Upload 8 files
Browse files- .gitattributes +35 -0
- README.md +59 -0
- config.json +32 -0
- special_tokens_map.json +15 -0
- spiece.model +3 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +57 -0
.gitattributes
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README.md
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---
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license: apache-2.0
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base_model: albert-base-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: Kemasu/albert-base-v2-finetuned-squad
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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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# Kemasu/albert-base-v2-finetuned-squad
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.6529
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- Train End Logits Accuracy: 0.8116
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- Train Start Logits Accuracy: 0.7711
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- Validation Loss: 0.8559
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- Validation End Logits Accuracy: 0.7604
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- Validation Start Logits Accuracy: 0.7262
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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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11078, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
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|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
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| 0.9838 | 0.7333 | 0.6924 | 0.8735 | 0.7515 | 0.7178 | 0 |
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| 0.6529 | 0.8116 | 0.7711 | 0.8559 | 0.7604 | 0.7262 | 1 |
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### Framework versions
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- Transformers 4.38.2
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- TensorFlow 2.15.0
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "albert-base-v2",
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"architectures": [
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"AlbertForQuestionAnswering"
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],
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"attention_probs_dropout_prob": 0,
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"bos_token_id": 2,
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"classifier_dropout_prob": 0.1,
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"down_scale_factor": 1,
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"embedding_size": 128,
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"eos_token_id": 3,
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"gap_size": 0,
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"hidden_act": "gelu_new",
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"hidden_dropout_prob": 0,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"inner_group_num": 1,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "albert",
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"net_structure_type": 0,
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"num_attention_heads": 12,
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"num_hidden_groups": 1,
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"num_hidden_layers": 12,
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"num_memory_blocks": 0,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.38.2",
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"type_vocab_size": 2,
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"vocab_size": 30000
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}
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special_tokens_map.json
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{
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"unk_token": "<unk>"
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}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:fefb02b667a6c5c2fe27602d28e5fb3428f66ab89c7d6f388e7c8d44a02d0336
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size 760289
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:0ef268a4c88cf2fb7a2fc90ce52d8b43a288f5303e9f8893ff19aab14806f6df
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size 44417688
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tokenizer.json
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tokenizer_config.json
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}
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