
# TExAS-SQuAD-da

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset.
It achieves the following results on the evaluation set:
- Exact match: 63.96%
- F1-score: 68.40%

In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves 30.37% EM and 37.15% F1.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6438        | 1.0   | 4183  | 1.4711          |
| 1.4079        | 2.0   | 8366  | 1.4356          |
| 1.2532        | 3.0   | 12549 | 1.4509          |


### Framework versions

- Transformers 4.12.2
- Pytorch 1.8.1+cu101
- Datasets 1.12.1
- Tokenizers 0.10.3

{"language": ["da"], "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Hvem handler artiklen om?", "context": "Forfatter og musiker Flemming Quist M\u00f8ller er d\u00f8d i en alder af 79 \u00e5r. Den folkek\u00e6re kunstner faldt om ved morgenbordet med en blodprop i hjertet i mandags. Det kunne forfatterens s\u00f8n, Carl Quist-M\u00f8ller, bekr\u00e6fte over for TV 2 Lorry.- Han faldt om i det hus i Taarb\u00e6k, hvor han er vokset op og ogs\u00e5 har boet de sidste \u00e5r af sit liv. Han blev lagt i koma p\u00e5 Rigshospitalet. Her har vi siddet omkring ham i en uge, siger Carl Quist-M\u00f8ller til mediet.MindeordI mange \u00e5r var Flemming Quist M\u00f8ller en del af bandet Bazaar sammen med Peter Bastian, Anders Koppel og Mehmet Ozan.Anders Koppel er tydeligt r\u00f8rt over vennens d\u00f8d, da Ekstra Bladet r\u00e6kker ud til ham mandag aften.- Det er en stor del af mit liv, der er forsvundet med Flemmings liv, det er klart. Vi har spillet sammen i 37 \u00e5r, siger han og forts\u00e6tter:- Jeg vil mest huske ham for hans ukonventionelle tilgang til alting. Flemming havde et meget st\u00e6rkt blik for det autentiske og \u00e6rlige. Han var ikke bundet af normer -tv\u00e6rtimod, hvis han s\u00e5 en norm, hvor noget skulle g\u00f8res p\u00e5 en bestemt m\u00e5de, s\u00e5 flygtede han eller pr\u00f8vede at springe det i stumper og stykker.If\u00f8lge den danske musiker og komponist er netop f\u00f8lgende ord rammende for Flemming Quist M\u00f8ller: Original, vidende, kompromisl\u00f8s og humoristisk."}], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlmr-base-texas-squad-da", "results": []}]}
transformers pytorch tensorboard safetensors xlm-roberta question-answering generated_from_trainer da base_model:xlm-roberta-base license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #da #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us 

TExAS-SQuAD-da
==============


This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-da dataset.
It achieves the following results on the evaluation set:


* Exact match: 63.96%
* F1-score: 68.40%


In comparison, the 'jacobshein/danish-bert-botxo-qa-squad' model achieves 30.37% EM and 37.15% F1.


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #da #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #da #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3