Training in progress epoch 0
Browse files- README.md +54 -0
- config.json +87 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +13 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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tags:
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- generated_from_keras_callback
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model-index:
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- name: inpreet/model_categorization
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# inpreet/model_categorization
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 2.4855
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- Validation Loss: 2.0873
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- Train Accuracy: 0.4226
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- Epoch: 0
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7495, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Accuracy | Epoch |
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|:----------:|:---------------:|:--------------:|:-----:|
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| 2.4855 | 2.0873 | 0.4226 | 0 |
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### Framework versions
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- Transformers 4.30.0
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- TensorFlow 2.12.0
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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config.json
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{
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"_name_or_path": "distilbert-base-uncased",
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"id2label": {
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"0": "Agriculture & Allied Sectors",
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"1": "Animation",
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"2": "Astrology",
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"3": "Autos & Vehicles",
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"4": "Beauty",
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"5": "Blogs and Travel",
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"6": "Comedy",
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"7": "DIY",
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"8": "Education",
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"9": "Entertainment",
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"10": "Events",
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"11": "Fashion & Style",
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"12": "Finance",
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"13": "Food & Drinks",
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"14": "Gaming",
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"15": "Health & Fitness",
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"16": "Infotainment",
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"17": "Kids & Animation",
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"18": "Miscellaneous",
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"19": "Motivational",
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"20": "Movie & Shows",
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"21": "Music",
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"22": "News & Politics",
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"23": "Non profits",
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"24": "People & Culture",
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"25": "Pets & Animals",
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"26": "Religious Content",
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"27": "Science & Technology",
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"28": "Sports",
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"29": "Travel & Leisure"
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},
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"initializer_range": 0.02,
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"label2id": {
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"Agriculture & Allied Sectors": 0,
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"Animation": 1,
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"Astrology": 2,
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"Autos & Vehicles": 3,
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"Beauty": 4,
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"Blogs and Travel": 5,
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"Comedy": 6,
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"DIY": 7,
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"Education": 8,
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"Entertainment": 9,
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"Events": 10,
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"Fashion & Style": 11,
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"Finance": 12,
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"Food & Drinks": 13,
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"Gaming": 14,
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"Health & Fitness": 15,
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"Infotainment": 16,
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"Kids & Animation": 17,
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"Miscellaneous": 18,
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"Motivational": 19,
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"Movie & Shows": 20,
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"Music": 21,
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"News & Politics": 22,
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"Non profits": 23,
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"People & Culture": 24,
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"Pets & Animals": 25,
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"Religious Content": 26,
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"Science & Technology": 27,
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"Sports": 28,
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"Travel & Leisure": 29
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"transformers_version": "4.30.0",
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"vocab_size": 30522
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:1af396f417fd64247799de51c0c7b96adb590239859258b1742cab2fa2b84257
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size 268037824
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tokenizer.json
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tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "DistilBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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