Update README.md

README.md

@@ -1,11 +1,14 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- mteb/tweet_sentiment_extraction
+base_model:
+- openai-community/gpt2
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model fine-tunes GPT-2 on the "Tweet Sentiment Extraction" dataset for sentiment analysis tasks.
 
 
 
@@ -15,185 +18,95 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model fine-tunes GPT-2 using the "Tweet Sentiment Extraction" dataset to extract sentiment-relevant portions of text. 
+It demonstrates preprocessing, tokenization, and fine-tuning with Hugging Face libraries.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## 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. -->
+### Direct Use
 
-[More Information Needed]
+This model can be used to analyze text for sentiment-relevant extractions directly after fine-tuning. 
+It works as a baseline model for learning sentiment-specific features.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Fine-tuned for tasks that involve sentiment analysis, such as social media monitoring or customer feedback analysis.
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Avoid using the model for real-time sentiment prediction or deployment without additional training/testing for specific use cases.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The dataset used may not fully represent the diversity of text, leading to biases in the output. There is a risk of overfitting to the specific dataset.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Carefully evaluate the model for biases and limitations before deploying in production environments. Consider retraining on a more diverse dataset if required.
 
-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]
-
-## Training Details
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-### Training Data
+model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/Wexnflex/Tweet_Sentiment")
+tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/Wexnflex/Tweet_Sentiment")
 
-<!-- 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. -->
+text = "Input your text here."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
 
-[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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
 
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+Training Hyperparameters
+Batch size: 16
+Learning rate: 2e-5
+Epochs: 3
+Optimizer: AdamW
 
-<!-- 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. -->
-
-### Testing Data, Factors & Metrics
+#### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+The evaluation was performed on the test split of the "Tweet Sentiment Extraction" dataset.
 
-[More Information Needed]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Evaluation is segmented by sentiment labels (e.g., positive, negative, neutral).
 
-[More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Accuracy 
 
 ### Results
 
-[More Information Needed]
-
+70% Accuracy 
 #### Summary
 
+The fine-tuned model performs well for extracting sentiment-relevant text, with room for improvement in handling ambiguous cases.
 
 
-## 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
-
-[More Information Needed]
 
 #### Hardware
 
-[More Information Needed]
-
+T4 GPU (Google Colab)
 #### 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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+Hugging Face Transformers Library
 
-[More Information Needed]