Update README.md

README.md

@@ -50,165 +50,34 @@ ROUGEL : 0.154
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 You can use this model for text summarization.
-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- 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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-[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.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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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-[More Information Needed]
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
+import torch
 
-#### Factors
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+tokenizer = AutoTokenizer.from_pretrained("pascalrai/nep_summ_BART")
+model = AutoModelForSeq2SeqLM.from_pretrained("pascalrai/nep_summ_BART")
 
-[More Information Needed]
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-#### Metrics
+sentence = """अत्यधिक माग भएका बेला दसैंमा चिनीको हाहाकार भएको थियो । उपत्यकाबाहिरका केही जिल्लामा चिनी पाइए पनि काठमाडौंमा भने अभाव नै कायम रहेको छ । प्रधानमन्त्री पुष्पकमल दाहालले बिहीबार बिहान उद्योग तथा वाणिज्य मन्त्री तथा मुख्यसचिवलाई चिनीको अभाव सिर्जना हुन नदिन सबै उपायको खोजी गर्न निर्देशन दिएका थिए ।
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+नेपाली चिनी उद्योगहरूले आम उपभोक्तालाई सहज हुने किसिमले बजारमा चिनी नपठाइ ठूला उद्योगलाई आपूर्ति गर्न गोदाममै राख्ने गरेको पनि भेटिएको छ । वाणिज्य विभागको तथ्यांक अनुसार, नेपालमा उत्पादन हुने चिनीको सत्तरी प्रतिशत चिनी बिभिन्न पेय पदार्थ, मिठाइ, चकलेट, विस्कुटलगायतका उद्योगहरुमा आपूर्ति हुने गर्दछ ।
 
-[More Information Needed]
+नेपाल प्रहरीले नेपालमा रहेका सबै चिनी उद्योगको स्टक रेकर्ड चेक गर्ने तथा सो आधारमा बजारमा चिनी पठाउन उद्योगीहरूसँग छलफल गरिने विभागले जनाएको छ"""
 
-### Results
+inputs = tokenizer(sentence, max_length=1000, return_tensors="pt")
+summary_ids = model.to(device).generate(inputs["input_ids"].to(device))
 
-[More Information Needed]
+tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
 
-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
+'दशैंका बेला सबै चिनी उद्योगको स्टक रेकर्ड गर्ने र बजारमा चिनी पठाउन उद्योगीहरूसँग छलफल गरिने अधिकारीहरूले बताएका छन्। यसकारण, उपत्यकाबाहिरका केही जिल्लामा पनि चिनीको अभाव कायम रहेको अधिकारीहरूले बताएका छन्।'
 
 #### Hardware
 
-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- 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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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
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+The model was trained on a single A10G GPU in an AWS instance with each epoch taking roughly 2 days.