Instructions to use LongRiver/transformer_QAVi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LongRiver/transformer_QAVi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="LongRiver/transformer_QAVi")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("LongRiver/transformer_QAVi") model = AutoModelForQuestionAnswering.from_pretrained("LongRiver/transformer_QAVi") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files
README.md
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss:
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- Train End Logits Accuracy: 0.
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- Train Start Logits Accuracy: 0.
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- Validation Loss: 2.
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- Validation End Logits Accuracy: 0.
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- Validation Start Logits Accuracy: 0.
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- Epoch:
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## Model description
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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':
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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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| 2.
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| 1.7583 | 0.5813 | 0.5573 | 2.0446 | 0.5277 | 0.4928 | 1 |
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### Framework versions
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- Transformers 4.
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- TensorFlow 2.15.0
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- Datasets 2.
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- Tokenizers 0.
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 2.3715
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- Train End Logits Accuracy: 0.5054
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- Train Start Logits Accuracy: 0.4994
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- Validation Loss: 2.0790
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- Validation End Logits Accuracy: 0.5204
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- Validation Start Logits Accuracy: 0.4818
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- Epoch: 0
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## Model description
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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': 45240, '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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| 2.3715 | 0.5054 | 0.4994 | 2.0790 | 0.5204 | 0.4818 | 0 |
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### Framework versions
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- Transformers 4.39.3
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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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"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.
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"vocab_size": 28996
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}
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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": "4.39.3",
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"vocab_size": 28996
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}
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logs/train/events.out.tfevents.1714957848.f7da57357404.85.0.v2
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size 1441432
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logs/validation/events.out.tfevents.1714958638.f7da57357404.85.1.v2
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tf_model.h5
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size 260895720
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tokenizer_config.json
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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":
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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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"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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