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README.md

@@ -1,208 +1,92 @@
----
-datasets:
-- twitter_multi_class_sentiment
-language:
-- en
-license: apache-2.0
-tags:
-- twitter
-- sentiment-analysis
-- bert
-- nlp
-- emotion-classification
----
-
-# Model Card for Aakash22134/bert-twitter-sentiment-classifier
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This model is a fine-tuned BERT classifier trained on a Twitter multi-class sentiment dataset (emotions: sadness, joy, anger, love, fear, surprise). Achieved ~90% accuracy.
-
-- **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):** ['en']
-- **License:** apache-2.0
-- **Finetuned from model [optional]:** bert-base-uncased
-
-### 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. -->
-
-[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]
+# bert-twitter-sentiment-classifier
 
-### Out-of-Scope Use
+**Fine-tuned model**: `bert-base-uncased` → **bert-twitter-sentiment-classifier**
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+**Author:** Aakash (Aakash22134)
+**Contact:** saiaakash33333@gmail.com
+**License:** apache-2.0
+**Languages:** en
 
-[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]
+## Model description
+This model is a fine-tuned **BERT** classifier for multi-class emotion / sentiment classification on short Twitter text.
+It predicts one of the following classes: **sadness, joy, love, anger, fear, surprise**.
 
+The model was trained on the *twitter_multi_class_sentiment* dataset and demonstrates strong classification performance on the held-out test set.
 
-#### 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 -->
+## Training data
+- **Dataset:** twitter_multi_class_sentiment (public CSV from example notebook)
+- **Train / Validation / Test:** 11200 / 1600 / 3200
+- **Preprocessing:** tokenized with `bert-base-uncased` tokenizer, padding + truncation to default BERT max length in the notebook
 
-#### Speeds, Sizes, Times [optional]
+---
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+## Training procedure & hyperparameters
+- **Base model:** bert-base-uncased
+- **Training epochs:** 2
+- **Batch size (train/eval):** 64 / 64
+- **Learning rate:** 2e-05
+- **Weight decay:** 0.01
+- **Trainer:** `transformers.Trainer` (Hugging Face Transformers)
+- **Notes:** model was trained for 2 epochs in a Colab environment; consider longer training or more data for further improvements.
 
-[More Information Needed]
+---
 
 ## Evaluation
+**Test set results (approx):**
+- **Accuracy:** 0.900625
+- **F1 (weighted):** 0.900321
+
+**Per-class (precision / recall / f1 / support):**
+{
+  "sadness": {
+    "precision": 0.93,
+    "recall": 0.95,
+    "f1": 0.94,
+    "support": 933
+  },
+  "joy": {
+    "precision": 0.92,
+    "recall": 0.91,
+    "f1": 0.92,
+    "support": 1072
+  },
+  "love": {
+    "precision": 0.76,
+    "recall": 0.75,
+    "f1": 0.76,
+    "support": 261
+  },
+  "anger": {
+    "precision": 0.91,
+    "recall": 0.91,
+    "f1": 0.91,
+    "support": 432
+  },
+  "fear": {
+    "precision": 0.89,
+    "recall": 0.88,
+    "f1": 0.88,
+    "support": 387
+  },
+  "surprise": {
+    "precision": 0.75,
+    "recall": 0.72,
+    "f1": 0.74,
+    "support": 115
+  }
+}
+
+**Evaluation details:** computed with `sklearn.metrics.classification_report`.
+
+**WandB logs:** https://wandb.ai/saiaakash33333-gitam/huggingface
+**Run:** https://wandb.ai/saiaakash33333-gitam/huggingface/runs/qtmurwgd
 
-<!-- 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
+## Usage
 
-[More Information Needed]
+_Last updated: 2025-09-29 09:20:19 UTC_