RafaelMayer
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Training in progress epoch 1
Browse files- README.md +60 -0
- config.json +37 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.txt +0 -0
README.md
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---
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base_model: mrm8488/electricidad-base-discriminator
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tags:
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- generated_from_keras_callback
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model-index:
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- name: RafaelMayer/electra-copec-2
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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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# RafaelMayer/electra-copec-2
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This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.7303
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- Validation Loss: 0.6874
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- Train Accuracy: 0.8824
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- Train Precision: [0.75 0.92307692]
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- Train Precision W: 0.8824
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- Train Recall: [0.75 0.92307692]
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- Train Recall W: 0.8824
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- Train F1: [0.75 0.92307692]
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- Train F1 W: 0.8824
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- Epoch: 1
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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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, '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 | Train Precision | Train Precision W | Train Recall | Train Recall W | Train F1 | Train F1 W | Epoch |
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|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:-----------------------:|:--------------:|:-----------------------:|:----------:|:-----:|
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| 0.7303 | 0.6874 | 0.8824 | [0.75 0.92307692] | 0.8824 | [0.75 0.92307692] | 0.8824 | [0.75 0.92307692] | 0.8824 | 1 |
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### Framework versions
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- Transformers 4.32.1
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- TensorFlow 2.12.0
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- Datasets 2.14.4
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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": "mrm8488/electricidad-base-discriminator",
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"architectures": [
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"ElectraForSequenceClassification"
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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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"embedding_size": 768,
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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": "False",
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"1": "True"
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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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"False": 0,
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"True": 1
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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": "electra",
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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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"position_embedding_type": "absolute",
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"summary_activation": "gelu",
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"summary_last_dropout": 0.1,
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"summary_type": "first",
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"summary_use_proj": true,
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"transformers_version": "4.32.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 31002
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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:fcb6a93eea46bf34e5361811b40f88464278c5b440b46b2d6ca0b3002bf5e953
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size 439699664
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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_basic_tokenize": true,
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"do_lower_case": true,
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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": "ElectraTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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