Training in progress epoch 0

README.md

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+---
+license: mit
+base_model: xlnet-large-cased
+tags:
+- generated_from_keras_callback
+model-index:
+- name: vedantjumle/xlnet-1
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
+
+# vedantjumle/xlnet-1
+
+This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Train Loss: 5.1007
+- Validation Loss: 4.9565
+- Train Accuracy: 0.0133
+- Epoch: 0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- 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': 6000, '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}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Accuracy | Epoch |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 5.1007     | 4.9565          | 0.0133         | 0     |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- TensorFlow 2.13.0
+- Datasets 2.14.5
+- Tokenizers 0.14.1

added_tokens.json

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config.json

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+{
+  "_name_or_path": "xlnet-large-cased",
+  "architectures": [
+    "XLNetForSequenceClassification"
+  ],
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+  "clamp_len": -1,
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special_tokens_map.json

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