| language: en | |
| tags: | |
| - summarization | |
| model-index: | |
| - name: yuvraj/summarizer-cnndm | |
| results: | |
| - task: | |
| type: summarization | |
| name: Summarization | |
| dataset: | |
| name: sepidmnorozy/Urdu_sentiment | |
| type: sepidmnorozy/Urdu_sentiment | |
| config: sepidmnorozy--Urdu_sentiment | |
| split: train | |
| metrics: | |
| - name: ROUGE-1 | |
| type: rouge | |
| value: 0.0 | |
| verified: true | |
| - name: ROUGE-2 | |
| type: rouge | |
| value: 0.0 | |
| verified: true | |
| - name: ROUGE-L | |
| type: rouge | |
| value: 0.0 | |
| verified: true | |
| - name: ROUGE-LSUM | |
| type: rouge | |
| value: 0.0 | |
| verified: true | |
| - name: loss | |
| type: loss | |
| value: 10.730116844177246 | |
| verified: true | |
| - name: gen_len | |
| type: gen_len | |
| value: 19.9912 | |
| verified: true | |
| | |
| # Summarization | |
| | |
| ## Model description | |
| | |
| BartForConditionalGeneration model fine tuned for summarization on 10000 samples from the cnn-dailymail dataset | |
| | |
| ## How to use | |
| | |
| PyTorch model available | |
| | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline | |
| | |
| tokenizer = AutoTokenizer.from_pretrained("yuvraj/summarizer-cnndm") | |
| AutoModelWithLMHead.from_pretrained("yuvraj/summarizer-cnndm") | |
| | |
| summarizer = pipeline('summarization', model=model, tokenizer=tokenizer) | |
| summarizer("<Text to be summarized>") | |
| | |
| ## Limitations and bias | |
| Trained on a small dataset | |