    "51": "A52",
-    "52": "A53",
-    "53": "A54",
-    "54": "A55",
-    "55": "A56",
-    "56": "A57",
-    "57": "A58",
-    "58": "A59",
-    "59": "A60",
-    "60": "A61",
-    "61": "A62",
-    "62": "A63",
-    "63": "A64",
-    "64": "A65",
-    "65": "A66",
-    "66": "A67",
-    "67": "A68",
-    "68": "A69",
-    "69": "A70",
-    "70": "A71",
-    "71": "A72",
-    "72": "A73",
-    "73": "B01",
-    "74": "B02",
-    "75": "B03",
-    "76": "B04",
-    "77": "B05",
-    "78": "B06",
-    "79": "B07",
-    "80": "B08",
-    "81": "B09",
-    "82": "B10",
-    "83": "B11",
-    "84": "B12",
-    "85": "B13",
-    "86": "B14",
-    "87": "B15",
-    "88": "B16",
-    "89": "B17",
-    "90": "B18",
-    "91": "B19",
-    "92": "B20",
-    "93": "B21",
-    "94": "B22",
-    "95": "B23",
-    "96": "B24",
-    "97": "B25",
-    "98": "B26",
-    "99": "B27",
-    "100": "B28",
-    "101": "B29",
-    "102": "B30",
-    "103": "B31",
-    "104": "B32",
-    "105": "B33",
-    "106": "B34",
-    "107": "B35",
-    "108": "B36",
-    "109": "B37",
-    "110": "B38",
-    "111": "B39",
-    "112": "B40",
-    "113": "B41",
-    "114": "B42",
-    "115": "B43",
-    "116": "B44",
-    "117": "B45",
-    "118": "B46",
-    "119": "B47",
-    "120": "B48",
-    "121": "B49",
-    "122": "B50",
-    "123": "B51",
-    "124": "B52",
-    "125": "B53",
-    "126": "B54",
-    "127": "B55",
-    "128": "B56",
-    "129": "B57",
-    "130": "B58",
-    "131": "B59",
-    "132": "B60",
-    "133": "B61",
-    "134": "B62",
-    "135": "B63",
-    "136": "B64",
-    "137": "B65",
-    "138": "B66",
-    "139": "B67",
-    "140": "B68",
-    "141": "B69",
-    "142": "B70",
-    "143": "B71",
-    "144": "B72",
-    "145": "B73",
-    "146": "C01",
-    "147": "C02",
-    "148": "C03",
-    "149": "C04",
-    "150": "C05",
-    "151": "C06",
-    "152": "C07",
-    "153": "C08",
-    "154": "C09",
-    "155": "C10",
-    "156": "C11",
-    "157": "C12",
-    "158": "C13",
-    "159": "C14",
-    "160": "C15",
-    "161": "C16",
-    "162": "C17",
-    "163": "C18",
-    "164": "C19",
-    "165": "C20",
-    "166": "C21",
-    "167": "C22",
-    "168": "C23",
-    "169": "C24",
-    "170": "C25",
-    "171": "C26",
-    "172": "C27",
-    "173": "C28",
-    "174": "C29",
-    "175": "C30",
-    "176": "C31",
-    "177": "C32",
-    "178": "C33",
-    "179": "C34",
-    "180": "C35",
-    "181": "C36",
-    "182": "C37",
-    "183": "C38",
-    "184": "C39",
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-    "188": "C43",
-    "189": "C44",
-    "190": "C45",
-    "191": "C46",
-    "192": "C47",
-    "193": "C48",
-    "194": "C49",
-    "195": "C50",
-    "196": "C51",
-    "197": "C52",
-    "198": "C53",
-    "199": "C54",
-    "200": "C55",
-    "201": "C56",
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-    "211": "C66",
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   },
   "num_labels": 656,
   "classification_type": "CPC",
-  "description": "Cooperative Patent Classification (CPC) labels for patent classification"
 }

 {
   "id2label": {
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   "num_labels": 656,
   "classification_type": "CPC",
+  "description": "Real Cooperative Patent Classification (CPC) labels from PatentBERT training data"
 }