Training in progress epoch 0
Browse files- README.md +58 -0
- config.json +40 -0
- logs/train/events.out.tfevents.1720676383.4714aa4ab9cd.30701.0.v2 +3 -0
- logs/validation/events.out.tfevents.1720676519.4714aa4ab9cd.30701.1.v2 +3 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +55 -0
- vocab.txt +0 -0
README.md
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---
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license: apache-2.0
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base_model: google-bert/bert-base-multilingual-cased
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tags:
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- generated_from_keras_callback
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model-index:
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- name: aadhistii/tsel-finetune-bert-base-multilingual-cased-2k-formal-v2
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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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# aadhistii/tsel-finetune-bert-base-multilingual-cased-2k-formal-v2
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.9754
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- Validation Loss: 0.8980
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- Train Precision: 0.7162
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- Train Recall: 0.4951
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- Train F1: 0.4870
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- Train Accuracy: 0.5824
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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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 940, '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}
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- training_precision: float32
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### Training results
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| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
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|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
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| 0.9754 | 0.8980 | 0.7162 | 0.4951 | 0.4870 | 0.5824 | 0 |
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### Framework versions
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- Transformers 4.42.3
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- TensorFlow 2.15.0
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "google-bert/bert-base-multilingual-cased",
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"architectures": [
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"BertForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "negative",
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"1": "neutral",
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"2": "positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"negative": 0,
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"neutral": 1,
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"positive": 2
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"transformers_version": "4.42.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 119547
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}
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logs/train/events.out.tfevents.1720676383.4714aa4ab9cd.30701.0.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:55f8a7211a929f1c2b7125900c5951f27dc48271ae507effa76b3a60d221df7d
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size 3530253
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logs/validation/events.out.tfevents.1720676519.4714aa4ab9cd.30701.1.v2
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version https://git-lfs.github.com/spec/v1
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oid sha256:e392e4e17a0f242d31c8a0cdb497246366d0a39a1e311bce761b2f327416ed6a
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size 231
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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:d4d9852d066102faa61daac72f368b37ea497a16498dd22312286688b3631b3d
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size 711711004
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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_lower_case": false,
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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": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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