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# canine-s-mongolian-ner

This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3588
- Precision: 0.5262
- Recall: 0.5383
- F1: 0.5322
- Accuracy: 0.9073

## 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6287        | 1.0   | 477  | 0.5233          | 0.2908    | 0.1814 | 0.2234 | 0.8471   |
| 0.4898        | 2.0   | 954  | 0.4404          | 0.3727    | 0.2993 | 0.3320 | 0.8677   |
| 0.403         | 3.0   | 1431 | 0.3939          | 0.4498    | 0.3922 | 0.4191 | 0.8832   |
| 0.3416        | 4.0   | 1908 | 0.3720          | 0.4734    | 0.4606 | 0.4669 | 0.8934   |
| 0.2986        | 5.0   | 2385 | 0.3640          | 0.5093    | 0.4745 | 0.4913 | 0.8987   |
| 0.2693        | 6.0   | 2862 | 0.3599          | 0.5126    | 0.5039 | 0.5082 | 0.9012   |
| 0.243         | 7.0   | 3339 | 0.3534          | 0.5009    | 0.5241 | 0.5123 | 0.9026   |
| 0.2262        | 8.0   | 3816 | 0.3582          | 0.5103    | 0.5315 | 0.5207 | 0.9048   |
| 0.213         | 9.0   | 4293 | 0.3534          | 0.5195    | 0.5327 | 0.5260 | 0.9059   |
| 0.2064        | 10.0  | 4770 | 0.3588          | 0.5262    | 0.5383 | 0.5322 | 0.9073   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "canine-s-mongolian-ner", "results": []}]}
transformers pytorch tensorboard canine token-classification generated_from_trainer mn license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #canine #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #region-us 

canine-s-mongolian-ner
======================


This model is a fine-tuned version of facebook/xlm-v-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3588
* Precision: 0.5262
* Recall: 0.5383
* F1: 0.5322
* Accuracy: 0.9073


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: 32
* 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 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* 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 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #canine #token-classification #generated_from_trainer #mn #license-mit #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: 32
* 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 4.28.0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3