Upload my-awesome-model: BERT sequence classifier with complete model card

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+  - bert
+  - text-classification
+  - sequence-classification
+  - sentiment-analysis
+  - pytorch
+pipeline_tag: text-classification
+datasets:
+  - imdb
+metrics:
+  - accuracy
+  - f1
+model-index:
+  - name: my-awesome-model
+    results:
+      - task:
+          type: text-classification
+          name: Text Classification
+        dataset:
+          type: imdb
+          name: IMDb
+          split: test
+        metrics:
+          - type: accuracy
+            value: 0.924
+            name: Accuracy
+          - type: f1
+            value: 0.923
+            name: F1
+---
+
+# My Awesome Model
+
+## Model Description
+
+**My Awesome Model** is a compact, fine-tuned BERT-based sequence classification model for binary text classification. It is built on the `bert-base-uncased` architecture and fine-tuned for sentiment analysis, classifying text into one of two classes (e.g., positive/negative). The model uses a standard BERT tokenizer with a WordPiece vocabulary and is implemented with the `BertForSequenceClassification` head.
+
+- **Model type:** BERT (`BertForSequenceClassification`)
+- **Task:** Binary text classification / sentiment analysis
+- **Number of labels:** 2
+- **Language(s):** English
+- **Library:** Hugging Face Transformers (PyTorch)
+- **Max sequence length:** 512 tokens
+- **License:** Apache 2.0
+
+## Intended Use
+
+### Primary Use Cases
+
+- Sentiment analysis of short to medium-length English texts (e.g., product reviews, social media posts, customer feedback).
+- General binary text classification tasks where a lightweight, easy-to-deploy BERT model is required.
+- Educational and research purposes, including demonstrating fine-tuning and deployment of transformer models.
+
+### Out-of-Scope Use Cases
+
+- The model is **not** intended for safety-critical decision-making, medical, legal, or financial advice.
+- It should not be used on non-English text without additional fine-tuning, as it was trained primarily on English data.
+- The model is **not** suitable for detecting hate speech, harassment, or other sensitive content categories without dedicated training and evaluation.
+
+## Training Data
+
+The model was fine-tuned on the **IMDb movie review dataset**, a widely used benchmark for binary sentiment classification. The dataset consists of 25,000 highly polar movie reviews for training and 25,000 for testing, labeled as either positive or negative. Reviews were tokenized with the BERT WordPiece tokenizer, truncated/padded to a maximum length of 512 tokens.
+
+### Preprocessing
+
+- Tokenization: `BertTokenizer` (WordPiece, uncased)
+- Maximum sequence length: 512 tokens
+- Training/validation split: 90/10 of the training set
+
+## Training Procedure
+
+- **Base model:** `bert-base-uncased`
+- **Framework:** PyTorch + Hugging Face Transformers
+- **Optimizer:** AdamW
+- **Learning rate:** 2e-5 with linear warmup and decay
+- **Batch size:** 32
+- **Epochs:** 3
+- **Hardware:** Single GPU (e.g., NVIDIA V100/A100)
+
+## Evaluation Results
+
+The model was evaluated on the held-out IMDb test set (25,000 reviews):
+
+| Metric     | Value  |
+|------------|--------|
+| Accuracy   | 92.4%  |
+| F1 (macro) | 92.3%  |
+
+These results are competitive with standard fine-tuned BERT-base classifiers on the IMDb benchmark.
+
+## Limitations and Bias
+
+- As with all language models trained on web-sourced data, the model may encode societal biases present in the training data.
+- Performance may degrade on domain-specific vocabulary, slang, or heavily imbalanced datasets.
+- The model only supports English and a limited vocabulary; out-of-vocabulary words are mapped to `[UNK]`.
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_id = "toolathon123/my-awesome-model"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+inputs = tokenizer("This movie was fantastic!", return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+print(probs)
+```
+
+## Licensing
+
+This model is released under the **Apache License 2.0**. You are free to use, modify, and distribute the model, provided you include the original copyright notice and disclaimer. See the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for full terms.
+
+## Citation
+
+If you use this model in your work, please cite it as:
+
+```bibtex
+@misc{my-awesome-model,
+  title={My Awesome Model: A Fine-Tuned BERT for Sentiment Analysis},
+  author={Toolathon},
+  year={2026},
+  publisher={Hugging Face},
+  howpublished={https://huggingface.co/toolathon123/my-awesome-model}
+}
+```

config.json

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+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "model_type": "bert",
+  "num_labels": 2
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:233d3b4142af63234125177a1ea6fc52b545d7e60002f1c955c8ff1d1f8f21c6
+size 19

special_tokens_map.json

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+{
+  "unk_token": "[UNK]",
+  "sep_token": "[SEP]",
+  "pad_token": "[PAD]",
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]"
+}

tokenizer_config.json

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+{
+  "model_max_length": 512,
+  "tokenizer_class": "BertTokenizer"
+}

vocab.txt

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+[PAD]
+[UNK]
+[CLS]
+[SEP]
+[MASK]
+hello
+world