Update README.md

README.md

@@ -1,227 +1,82 @@
 ---
+language: en
 license: apache-2.0
+tags:
+  - text-classification
+  - multi-label
+  - bert
+  - goemotions
+  - emotion-classification
 datasets:
-- google-research-datasets/go_emotions
-language:
-- en
+  - goemotions
 metrics:
-- accuracy
-base_model:
-- google-bert/bert-base-uncased
-pipeline_tag: text-classification
+  - f1
+  - precision
+  - recall
+widget:
+  - text: "I’m just chilling today."
+    example_title: "Neutral Example"
+  - text: "Thank you for saving my life!"
+    example_title: "Gratitude Example"
+  - text: "I’m nervous about my exam tomorrow."
+    example_title: "Nervousness Example"
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# GoEmotions BERT Classifier
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This is a fine-tuned **BERT-base-uncased** model for multi-label emotion classification on the [GoEmotions dataset](https://huggingface.co/datasets/goemotions), predicting 28 emotions (e.g., admiration, anger, joy, neutral).
 
 ## Model Details
 
-### Model Description
+- **Architecture**: BERT-base-uncased (110M parameters)
+- **Training Data**: GoEmotions (58k Reddit comments, 28 emotions)
+- **Loss Function**: Focal Loss (gamma=2)
+- **Optimizer**: AdamW (lr=2e-5, weight_decay=0.01)
+- **Epochs**: 5
+- **Hardware**: Kaggle T4 x2 GPUs
 
-<!-- Provide a longer summary of what this model is. -->
+## Performance
 
+- **Micro F1**: 0.6025 (optimized thresholds)
+- **Macro F1**: 0.5266
+- **Precision**: 0.5425
+- **Recall**: 0.6775
+- **Hamming Loss**: 0.0372
+- **Avg Positive Predictions**: 1.4564
 
+**Class-wise Performance** (selected):
+- Gratitude: F1 0.9120
+- Love: F1 0.8032
+- Neutral: F1 0.6827
+- Nervousness: F1 0.2564
+- Relief: F1 0.2857
 
-- **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]
+## Usage
 
-### 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. -->
+The model uses optimized thresholds stored in `thresholds.json` for predictions. Example in Python:
 
+```python
 from transformers import BertForSequenceClassification, BertTokenizer
+import torch
 import json
+import requests
 
-model = BertForSequenceClassification.from_pretrained("logasanjeev/goemotions-bert")
-tokenizer = BertTokenizer.from_pretrained("logasanjeev/goemotions-bert")
+# Load model and tokenizer
+repo_id = "logasanjeev/goemotions-bert"
+model = BertForSequenceClassification.from_pretrained(repo_id)
+tokenizer = BertTokenizer.from_pretrained(repo_id)
 
 # Load thresholds
-import requests
-thresholds_url = "https://huggingface.co/logasanjeev/goemotions-bert/raw/main/thresholds.json"
+thresholds_url = f"https://huggingface.co/{repo_id}/raw/main/thresholds.json"
 thresholds_data = json.loads(requests.get(thresholds_url).text)
 emotion_labels = thresholds_data["emotion_labels"]
 thresholds = thresholds_data["thresholds"]
 
-# Test prediction
+# Predict
 text = "I’m just chilling today."
 encodings = tokenizer(text, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
 with torch.no_grad():
     logits = torch.sigmoid(model(**encodings).logits).numpy()[0]
 predictions = [(emotion_labels[i], logit) for i, (logit, thresh) in enumerate(zip(logits, thresholds)) if logit >= thresh]
-print("Predicted emotions:", sorted(predictions, key=lambda x: x[1], reverse=True))
-
-[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
-
-<!-- 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]
-
-## Training Details
-
-### 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. -->
-
-[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]
-
-<!-- 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
-
-<!-- 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
-
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+print(sorted(predictions, key=lambda x: x[1], reverse=True))
+# Output: [('neutral', 0.8147)]