Update README.md

README.md

@@ -10,16 +10,16 @@ model-index:
           type: text-classification
           name: Text Classification
         dataset:
+          type: amazon-reviews-multi
           name: amazon_reviews_multi
-          type: amazon_reviews_multi22
           split: test
         metrics:
           - type: accuracy
-            value: .85
+            value: .80
             name: Accuracy
             
           - type: loss
-            value: 0.1
+            value: 0.5
             name: loss
 
 tags:
@@ -38,7 +38,7 @@ pipeline_tag: text-classification
 - [Table of Contents](#table-of-contents)
 - [Model Details](#model-details)
 - [Uses](#uses)
-- [Training Details](#training-details)
+- [Fine-tuning hyperparameters](#training-details)
 - [Evaluation](#evaluation)
 - [Framework versions](#framework-versions)
 
@@ -61,66 +61,59 @@ This model reaches an accuracy of xxx on the dev set.
 
 # 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. -->
+You can use this model directly with a pipeline for text classification.
 
-## 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
+from transformers import pipeline
 
 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)
+classifier = pipeline("text-classification", model=checkpoint)
+classifier(["Replace me by any text you'd like."])
+```
+and in TensorFlow:
+```
+from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
 
+checkpoint = "amir7d0/distilbert-base-uncased-finetuned-amazon-reviews"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)
 
+text = "Replace me by any text you'd like."
+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. -->
-
-## Testing Data, Factors & Metrics
-
-### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-[amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)
-
+## Training and Evaluation Data
 
-### Factors
+Here is the raw dataset ([amazon_reviews_multi](https://huggingface.co/datasets/amazon_reviews_multi)) we used for finetuning the model.
+The dataset contains 200,000, 5,000, and 5,000 reviews in the training, dev, and test sets respectively.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## Fine-tuning hyperparameters
 
-acc
-f1
-precision
+The following hyperparameters were used during training:
 
-### Metrics
++ learning_rate: 2e-05
++ train_batch_size: 16
++ eval_batch_size: 16
++ seed: 42
++ optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
++ num_epochs: 5
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-metric1
+### Training results
 
-## Results 
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|:-----:|:-------------:|:---------------:|:--------:|
+|   1   |      123      |       123       |    123   |
+|   2   |      123      |       123       |    123   |
+|   3   |      231      |       123       |    123   |
+|   4   |      123      |       123       |    123   |
+|   5   |      123      |       123       |    123   |
 
-result1
+## Results
 
 
 # Framework versions