matthewleechen/tech_classes_multilabel_classifier

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,77 @@
+---
+library_name: transformers
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+model-index:
+- name: multiclass-classifier-patents
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# multiclass-classifier-patents
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0067
+- F1 Micro: 0.7001
+- Precision Micro: 0.8337
+- Recall Micro: 0.6034
+- Exact Match F1: 0.5296
+- Exact Match Precision: 0.5296
+- Exact Match Recall: 0.5296
+- Any Match F1: 0.9079
+- Any Match Precision: 0.9079
+- Any Match Recall: 0.9079
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | F1 Micro | Precision Micro | Recall Micro | Exact Match F1 | Exact Match Precision | Exact Match Recall | Any Match F1 | Any Match Precision | Any Match Recall |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:|:---------------------:|:------------------:|:------------:|:-------------------:|:----------------:|
+| 0.01          | 1.0   | 1292  | 0.0083          | 0.5977   | 0.8265          | 0.4681       | 0.4300         | 0.4300                | 0.4300             | 0.7675       | 0.7675              | 0.7675           |
+| 0.0077        | 2.0   | 2584  | 0.0074          | 0.6595   | 0.8326          | 0.5460       | 0.4879         | 0.4879                | 0.4879             | 0.8636       | 0.8636              | 0.8636           |
+| 0.007         | 3.0   | 3876  | 0.0071          | 0.6829   | 0.8173          | 0.5864       | 0.5035         | 0.5035                | 0.5035             | 0.8958       | 0.8958              | 0.8958           |
+| 0.0063        | 4.0   | 5168  | 0.0069          | 0.6883   | 0.8317          | 0.5871       | 0.5140         | 0.5140                | 0.5140             | 0.8956       | 0.8956              | 0.8956           |
+| 0.0058        | 5.0   | 6460  | 0.0068          | 0.6957   | 0.8337          | 0.5969       | 0.5182         | 0.5182                | 0.5182             | 0.9058       | 0.9058              | 0.9058           |
+| 0.0053        | 6.0   | 7752  | 0.0069          | 0.6999   | 0.8366          | 0.6017       | 0.5271         | 0.5271                | 0.5271             | 0.9082       | 0.9082              | 0.9082           |
+| 0.0048        | 7.0   | 9044  | 0.0069          | 0.7046   | 0.8159          | 0.6201       | 0.5225         | 0.5225                | 0.5225             | 0.9185       | 0.9185              | 0.9185           |
+| 0.0046        | 8.0   | 10336 | 0.0069          | 0.7069   | 0.8100          | 0.6271       | 0.5241         | 0.5241                | 0.5241             | 0.9196       | 0.9196              | 0.9196           |
+| 0.0042        | 9.0   | 11628 | 0.0070          | 0.7064   | 0.8208          | 0.6200       | 0.5282         | 0.5282                | 0.5282             | 0.9174       | 0.9174              | 0.9174           |
+| 0.004         | 10.0  | 12920 | 0.0070          | 0.7064   | 0.8184          | 0.6214       | 0.5276         | 0.5276                | 0.5276             | 0.9177       | 0.9177              | 0.9177           |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.0.1+cu117
+- Datasets 3.0.1
+- Tokenizers 0.20.3

config.json

@@ -0,0 +1,325 @@
+{
+  "_name_or_path": "FacebookAI/xlm-roberta-large",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "Acids and salts, etc",
+    "1": "Acids, alkalis, etc",
+    "2": "Advertising",
+    "3": "Aeronautics",
+    "4": "Agricultural appliances, farmyard",
+    "5": "Agricultural appliances, for treatment of land, etc.",
+    "6": "Air and gas engines",
+    "7": "Air and gases, compressing, etc",
+    "8": "Ammunition",
+    "9": "Animal powered engines",
+    "10": "Artists' instruments",
+    "11": "Bearings, etc.",
+    "12": "Bells, etc",
+    "13": "Beverages",
+    "14": "Bleaching, etc.",
+    "15": "Books",
+    "16": "Boots, etc",
+    "17": "Boxes, etc",
+    "18": "Brushing, etc",
+    "19": "Buildings",
+    "20": "Casks",
+    "21": "Cements",
+    "22": "Centrifugal drying",
+    "23": "Chains",
+    "24": "Chimneys",
+    "25": "Closets",
+    "26": "Coin-feed apparatus",
+    "27": "Cooking, etc",
+    "28": "Cooling",
+    "29": "Cutlery",
+    "30": "Cutting",
+    "31": "Distilling",
+    "32": "Drains",
+    "33": "Drying",
+    "34": "Dynamo electric generators",
+    "35": "Electric lamps",
+    "36": "Electric telegraphs",
+    "37": "Electricity conducting",
+    "38": "Electricity measuring",
+    "39": "Electricity regulating",
+    "40": "Electrolysis",
+    "41": "Fabrics, dressing",
+    "42": "Fastenings, dress",
+    "43": "Fastenings, lock",
+    "44": "Fencing",
+    "45": "Filtering",
+    "46": "Fire extinction",
+    "47": "Fish",
+    "48": "Food",
+    "49": "Fuel, manufacture",
+    "50": "Furnaces",
+    "51": "Furniture",
+    "52": "Galvanic batteries",
+    "53": "Gas distribution",
+    "54": "Gas manufacture",
+    "55": "Glass",
+    "56": "Governors",
+    "57": "Grain",
+    "58": "Grinding and crushing",
+    "59": "Grinding or abrading",
+    "60": "Hand tools",
+    "61": "Harness",
+    "62": "Hats",
+    "63": "Heating",
+    "64": "Hinges",
+    "65": "Hollow-ware",
+    "66": "Horse-shoes",
+    "67": "Hydraulic engineering",
+    "68": "Hydraulic machinery",
+    "69": "India-rubber",
+    "70": "Injectors",
+    "71": "Iron",
+    "72": "Labels",
+    "73": "Lace-making",
+    "74": "Lamps",
+    "75": "Leather",
+    "76": "Life-saving",
+    "77": "Lifting",
+    "78": "Locomotives",
+    "79": "Mechanism",
+    "80": "Medicine",
+    "81": "Metals and alloys",
+    "82": "Metals, Cutting, etc",
+    "83": "Milking",
+    "84": "Mining",
+    "85": "Mixing",
+    "86": "Moulding",
+    "87": "Music",
+    "88": "Nails",
+    "89": "Non-metallic elements",
+    "90": "Oils",
+    "91": "Ordnance",
+    "92": "Ornamenting",
+    "93": "Packing",
+    "94": "Paints",
+    "95": "Paper",
+    "96": "Philosophical instruments",
+    "97": "Photography",
+    "98": "Pipes",
+    "99": "Printing, letterpress",
+    "100": "Printing, other",
+    "101": "Pumps",
+    "102": "Railway etc. vehicles",
+    "103": "Railway signals",
+    "104": "Railways, etc.",
+    "105": "Registering",
+    "106": "Road vehicles",
+    "107": "Roads",
+    "108": "Ropes",
+    "109": "Rotary engines",
+    "110": "Sewage",
+    "111": "Sewing",
+    "112": "Ships, Div I",
+    "113": "Ships, Div II",
+    "114": "Ships, Div III",
+    "115": "Shop accessories",
+    "116": "Sifting",
+    "117": "Signalling",
+    "118": "Small arms",
+    "119": "Spinning",
+    "120": "Starch",
+    "121": "Steam engines",
+    "122": "Steam generators",
+    "123": "Stone",
+    "124": "Stoppering",
+    "125": "Stoves",
+    "126": "Sugar",
+    "127": "Table articles",
+    "128": "Tea",
+    "129": "Tobacco",
+    "130": "Toilet",
+    "131": "Toys",
+    "132": "Trunks",
+    "133": "Umbrellas",
+    "134": "Valves",
+    "135": "Velocipedes",
+    "136": "Ventilation",
+    "137": "Washing",
+    "138": "Watches",
+    "139": "Waterproof fabrics",
+    "140": "Wearing apparel",
+    "141": "Weaving",
+    "142": "Weighing apparatus",
+    "143": "Wheels",
+    "144": "Wood",
+    "145": "Writing instruments"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "Acids and salts, etc": 0,
+    "Acids, alkalis, etc": 1,
+    "Advertising": 2,
+    "Aeronautics": 3,
+    "Agricultural appliances, farmyard": 4,
+    "Agricultural appliances, for treatment of land, etc.": 5,
+    "Air and gas engines": 6,
+    "Air and gases, compressing, etc": 7,
+    "Ammunition": 8,
+    "Animal powered engines": 9,
+    "Artists' instruments": 10,
+    "Bearings, etc.": 11,
+    "Bells, etc": 12,
+    "Beverages": 13,
+    "Bleaching, etc.": 14,
+    "Books": 15,
+    "Boots, etc": 16,
+    "Boxes, etc": 17,
+    "Brushing, etc": 18,
+    "Buildings": 19,
+    "Casks": 20,
+    "Cements": 21,
+    "Centrifugal drying": 22,
+    "Chains": 23,
+    "Chimneys": 24,
+    "Closets": 25,
+    "Coin-feed apparatus": 26,
+    "Cooking, etc": 27,
+    "Cooling": 28,
+    "Cutlery": 29,
+    "Cutting": 30,
+    "Distilling": 31,
+    "Drains": 32,
+    "Drying": 33,
+    "Dynamo electric generators": 34,
+    "Electric lamps": 35,
+    "Electric telegraphs": 36,
+    "Electricity conducting": 37,
+    "Electricity measuring": 38,
+    "Electricity regulating": 39,
+    "Electrolysis": 40,
+    "Fabrics, dressing": 41,
+    "Fastenings, dress": 42,
+    "Fastenings, lock": 43,
+    "Fencing": 44,
+    "Filtering": 45,
+    "Fire extinction": 46,
+    "Fish": 47,
+    "Food": 48,
+    "Fuel, manufacture": 49,
+    "Furnaces": 50,
+    "Furniture": 51,
+    "Galvanic batteries": 52,
+    "Gas distribution": 53,
+    "Gas manufacture": 54,
+    "Glass": 55,
+    "Governors": 56,
+    "Grain": 57,
+    "Grinding and crushing": 58,
+    "Grinding or abrading": 59,
+    "Hand tools": 60,
+    "Harness": 61,
+    "Hats": 62,
+    "Heating": 63,
+    "Hinges": 64,
+    "Hollow-ware": 65,
+    "Horse-shoes": 66,
+    "Hydraulic engineering": 67,
+    "Hydraulic machinery": 68,
+    "India-rubber": 69,
+    "Injectors": 70,
+    "Iron": 71,
+    "Labels": 72,
+    "Lace-making": 73,
+    "Lamps": 74,
+    "Leather": 75,
+    "Life-saving": 76,
+    "Lifting": 77,
+    "Locomotives": 78,
+    "Mechanism": 79,
+    "Medicine": 80,
+    "Metals and alloys": 81,
+    "Metals, Cutting, etc": 82,
+    "Milking": 83,
+    "Mining": 84,
+    "Mixing": 85,
+    "Moulding": 86,
+    "Music": 87,
+    "Nails": 88,
+    "Non-metallic elements": 89,
+    "Oils": 90,
+    "Ordnance": 91,
+    "Ornamenting": 92,
+    "Packing": 93,
+    "Paints": 94,
+    "Paper": 95,
+    "Philosophical instruments": 96,
+    "Photography": 97,
+    "Pipes": 98,
+    "Printing, letterpress": 99,
+    "Printing, other": 100,
+    "Pumps": 101,
+    "Railway etc. vehicles": 102,
+    "Railway signals": 103,
+    "Railways, etc.": 104,
+    "Registering": 105,
+    "Road vehicles": 106,
+    "Roads": 107,
+    "Ropes": 108,
+    "Rotary engines": 109,
+    "Sewage": 110,
+    "Sewing": 111,
+    "Ships, Div I": 112,
+    "Ships, Div II": 113,
+    "Ships, Div III": 114,
+    "Shop accessories": 115,
+    "Sifting": 116,
+    "Signalling": 117,
+    "Small arms": 118,
+    "Spinning": 119,
+    "Starch": 120,
+    "Steam engines": 121,
+    "Steam generators": 122,
+    "Stone": 123,
+    "Stoppering": 124,
+    "Stoves": 125,
+    "Sugar": 126,
+    "Table articles": 127,
+    "Tea": 128,
+    "Tobacco": 129,
+    "Toilet": 130,
+    "Toys": 131,
+    "Trunks": 132,
+    "Umbrellas": 133,
+    "Valves": 134,
+    "Velocipedes": 135,
+    "Ventilation": 136,
+    "Washing": 137,
+    "Watches": 138,
+    "Waterproof fabrics": 139,
+    "Wearing apparel": 140,
+    "Weaving": 141,
+    "Weighing apparatus": 142,
+    "Wheels": 143,
+    "Wood": 144,
+    "Writing instruments": 145
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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tokenizer_config.json

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