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Gabriel
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bart-base-cnn-swe

Summarization
Transformers
PyTorch
TensorBoard
Swedish
bart
text2text-generation
Eval Results
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Gabriel commited on
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update model card README.md

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  ---
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- language: sv
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  license: mit
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- datasets:
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- - Gabriel/cnn_daily_swe
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  tags:
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- - summarization
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-
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- widget:
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- - text: "En kronologi av bombningar och försök bombattacker i det brittiska fastlandet sedan 1970-talet:. Polisen stänger gatorna runt Haymarket, i Londons livliga teaterdistrikt. 29 juni 2007: Polisen desarmerar en bomb bestående av 200 liter bränsle, gasflaskor och spikar som hittats i en övergiven bil i Haymarket i centrala London. En andra bil fylld med gas och spikar befanns senare ha parkerats bara några hundra meter från den första, innan den bogserades bort av trafikvakter i början av fredagen för att bryta parkeringsrestriktioner. Polisen säger att två fordon är tydligt kopplade. 21 juli 2005: Två veckor efter de dödliga 7/7 bombningarna påstås fyra män ha försökt genomföra en andra våg av attacker mot Londons transportnät vid tre tunnelbanestationer i London och ombord på en buss. Men deras påstådda ryggsäcksbomber exploderar inte. 7 juli 2005: Fyra självmordsbombare detonerar sig själva ombord på tre underjordiska tåg och en buss i en morgon rusningstid attack mot Londons transportnät, döda 52 människor och skada omkring 700 fler. Al-Qaida tar på sig ansvaret i ett videouttalande."
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-
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  model-index:
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  - name: bart-base-cnn-swe
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- results:
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- - task:
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- type: summarization
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- name: summarization
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- dataset:
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- name: Gabriel/cnn_daily_swe
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- type: Gabriel/cnn_daily_swe
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- split: validation
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- metrics:
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- - name: Validation ROGUE-1
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- type: rouge-1
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- value: 21.7291
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- verified: true
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- - name: Validation ROGUE-2
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- type: rouge-2
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- value: 10.0209
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- verified: true
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- - name: Validation ROGUE-L
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- type: rouge-l
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- value: 17.775
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- verified: true
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  ---
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- # bart-base-cnn-swe
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-
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- WORK IN PROGRESS!
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- - 1. Further fine-tune on checkpoint with cnn-daily-swe.
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- - 2. Further fine-tune on xsum-swe.
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- - 3. Lastly fine-tune on smaller domain dataset.
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- This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the [Gabriel/bart-base-cnn-swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe) dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.1656
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- - Rouge1: 21.7291
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- - Rouge2: 10.0209
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- - Rougel: 17.775
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- - Rougelsum: 20.429
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- - Gen Len: 19.9931
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  ## Model description
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- WORK IN PROGRESS!
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  ## Intended uses & limitations
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- WORK IN PROGRESS!
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  ## Training and evaluation data
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- WORK IN PROGRESS!
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  ## Training procedure
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 1
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- - eval_batch_size: 1
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  - seed: 42
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- - gradient_accumulation_steps: 16
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  - total_train_batch_size: 16
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 1
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  - mixed_precision_training: Native AMP
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:|
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- | 2.3512 | 1.0 | 17944 | 2.1656 | 21.7291 | 10.0209 | 17.775 | 20.429 | 19.9931 |
 
 
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  ---
 
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  license: mit
 
 
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  tags:
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+ - generated_from_trainer
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+ metrics:
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+ - rouge
 
 
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  model-index:
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  - name: bart-base-cnn-swe
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+ results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
 
 
 
 
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+ # bart-base-cnn-swe
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+ This model is a fine-tuned version of [Gabriel/bart-base-cnn-swe](https://huggingface.co/Gabriel/bart-base-cnn-swe) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.0253
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+ - Rouge1: 22.0568
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+ - Rouge2: 10.3302
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+ - Rougel: 18.0648
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+ - Rougelsum: 20.7482
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+ - Gen Len: 19.9996
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  ## Model description
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+ More information needed
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  ## Intended uses & limitations
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+ More information needed
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  ## Training and evaluation data
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+ More information needed
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  ## Training procedure
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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+ - gradient_accumulation_steps: 2
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  - total_train_batch_size: 16
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 2
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  - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
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+ | 2.2349 | 1.0 | 17944 | 2.0643 | 21.9564 | 10.2133 | 17.9958 | 20.6502 | 19.9992 |
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+ | 2.0726 | 2.0 | 35888 | 2.0253 | 22.0568 | 10.3302 | 18.0648 | 20.7482 | 19.9996 |
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+
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+ ### Framework versions
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+ - Transformers 4.22.0
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+ - Pytorch 1.12.1+cu113
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+ - Datasets 2.4.0
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+ - Tokenizers 0.12.1