Training in progress epoch 0

README.md

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+---
+license: mit
+base_model: microsoft/deberta-base
+tags:
+- generated_from_keras_callback
+model-index:
+- name: vladjr/deberta-teste
+  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/deberta-teste
+
+This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 2.0764
+- Validation Loss: 0.2430
+- Train Accuracy: 0.9405
+- 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': 2100, '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 |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 2.0764     | 0.2430          | 0.9405         | 0     |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- TensorFlow 2.13.0
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

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config.json

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+{
+  "_name_or_path": "microsoft/deberta-base",
+  "architectures": [
+    "DebertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "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",
+    "41": "Small and Medium-sized Enterprises",
+    "42": "Subject Matter Expert"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "Alternative Investment": 0,
+    "Annual Review": 4,
+    "Applicant Tracking System": 5,
+    "Appreciative Inquiry": 1,
+    "Artificial Intelligence": 2,
+    "Automated Trading System": 6,
+    "Call to Action": 16,
+    "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": 41,
+    "Subject Matter Expert": 42
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "c2p",
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+  "position_biased_input": false,
+  "relative_attention": true,
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 0,
+  "vocab_size": 50265
+}

special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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+  "vocab_type": "gpt2"
+}