Update README.md

README.md

@@ -1,48 +1,131 @@
 ---
+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
-model-index:
-- name: distilbert-base-uncased-finetuned-amazon-reviews
-  results: []
+
+pipeline_tag: text-classification
 ---
 
-<!-- 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. -->
 
-# distilbert-base-uncased-finetuned-amazon-reviews
+# 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. -->
 
-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, Factors & Metrics
 
+### Testing Data
 
-## Model description
+<!-- This should link to a Data Card if possible. -->
 
-More information needed
+[amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)
 
-## Intended uses & limitations
 
-More information needed
+### Factors
 
-## Training and evaluation data
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-More information needed
+acc
+f1
+precision
 
-## Training procedure
+### Metrics
 
-### Training hyperparameters
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-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
+metric1
 
-### Training results
+## 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