Training in progress epoch 0

README.md

@@ -4,55 +4,51 @@ base_model: deepset/roberta-base-squad2
 tags:
 - generated_from_keras_callback
 model-index:
-- name: Kiran2004/my_qa_model
+- name: Kiran2004/Roberta_qca_sample
   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. -->
 
-# Kiran2004/my_qa_model
-
-This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 
+# Kiran2004/Roberta_qca_sample
 
+This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 3.4107
-- Validation Loss: 9.9990
-- Epoch: 1
+- Train Loss: 0.4681
+- Validation Loss: 0.0034
+- Epoch: 0
+
+## Model description
 
-## Usage
+More information needed
 
-### In Transformers
-```python
-from transformers import pipeline
+## Intended uses & limitations
 
-model_name = "Kiran2004/Roberta_qca_sample"
+More information needed
 
-question_answerer = pipeline("question-answering", model = model_name)
+## Training and evaluation data
 
-question = "How many programming languages does BLOOM support?"
-context = "BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages."
+More information needed
 
-question_answerer(question=question, context=context)
-```
+## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 8, '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}
+- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 80, '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 | Epoch |
 |:----------:|:---------------:|:-----:|
-| 3.0643     | 9.9990          | 0     |
-| 3.4107     | 9.9990          | 1     |
+| 0.4681     | 0.0034          | 0     |
 
 
 ### Framework versions
 
 - Transformers 4.38.2
 - TensorFlow 2.15.0
-- Datasets 2.18.0
+- Datasets 2.19.0
 - Tokenizers 0.15.2

tf_model.h5

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