badokorach
commited on
Commit
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Parent(s):
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Training in progress epoch 0
Browse files- README.md +54 -0
- config.json +32 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +58 -0
- vocab.txt +0 -0
README.md
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---
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license: mit
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base_model: csarron/mobilebert-uncased-squad-v2
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tags:
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- generated_from_keras_callback
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model-index:
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- name: badokorach/mobilebert-uncased-finetuned-squad-031223
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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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# badokorach/mobilebert-uncased-finetuned-squad-031223
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This model is a fine-tuned version of [csarron/mobilebert-uncased-squad-v2](https://huggingface.co/csarron/mobilebert-uncased-squad-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.6078
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- Validation Loss: 0.0
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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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 33195, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
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- training_precision: mixed_float16
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### Training results
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| Train Loss | Validation Loss | Epoch |
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|:----------:|:---------------:|:-----:|
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| 0.6078 | 0.0 | 0 |
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### Framework versions
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- Transformers 4.35.2
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- TensorFlow 2.14.0
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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config.json
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{
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"_name_or_path": "csarron/mobilebert-uncased-squad-v2",
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"architectures": [
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"MobileBertForQuestionAnswering"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_activation": false,
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"classifier_dropout": null,
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"embedding_size": 128,
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"hidden_act": "relu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 512,
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"intra_bottleneck_size": 128,
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"key_query_shared_bottleneck": true,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "mobilebert",
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"normalization_type": "no_norm",
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"num_attention_heads": 4,
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"num_feedforward_networks": 4,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"transformers_version": "4.35.2",
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"trigram_input": true,
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"true_hidden_size": 128,
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"type_vocab_size": 2,
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"use_bottleneck": true,
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"use_bottleneck_attention": false,
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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:fcc9f271900aece2bf25c7173b192c751347548667f3c6852271416e3671eece
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size 99822192
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tokenizer.json
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"100": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"101": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"102": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"103": {
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"content": "[MASK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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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_basic_tokenize": true,
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"do_lower_case": true,
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"full_tokenizer_file": null,
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"never_split": null,
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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": "MobileBertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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