
# TExAS-SQuAD-is

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-is dataset.
It achieves the following results on the evaluation set:
- Exact match: 56.91%
- F1-score: 59.93%

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1458        | 1.0   | 4219  | 1.8892          |
| 1.9202        | 2.0   | 8438  | 1.8566          |
| 1.7377        | 3.0   | 12657 | 1.8688          |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Hven\u00e6r var Halld\u00f3r Laxness \u00ed menntask\u00f3la ?", "context": "Halld\u00f3r Laxness ( Halld\u00f3r Kiljan ) f\u00e6ddist \u00ed Reykjav\u00edk 23. apr\u00edl \u00e1ri\u00f0 1902 og \u00e1tti \u00ed fyrstu heima vi\u00f0 Laugaveg en \u00e1ri\u00f0 1905 settist fj\u00f6lskyldan a\u00f0 \u00ed Laxnesi \u00ed Mosfellssveit . \u00dear \u00f3lst Halld\u00f3r upp en s\u00f3tti sk\u00f3la \u00ed Reykjav\u00edk \u00e1 unglings\u00e1rum . Ungur h\u00e9lt hann s\u00ed\u00f0an utan og var langdv\u00f6lum erlendis um \u00e1rabil \u2013 \u00ed \u00fdmsum Evr\u00f3pul\u00f6ndum og s\u00ed\u00f0ar \u00ed Amer\u00edku . \u00deegar hann var heima bj\u00f3 hann \u00ed Reykjav\u00edk \u00fear til hann og kona hans , Au\u00f0ur Sveinsd\u00f3ttir , bygg\u00f0u s\u00e9r h\u00fasi\u00f0 Glj\u00fafrastein \u00ed Mosfellssveit og fluttu \u00feanga\u00f0 \u00e1ri\u00f0 1945 . \u00dear var heimili \u00feeirra alla t\u00ed\u00f0 s\u00ed\u00f0an og \u00fear er n\u00fa safn til minningar um \u00feau . Halld\u00f3r l\u00e9st 8. febr\u00faar 1998 . Sk\u00f3laganga Halld\u00f3rs var\u00f0 ekki l\u00f6ng . \u00c1ri\u00f0 1918 h\u00f3f hann n\u00e1m vi\u00f0 Menntask\u00f3lann \u00ed Reykjav\u00edk en haf\u00f0i l\u00edtinn t\u00edma til a\u00f0 l\u00e6ra , enda var hann a\u00f0 skrifa sk\u00e1lds\u00f6gu , Barn n\u00e1tt\u00farunnar , sem kom \u00fat hausti\u00f0 1919 \u2013 \u00fe\u00e1 \u00feegar var h\u00f6fundurinn ungi farinn af landi brott . Sagan vakti \u00fe\u00f3 nokkra athygli og \u00ed Al\u00fe\u00fd\u00f0ubla\u00f0inu sag\u00f0i m.a. : \u201e Og hver veit nema a\u00f0 Halld\u00f3r fr\u00e1 Laxnesi eigi eftir a\u00f0 ver\u00f0a \u00f3skabarn \u00edslensku \u00fej\u00f3\u00f0arinnar . \u201c Upp fr\u00e1 \u00feessu sendi Halld\u00f3r fr\u00e1 s\u00e9r b\u00f3k n\u00e1nast \u00e1 hverju \u00e1ri , stundum fleiri en eina , \u00ed yfir sex \u00e1ratugi . Afk\u00f6st hans voru me\u00f0 eind\u00e6mum ; hann skrifa\u00f0i fj\u00f6lda sk\u00e1ldsagna , sumar \u00ed nokkrum hlutum , leikrit , kv\u00e6\u00f0i , sm\u00e1sagnas\u00f6fn og endurminningab\u00e6kur og gaf auk \u00feess \u00fat m\u00f6rg greinas\u00f6fn og ritger\u00f0ir . B\u00e6kurnar eru fj\u00f6lbreyttar en eiga \u00fea\u00f0 sameiginlegt a\u00f0 vera skrifa\u00f0ar af einstakri st\u00edlg\u00e1fu , dj\u00fapum mannskilningi og v\u00ed\u00f0t\u00e6kri \u00feekkingu \u00e1 s\u00f6gu og samf\u00e9lagi . \u00dear birtast oft afgerandi sko\u00f0anir \u00e1 \u00fej\u00f3\u00f0f\u00e9lagsm\u00e1lum og s\u00f6gupers\u00f3nur eru margar einkar eftirminnilegar ; tilsv\u00f6r \u00feeirra og lunderni hafa or\u00f0i\u00f0 samofin \u00fej\u00f3\u00f0ars\u00e1linni . \u00deekktustu verk Halld\u00f3rs eru eflaust sk\u00e1lds\u00f6gurnar st\u00f3ru og rismiklu , s.s. Salka Valka , Sj\u00e1lfst\u00e6tt f\u00f3lk , Heimslj\u00f3s , \u00cdslandsklukkan og Gerpla , og raunar m\u00e6tti telja upp mun fleiri ; Kv\u00e6\u00f0ab\u00f3k hans er \u00ed upp\u00e1haldi hj\u00e1 m\u00f6rgum sem og minningab\u00e6kurnar sem hann skrifa\u00f0i \u00e1 efri \u00e1rum um \u00e6sku\u00e1r s\u00edn ; af \u00feekktum greinas\u00f6fnum og ritger\u00f0um m\u00e1 nefna Al\u00fe\u00fd\u00f0ub\u00f3kina og Sk\u00e1ldat\u00edma . Miki\u00f0 hefur veri\u00f0 skrifa\u00f0 um verk og \u00e6vi sk\u00e1ldsins , en h\u00e9r skal a\u00f0eins bent \u00e1 \u00edtarlega fr\u00e1s\u00f6gn og greiningu Halld\u00f3rs Gu\u00f0mundssonar \u00ed b\u00f3kinni Halld\u00f3r Laxness \u2013 \u00e6visaga ."}], "model-index": [{"name": "xlmr-base-texas-squad-is", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

TExAS-SQuAD-is
==============


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


* Exact match: 56.91%
* F1-score: 59.93%


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 #xlm-roberta #question-answering #generated_from_trainer #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 #xlm-roberta #question-answering #generated_from_trainer #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