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| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-classification |
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| |
| # Model Card for Model ID |
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| <!-- Based on https://huggingface.co/t5-small, model generates SQL from text given table list with "CREATE TABLE" statements. |
| This is a very light weigh model and could be used in multiple analytical applications. --> |
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| Based on [bert-base-uncased](https://huggingface.co/bert-base-uncased) . This model takes in news summary/news headlines/news article and classifies into one of 40 categories (listed below) . Dataset used to traing this model is from [Kaggle](www.kaggle.com) called [News Category Dataset](https://www.kaggle.com/datasets/rmisra/news-category-dataset) porvided by [ rishabhmisra.github.io/publications]( rishabhmisra.github.io/publications) (Please cite this datatset when using this model). |
| Contact us for more info: support@cloudsummary.com. |
| ### Below are the output labels: |
| - arts = 0, arts & culture =1, black voices = 2, business = 3, college = 4, comedy = 5, crime = 6, culture & arts = 7, education = 8, entertainment = 9,environment = 10 |
| fifty=11, food & drink = 12 ,good news = 13, green = 14, healthy living = 15, home & living = 16, impact = 17, latino voices = 18 , media = 19, money = 20 , parenting = 21 , parents = 22 |
| politics = 23, queer voices = 24, religion = 25, science = 26, sports = 27, style = 28, style & beauty = 29 ,taste = 30 ,tech = 31, the worldpost = 32,travel = 33 |
| u.s. news = 34, weddings = 35, weird news = 36, wellness = 37, women = 38 , world news = 39 , worldpost = 40 |
| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** CloudSummary (support@cloudsummary.com) |
| - **Model type:** Language model |
| - **Language(s) (NLP):** English |
| - **License:** apache-2.0 |
| - **Finetuned from model :** [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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| ### Model Sources |
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| <!-- Provide the basic links for the model. --> |
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| Please refer [bert-base-uncased](https://huggingface.co/bert-base-uncased) for Model Sources. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| ```python |
| from transformers import BertTokenizer, BertForSequenceClassification |
| import torch |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
| model = BertForSequenceClassification.from_pretrained ("cssupport/bert-news-class").to(device) |
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| def predict(text): |
| id_to_class = {0: 'arts', 1: 'arts & culture', 2: 'black voices', 3: 'business', 4: 'college', 5: 'comedy', 6: 'crime', 7: 'culture & arts', 8: 'education', 9: 'entertainment', 10: 'environment', 11: 'fifty', 12: 'food & drink', 13: 'good news', 14: 'green', 15: 'healthy living', 16: 'home & living', 17: 'impact', 18: 'latino voices', 19: 'media', 20: 'money', 21: 'parenting', 22: 'parents', 23: 'politics', 24: 'queer voices', 25: 'religion', 26: 'science', 27: 'sports', 28: 'style', 29: 'style & beauty', 30: 'taste', 31: 'tech', 32: 'the worldpost', 33: 'travel', 34: 'u.s. news', 35: 'weddings', 36: 'weird news', 37: 'wellness', 38: 'women', 39: 'world news', 40: 'worldpost'} |
| # Tokenize the input text |
| inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=512, padding='max_length').to(device) |
| with torch.no_grad(): |
| logits = model(inputs['input_ids'], inputs['attention_mask'])[0] |
| # Get the predicted class index |
| pred_class_idx = torch.argmax(logits, dim=1).item() |
| return id_to_class[pred_class_idx] |
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| text ="The UK’s growing debt burden puts it on shaky ground ahead of upcoming assessments by the three main credit ratings agencies. A downgrade to its credit rating, which is a reflection of a country’s creditworthiness, could raise borrowing costs further still, although the impact may be limited." |
| predicted_class = predict(text) |
| print(predicted_class) |
| #OUTPUT : business |
| ``` |
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| ## Uses |
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| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| [More Information Needed] |
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| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
| Could used in application where natural language is to be converted into SQL queries. |
| [More Information Needed] |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| [More Information Needed] |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| [More Information Needed] |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## Technical Specifications |
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| ### Model Architecture and Objective |
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| [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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| ### Compute Infrastructure |
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| #### Hardware |
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| one P6000 GPU |
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| #### Software |
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| Pytorch and HuggingFace |
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| ### Citation |
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| Misra, Rishabh. "News Category Dataset." arXiv preprint arXiv:2209.11429 (2022). |
| Misra, Rishabh and Jigyasa Grover. "Sculpting Data for ML: The first act of Machine Learning." ISBN 9798585463570 (2021). |
| Tandon, Karan. "This LLM is based on BERT (2018) a bidirectional Transformer. **cssupport/bert-news-class** was finetuned using AdamW with the help of NVIDIA AMP and trained in 45 minutes on one P6000 GPU. This model accepts news summary/news headlines/news article and classifies into one of 40 categories" |
