


  [ElhBERTeu](https://huggingface.co/orai-nlp/ElhBERTeu) model finetuned on the NERC dataset from [BasqueGlue](https://huggingface.co/datasets/orai-nlp/basqueGLUE). 
 

 In Domain and Out-of-domain tasks' train and dev datasets were merged for training. Reported performance results are tested on the merged tests dataset after training for 10 epochs (batch size 32, learning rate  3e-5).

Results on test set:

* accuracy = 0.9785551028520322
* f1 = 0.8665899340797322
* loss = 0.11313809949144367
* precision = 0.8650511802799248
* recall = 0.8681341719077568

Per class results: 

| CLASS    | precision | recall | F1 score | support |
|----------|:---------:|:------:|:--------:|:-------:|
|      LOC |   89.43%  | 87.95% |   88.68  |   1844  |
|     MISC |   70.72%  | 67.75% |   69.20  |   502   |
|      ORG |   80.04%  | 84.32% |   82.12  |   1082  |
|      PER |   93.52%  | 94.57% |   94.04  |   1359  |
|          |           |        |          |         |
|   GLOBAL |   86.51%  | 86.81% |   86.66  |         |


Tagset: 
["O", "B-ORG", "B-PER", "I-PER", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]


Finetuning details: 10 epochs, batch size 32, learning rate  3e-5.
{"language": ["eu"], "license": "cc-by-nc-4.0", "tags": ["NERC"], "datasets": ["orai-nlp/basqueGLUE"], "metrics": ["accuracy", "f1"], "pipeline_tag": "token-classification"}
transformers pytorch safetensors bert token-classification NERC eu dataset:orai-nlp/basqueGLUE license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #token-classification #NERC #eu #dataset-orai-nlp/basqueGLUE #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 

ElhBERTeu model finetuned on the NERC dataset from BasqueGlue.


In Domain and Out-of-domain tasks' train and dev datasets were merged for training. Reported performance results are tested on the merged tests dataset after training for 10 epochs (batch size 32, learning rate 3e-5).


Results on test set:


* accuracy = 0.9785551028520322
* f1 = 0.8665899340797322
* loss = 0.11313809949144367
* precision = 0.8650511802799248
* recall = 0.8681341719077568


Per class results:



Tagset:
["O", "B-ORG", "B-PER", "I-PER", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC"]


Finetuning details: 10 epochs, batch size 32, learning rate 3e-5.



TAGS
#transformers #pytorch #safetensors #bert #token-classification #NERC #eu #dataset-orai-nlp/basqueGLUE #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us 
