Upload TFDistilBertForSequenceClassification

README.md

@@ -1,131 +1,48 @@
 ---
-language: en
 license: apache-2.0
-datasets:
-  - amazon_reviews_multi
-model-index:
-  - name: distilbert-base-uncased-finetuned-amazon-reviews
-    results:
-      - task:
-          type: text-classification
-          name: Text Classification
-        dataset:
-          name: amazon_reviews_multi
-          type: amazon_reviews_multi22
-          split: test
-        metrics:
-          - type: accuracy
-            value: .85
-            name: Accuracy
-            
-          - type: loss
-            value: 0.1
-            name: loss
-
 tags:
 - generated_from_keras_callback
-
-pipeline_tag: text-classification
+model-index:
+- name: distilbert-base-uncased-finetuned-amazon-reviews
+  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. -->
 
-# Model Card for distilbert-base-uncased-finetuned-amazon-reviews
-
-
-#  Table of Contents
-
-- [Model Card for distilbert-base-uncased-finetuned-amazon-reviews](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Model Details](#model-details)
-- [Uses](#uses)
-- [Training Details](#training-details)
-- [Evaluation](#evaluation)
-- [Framework versions](#framework-versions)
-
-
-# Model Details
-
-## Model Description
-
-<!-- Provide a longer summary of what this model is/does. -->
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi) dataset.
-This model reaches an accuracy of xxx on the dev set.
-
-- **Model type:** Language model
-- **Language(s) (NLP):** en
-- **License:** apache-2.0
-- **Parent Model:** For more details about DistilBERT, check out [this model card](https://huggingface.co/distilbert-base-uncased).
-- **Resources for more information:**
-    - [Model Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification)
-
-
-# Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-```
-from transformers import DistilBertTokenizer, TFDistilBertModel
-
-checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
-tokenizer = DistilBertTokenizer.from_pretrained(checkpoint)
-model = TFDistilBertModel.from_pretrained(checkpoint)
-text = "xxxxxxxxxxxxxxxxxxxxxxxxxx"
-encoded_input = tokenizer(text, return_tensors="tf")
-output = model(encoded_input)
-
-
-```
-
-
-
-# Training Details
-
-## Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-train data [amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)
-
- 
-# Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+# distilbert-base-uncased-finetuned-amazon-reviews
 
-## Testing Data, Factors & Metrics
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
 
-### Testing Data
 
-<!-- This should link to a Data Card if possible. -->
+## Model description
 
-[amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)
+More information needed
 
+## Intended uses & limitations
 
-### Factors
+More information needed
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## Training and evaluation data
 
-acc
-f1
-precision
+More information needed
 
-### Metrics
+## Training procedure
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### Training hyperparameters
 
-metric1
+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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 18750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
+- training_precision: float32
 
-## Results 
+### Training results
 
-result1
 
 
-# Framework versions
+### Framework versions
 
 - Transformers 4.26.1
 - TensorFlow 2.11.0
 - Datasets 2.1.0
-- Tokenizers 0.13.2
+- Tokenizers 0.13.2

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