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# distilbert-base-multilingual-cased-mapa_coarse-ner

This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the lextreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0882
- Precision: 0.7191
- Recall: 0.6453
- F1: 0.6802
- Accuracy: 0.9879

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0248        | 1.0   | 1739  | 0.0528          | 0.7451    | 0.5805 | 0.6525 | 0.9871   |
| 0.0181        | 2.0   | 3478  | 0.0595          | 0.7369    | 0.5749 | 0.6459 | 0.9875   |
| 0.0121        | 3.0   | 5217  | 0.0499          | 0.7404    | 0.6280 | 0.6796 | 0.9879   |
| 0.0088        | 4.0   | 6956  | 0.0634          | 0.6912    | 0.6334 | 0.6610 | 0.9875   |
| 0.0072        | 5.0   | 8695  | 0.0625          | 0.7109    | 0.6478 | 0.6779 | 0.9880   |
| 0.0052        | 6.0   | 10434 | 0.0702          | 0.7098    | 0.6518 | 0.6796 | 0.9878   |
| 0.0041        | 7.0   | 12173 | 0.0733          | 0.7176    | 0.6429 | 0.6782 | 0.9878   |
| 0.0026        | 8.0   | 13912 | 0.0779          | 0.7198    | 0.6540 | 0.6853 | 0.9879   |
| 0.0019        | 9.0   | 15651 | 0.0875          | 0.7181    | 0.6419 | 0.6779 | 0.9877   |
| 0.0018        | 10.0  | 17390 | 0.0882          | 0.7191    | 0.6453 | 0.6802 | 0.9879   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
{"language": ["en", "fr", "it", "es", "de", "nl", "pl", "ru", "pt"], "license": "apache-2.0", "tags": ["generated_from_trainer", "name-entity-recognition", "legal"], "datasets": ["lextreme"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-multilingual-cased-mapa_coarse-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "lextreme", "type": "lextreme", "config": "mapa_coarse", "split": "test", "args": "mapa_coarse"}, "metrics": [{"type": "precision", "value": 0.7191116088092572, "name": "Precision"}, {"type": "recall", "value": 0.6452855468095796, "name": "Recall"}, {"type": "f1", "value": 0.6802012534204254, "name": "F1"}, {"type": "accuracy", "value": 0.9878756336348935, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard safetensors distilbert token-classification generated_from_trainer name-entity-recognition legal en fr it es de nl pl ru pt dataset:lextreme license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #name-entity-recognition #legal #en #fr #it #es #de #nl #pl #ru #pt #dataset-lextreme #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

distilbert-base-multilingual-cased-mapa\_coarse-ner
===================================================


This model is a fine-tuned version of distilbert-base-multilingual-cased on the lextreme dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0882
* Precision: 0.7191
* Recall: 0.6453
* F1: 0.6802
* Accuracy: 0.9879


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #name-entity-recognition #legal #en #fr #it #es #de #nl #pl #ru #pt #dataset-lextreme #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #name-entity-recognition #legal #en #fr #it #es #de #nl #pl #ru #pt #dataset-lextreme #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu117
* Datasets 2.9.0
* Tokenizers 0.13.2