Training in progress epoch 0

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_keras_callback
+model-index:
+- name: vladjr/bert-full
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# vladjr/bert-full
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 2.4206
+- Validation Loss: 0.9155
+- Train Accuracy: 0.9353
+- Epoch: 0
+
+## 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:
+- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2900, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Accuracy | Epoch |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 2.4206     | 0.9155          | 0.9353         | 0     |
+
+
+### Framework versions
+
+- Transformers 4.34.1
+- TensorFlow 2.13.0
+- Datasets 2.14.5
+- Tokenizers 0.14.1

config.json

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+{
+  "_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": "Alternative Investment",
+    "1": "Appreciative Inquiry",
+    "2": "Artificial Intelligence",
+    "3": "Accounts Receivable",
+    "4": "Annual Review",
+    "5": "Applicant Tracking System",
+    "6": "Automated Trading System",
+    "7": "Career Advancement",
+    "8": "Chartered Accountant",
+    "9": "Customer Acquisition",
+    "10": "Career Pathing",
+    "11": "Commercial Paper",
+    "12": "Cost Per Mille",
+    "13": "Cost Per Million",
+    "14": "Credit Risk Management",
+    "15": "Customer Relationship Management",
+    "16": "Call to Action",
+    "17": "Commodity Trading Advisor",
+    "18": "Diversity, Equity, and Inclusion",
+    "19": "Dividend Equity Income",
+    "20": "Earnings Report",
+    "21": "Employee Relations",
+    "22": "Email Verification Process",
+    "23": "Employee Value Proposition",
+    "24": "Executive Vice President",
+    "25": "Marketing Automation",
+    "26": "Mergers & Acquisitions",
+    "27": "Management BuyOut",
+    "28": "Management By Objectives",
+    "29": "Performance Management",
+    "30": "Portfolio Manager",
+    "31": "Product Marketing",
+    "32": "Performance Review",
+    "33": "Public Relations",
+    "34": "Remarketing Audience",
+    "35": "Risk Assessment",
+    "36": "Return on Investment",
+    "37": "Return on Involvement",
+    "38": "Recruitment Process Outsourcing",
+    "39": "Request for Purchase Order",
+    "40": "Small and Medium-sized Enterprises",
+    "41": "Subject Matter Expert",
+    "42": "Annual Report ",
+    "43": "Canadian Dollar ",
+    "44": "computer-aided design ",
+    "45": "direct deposit ",
+    "46": "double booking ",
+    "47": "due date ",
+    "48": "millimeters ",
+    "49": "millions ",
+    "50": "message ",
+    "51": "monosodium glutamate ",
+    "52": "senior systems administrator ",
+    "53": "Social Security Administration ",
+    "54": "Workforce Now ",
+    "55": "World Federation of Neurology ",
+    "56": "you only live once ",
+    "57": "you only look once "
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Accounts Receivable": 3,
+    "Alternative Investment": 0,
+    "Annual Report ": 42,
+    "Annual Review": 4,
+    "Applicant Tracking System": 5,
+    "Appreciative Inquiry": 1,
+    "Artificial Intelligence": 2,
+    "Automated Trading System": 6,
+    "Call to Action": 16,
+    "Canadian Dollar ": 43,
+    "Career Advancement": 7,
+    "Career Pathing": 10,
+    "Chartered Accountant": 8,
+    "Commercial Paper": 11,
+    "Commodity Trading Advisor": 17,
+    "Cost Per Mille": 12,
+    "Cost Per Million": 13,
+    "Credit Risk Management": 14,
+    "Customer Acquisition": 9,
+    "Customer Relationship Management": 15,
+    "Diversity, Equity, and Inclusion": 18,
+    "Dividend Equity Income": 19,
+    "Earnings Report": 20,
+    "Email Verification Process": 22,
+    "Employee Relations": 21,
+    "Employee Value Proposition": 23,
+    "Executive Vice President": 24,
+    "Management BuyOut": 27,
+    "Management By Objectives": 28,
+    "Marketing Automation": 25,
+    "Mergers & Acquisitions": 26,
+    "Performance Management": 29,
+    "Performance Review": 32,
+    "Portfolio Manager": 30,
+    "Product Marketing": 31,
+    "Public Relations": 33,
+    "Recruitment Process Outsourcing": 38,
+    "Remarketing Audience": 34,
+    "Request for Purchase Order": 39,
+    "Return on Investment": 36,
+    "Return on Involvement": 37,
+    "Risk Assessment": 35,
+    "Small and Medium-sized Enterprises": 40,
+    "Social Security Administration ": 53,
+    "Subject Matter Expert": 41,
+    "Workforce Now ": 54,
+    "World Federation of Neurology ": 55,
+    "computer-aided design ": 44,
+    "direct deposit ": 45,
+    "double booking ": 46,
+    "due date ": 47,
+    "message ": 50,
+    "millimeters ": 48,
+    "millions ": 49,
+    "monosodium glutamate ": 51,
+    "senior systems administrator ": 52,
+    "you only live once ": 56,
+    "you only look once ": 57
+  },
+  "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",
+  "transformers_version": "4.34.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tf_model.h5

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+oid sha256:d4dca04e270ec09ef8e74cf0671206a399d9c154765150032e11de6bb2313858
+size 438395384

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "single_word": false,
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "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]"
+}