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sdg-scibert-zo_up

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Simon Clematide commited on Apr 30

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Revise README.md to enhance model documentation and usage instructions

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- 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.
-- **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. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### 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. -->
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-### 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]
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-#### 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. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## 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]
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## 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. -->
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-## Glossary [optional]
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+# SDG SciBERT Classifier (`sdg-scibert-zo_up`)
+This repository contains a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) for classifying scientific text into Sustainable Development Goal (SDG) categories.
+- Fine-tuned using the 🤗 `transformers` Trainer API
+- Uses standard `AutoModelForSequenceClassification`
+- Published with full label mappings, inference scripts, and CLI tool
+---
+## 🧪 Quick Inference (Python)
+You can use the model directly with the Hugging Face `pipeline`:
+```python
+from transformers import pipeline
+classifier = pipeline(
+    "text-classification",
+    model="simon-clmtd/sdg-scibert-zo_up",
+    tokenizer="simon-clmtd/sdg-scibert-zo_up",
+    truncation=True,
+    padding=True,
+    max_length=512,
+    return_all_scores=True,
+    device=0  # or -1 for CPU
+)
+text = "Ensure access to affordable, reliable, sustainable and modern energy for all"
+print(classifier(text))
+```
+---
+## 🖥️ CLI Tool: `sdg-predict`
+### 🔧 Installation (local)
+Clone the repo and install as a Python package:
+```bash
+git clone https://huggingface.co/simon-clmtd/sdg-scibert-zo_up
+cd sdg-scibert-zo_up
+pip install -e .
+```
+This will install a command-line tool called `sdg-predict`.
+### 📥 Input format
+The CLI tool accepts a `.jsonl` file (one JSON object per line). You must specify the key containing the text to classify:
+Example input file (`input.jsonl`):
+```json
+{"id": 1, "text": "Ensure access to affordable, reliable, sustainable and modern energy for all"}
+{"id": 2, "text": "Atmospheric warming is profoundly affecting high-mountain regions"}
+```
+### ▶️ Example usage
+#### Top-1 prediction:
+```bash
+sdg-predict input.jsonl --key text --top1 --output preds.jsonl
+```
+#### Full label distribution:
+```bash
+sdg-predict input.jsonl --key text --output preds_all.jsonl
+```
+#### Custom batch size:
+```bash
+sdg-predict input.jsonl --key text --batch_size 16
+```
+### 📤 Output format
+Each output line is the original input with an added `prediction` key:
+**With `--top1`:**
+```json
+{
+  "id": 1,
+  "text": "...",
+  "prediction": {
+    "label": "7",
+    "score": 0.9124
+  }
+}
+```
+**Without `--top1`:**
+```json
+{
+  "id": 1,
+  "text": "...",
+  "prediction": [
+    {"label": "1", "score": 0.0021},
+    {"label": "2", "score": 0.0005},
+    ...
+    {"label": "7", "score": 0.9124}
+  ]
+}
+```
+---
+## 📦 Repository Contents
+- `modeling.py`: Optional class wrapper if extending the base model.
+- `inference.py`: Reusable batch inference logic for Python scripts.
+- `cli_predict.py`: CLI tool using the inference logic.
+- `requirements.txt`: Runtime dependencies.
+- `setup.py`: Installation and entry point for the CLI.
+---
+## 🔍 Citation
+Please cite the original [SciBERT paper](https://arxiv.org/abs/1903.10676) if using this model, and attribute this fine-tuning setup if relevant.
+---
+## 👤 Author
+Simon Clematide
+Computational Linguistics, UZH
+[simon-clematide.net](https://simon-clematide.net) (if applicable)