Upload TFDistilBertForQuestionAnswering

README.md

@@ -1,70 +1,54 @@
 ---
-datasets:
-- squad
 license: apache-2.0
-tags: 
+base_model: distilbert-base-cased
+tags:
 - generated_from_keras_callback
-metrics: 
-- f1
-model-index: 
+model-index:
 - name: transformers-qa
-  results: 
-  - task: 
-      name: "Question Answering"
-      type: question-answering
-    dataset: 
-      type: squad
-      name: SQuAD
-      args: en
-    metrics:
-      []
-widget: 
-  - context: "Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error."
+  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. -->
 
-# Question Answering with Hugging Face Transformers and Keras 🤗❤️
+# transformers-qa
 
-This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on SQuAD dataset.
+This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.9300
-- Validation Loss: 1.1437
-- Epoch: 1
+- Train Loss: 1.5435
+- Validation Loss: 1.1638
+- Epoch: 0
 
 ## Model description
 
-Question answering model based on distilbert-base-cased, trained with 🤗Transformers + ❤️Keras.
+More information needed
 
 ## Intended uses & limitations
 
-This model is trained for Question Answering tutorial for Keras.io.
+More information needed
 
 ## Training and evaluation data
 
-It is trained on [SQuAD](https://huggingface.co/datasets/squad) question answering dataset. ⁉️ 
+More information needed
 
 ## Training procedure
 
-Find the notebook in Keras Examples [here](https://keras.io/examples/nlp/question_answering/). ❤️
-
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, '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': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
 - training_precision: mixed_float16
 
 ### Training results
 
 | Train Loss | Validation Loss | Epoch |
 |:----------:|:---------------:|:-----:|
-| 1.5145     | 1.1500          | 0     |
-| 0.9300     | 1.1437          | 1     |
- 
+| 1.5435     | 1.1638          | 0     |
+
 
 ### Framework versions
 
-- Transformers 4.16.0.dev0
-- TensorFlow 2.6.0
-- Datasets 1.16.2.dev0
-- Tokenizers 0.10.3
+- Transformers 4.32.0.dev0
+- TensorFlow 2.12.0
+- Datasets 2.14.4
+- Tokenizers 0.13.3

config.json

@@ -19,6 +19,6 @@
   "seq_classif_dropout": 0.2,
   "sinusoidal_pos_embds": false,
   "tie_weights_": true,
-  "transformers_version": "4.16.0.dev0",
+  "transformers_version": "4.32.0.dev0",
   "vocab_size": 28996
 }

tf_model.h5

@@ -1,3 +1,3 @@
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+size 260895720