Update README.md

README.md

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-library_name: transformers
-tags: []
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 # Model Card for Model ID
 
 Intent classification is the act of classifying customer's in to different pre defined categories. 
@@ -30,47 +25,12 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+- **Repository:** https://github.com/SerjSmor/intent_classification
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+
 
 [More Information Needed]
 
@@ -79,122 +39,53 @@ Use the code below to get started with the model.
 ### Training Data
 
 <!-- This should link to a Dataset 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. -->
+https://github.com/SerjSmor/intent_classification
+HF dataset will be added in the future.
 
 [More Information Needed]
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+https://github.com/SerjSmor/intent_classification/blob/main/t5_generator_trainer.py
 
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+Using HF trainer
 
-#### Training Hyperparameters
+    training_args = TrainingArguments(
+        output_dir='./results',
+        num_train_epochs=epochs,
+        per_device_train_batch_size=batch_size,
+        per_device_eval_batch_size=batch_size,
+        warmup_steps=500,
+        weight_decay=0.01,
+        logging_dir='./logs',
+        logging_steps=10,
+        evaluation_strategy="epoch"
+    )
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset,
+        eval_dataset=val_dataset,
+        tokenizer=tokenizer,
+        # compute_metrics=compute_metrics
+    )
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
+I've used Atis dataset for evaluation. 
+F1 AVG on the train set is 0.69
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
 
 #### Summary
 
 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
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-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+Nvidia RTX3060 12Gb
 
-[More Information Needed]