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# bert-base-multilingual-cased-finetuned-nli

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4681
- Accuracy: 0.8157

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9299        | 0.02  | 200   | 0.8468          | 0.6277   |
| 0.7967        | 0.03  | 400   | 0.7425          | 0.6855   |
| 0.7497        | 0.05  | 600   | 0.7116          | 0.6924   |
| 0.7083        | 0.07  | 800   | 0.6868          | 0.7153   |
| 0.6882        | 0.08  | 1000  | 0.6638          | 0.7289   |
| 0.6944        | 0.1   | 1200  | 0.6476          | 0.7361   |
| 0.6682        | 0.11  | 1400  | 0.6364          | 0.7458   |
| 0.6635        | 0.13  | 1600  | 0.6592          | 0.7337   |
| 0.6423        | 0.15  | 1800  | 0.6120          | 0.7510   |
| 0.6196        | 0.16  | 2000  | 0.5990          | 0.7582   |
| 0.6381        | 0.18  | 2200  | 0.6026          | 0.7538   |
| 0.6276        | 0.2   | 2400  | 0.6054          | 0.7598   |
| 0.6248        | 0.21  | 2600  | 0.6368          | 0.7526   |
| 0.6331        | 0.23  | 2800  | 0.5959          | 0.7655   |
| 0.6142        | 0.24  | 3000  | 0.6117          | 0.7554   |
| 0.6124        | 0.26  | 3200  | 0.6221          | 0.7570   |
| 0.6127        | 0.28  | 3400  | 0.5748          | 0.7695   |
| 0.602         | 0.29  | 3600  | 0.5735          | 0.7598   |
| 0.5923        | 0.31  | 3800  | 0.5609          | 0.7723   |
| 0.5827        | 0.33  | 4000  | 0.5635          | 0.7743   |
| 0.5732        | 0.34  | 4200  | 0.5547          | 0.7771   |
| 0.5757        | 0.36  | 4400  | 0.5629          | 0.7739   |
| 0.5736        | 0.37  | 4600  | 0.5680          | 0.7659   |
| 0.5642        | 0.39  | 4800  | 0.5437          | 0.7871   |
| 0.5763        | 0.41  | 5000  | 0.5589          | 0.7807   |
| 0.5713        | 0.42  | 5200  | 0.5355          | 0.7867   |
| 0.5644        | 0.44  | 5400  | 0.5346          | 0.7888   |
| 0.5727        | 0.46  | 5600  | 0.5519          | 0.7815   |
| 0.5539        | 0.47  | 5800  | 0.5219          | 0.7900   |
| 0.5516        | 0.49  | 6000  | 0.5560          | 0.7795   |
| 0.5539        | 0.51  | 6200  | 0.5544          | 0.7847   |
| 0.5693        | 0.52  | 6400  | 0.5322          | 0.7932   |
| 0.5632        | 0.54  | 6600  | 0.5404          | 0.7936   |
| 0.565         | 0.55  | 6800  | 0.5382          | 0.7880   |
| 0.5555        | 0.57  | 7000  | 0.5364          | 0.7920   |
| 0.5329        | 0.59  | 7200  | 0.5177          | 0.7964   |
| 0.54          | 0.6   | 7400  | 0.5286          | 0.7916   |
| 0.554         | 0.62  | 7600  | 0.5401          | 0.7835   |
| 0.5447        | 0.64  | 7800  | 0.5261          | 0.7876   |
| 0.5438        | 0.65  | 8000  | 0.5032          | 0.8020   |
| 0.5505        | 0.67  | 8200  | 0.5220          | 0.7924   |
| 0.5364        | 0.68  | 8400  | 0.5398          | 0.7876   |
| 0.5317        | 0.7   | 8600  | 0.5310          | 0.7944   |
| 0.5361        | 0.72  | 8800  | 0.5297          | 0.7936   |
| 0.5204        | 0.73  | 9000  | 0.5270          | 0.7940   |
| 0.5189        | 0.75  | 9200  | 0.5193          | 0.7964   |
| 0.5348        | 0.77  | 9400  | 0.5270          | 0.7867   |
| 0.5363        | 0.78  | 9600  | 0.5194          | 0.7924   |
| 0.5184        | 0.8   | 9800  | 0.5298          | 0.7888   |
| 0.5072        | 0.81  | 10000 | 0.4999          | 0.7992   |
| 0.5229        | 0.83  | 10200 | 0.4922          | 0.8108   |
| 0.5201        | 0.85  | 10400 | 0.5019          | 0.7920   |
| 0.5304        | 0.86  | 10600 | 0.4959          | 0.7992   |
| 0.5061        | 0.88  | 10800 | 0.5047          | 0.7980   |
| 0.5291        | 0.9   | 11000 | 0.4974          | 0.8068   |
| 0.5099        | 0.91  | 11200 | 0.4988          | 0.8036   |
| 0.5271        | 0.93  | 11400 | 0.4899          | 0.8028   |
| 0.5211        | 0.95  | 11600 | 0.4866          | 0.8092   |
| 0.4977        | 0.96  | 11800 | 0.5059          | 0.7960   |
| 0.5155        | 0.98  | 12000 | 0.4821          | 0.8084   |
| 0.5061        | 0.99  | 12200 | 0.4763          | 0.8116   |
| 0.4607        | 1.01  | 12400 | 0.5245          | 0.8020   |
| 0.4435        | 1.03  | 12600 | 0.5021          | 0.8032   |
| 0.4289        | 1.04  | 12800 | 0.5219          | 0.8060   |
| 0.4227        | 1.06  | 13000 | 0.5119          | 0.8076   |
| 0.4349        | 1.08  | 13200 | 0.4957          | 0.8104   |
| 0.4331        | 1.09  | 13400 | 0.4914          | 0.8129   |
| 0.4269        | 1.11  | 13600 | 0.4785          | 0.8145   |
| 0.4185        | 1.12  | 13800 | 0.4879          | 0.8161   |
| 0.4244        | 1.14  | 14000 | 0.4834          | 0.8149   |
| 0.4016        | 1.16  | 14200 | 0.5084          | 0.8056   |
| 0.4106        | 1.17  | 14400 | 0.4993          | 0.8052   |
| 0.4345        | 1.19  | 14600 | 0.5029          | 0.8124   |
| 0.4162        | 1.21  | 14800 | 0.4841          | 0.8120   |
| 0.4239        | 1.22  | 15000 | 0.4756          | 0.8189   |
| 0.4215        | 1.24  | 15200 | 0.4957          | 0.8088   |
| 0.4157        | 1.25  | 15400 | 0.4845          | 0.8112   |
| 0.3982        | 1.27  | 15600 | 0.5064          | 0.8048   |
| 0.4056        | 1.29  | 15800 | 0.4827          | 0.8241   |
| 0.4105        | 1.3   | 16000 | 0.4936          | 0.8088   |
| 0.4221        | 1.32  | 16200 | 0.4800          | 0.8129   |
| 0.4029        | 1.34  | 16400 | 0.4790          | 0.8181   |
| 0.4346        | 1.35  | 16600 | 0.4802          | 0.8137   |
| 0.4163        | 1.37  | 16800 | 0.4838          | 0.8213   |
| 0.4106        | 1.39  | 17000 | 0.4905          | 0.8209   |
| 0.4071        | 1.4   | 17200 | 0.4889          | 0.8153   |
| 0.4077        | 1.42  | 17400 | 0.4801          | 0.8165   |
| 0.4074        | 1.43  | 17600 | 0.4765          | 0.8217   |
| 0.4095        | 1.45  | 17800 | 0.4942          | 0.8096   |
| 0.4117        | 1.47  | 18000 | 0.4668          | 0.8225   |
| 0.3991        | 1.48  | 18200 | 0.4814          | 0.8161   |
| 0.4114        | 1.5   | 18400 | 0.4757          | 0.8193   |
| 0.4061        | 1.52  | 18600 | 0.4702          | 0.8209   |
| 0.4104        | 1.53  | 18800 | 0.4814          | 0.8149   |
| 0.3997        | 1.55  | 19000 | 0.4833          | 0.8141   |
| 0.3992        | 1.56  | 19200 | 0.4847          | 0.8169   |
| 0.4021        | 1.58  | 19400 | 0.4893          | 0.8189   |
| 0.4284        | 1.6   | 19600 | 0.4806          | 0.8173   |
| 0.3915        | 1.61  | 19800 | 0.4952          | 0.8092   |
| 0.4122        | 1.63  | 20000 | 0.4917          | 0.8112   |
| 0.4164        | 1.65  | 20200 | 0.4769          | 0.8157   |
| 0.4063        | 1.66  | 20400 | 0.4723          | 0.8141   |
| 0.4087        | 1.68  | 20600 | 0.4701          | 0.8157   |
| 0.4159        | 1.69  | 20800 | 0.4826          | 0.8141   |
| 0.4           | 1.71  | 21000 | 0.4760          | 0.8133   |
| 0.4024        | 1.73  | 21200 | 0.4755          | 0.8161   |
| 0.4201        | 1.74  | 21400 | 0.4728          | 0.8173   |
| 0.4066        | 1.76  | 21600 | 0.4690          | 0.8157   |
| 0.3941        | 1.78  | 21800 | 0.4687          | 0.8181   |
| 0.3987        | 1.79  | 22000 | 0.4735          | 0.8149   |
| 0.4074        | 1.81  | 22200 | 0.4715          | 0.8137   |
| 0.4083        | 1.83  | 22400 | 0.4660          | 0.8181   |
| 0.4107        | 1.84  | 22600 | 0.4699          | 0.8161   |
| 0.3924        | 1.86  | 22800 | 0.4732          | 0.8153   |
| 0.4205        | 1.87  | 23000 | 0.4686          | 0.8177   |
| 0.3962        | 1.89  | 23200 | 0.4688          | 0.8177   |
| 0.3888        | 1.91  | 23400 | 0.4778          | 0.8124   |
| 0.3978        | 1.92  | 23600 | 0.4713          | 0.8145   |
| 0.3963        | 1.94  | 23800 | 0.4704          | 0.8145   |
| 0.408         | 1.96  | 24000 | 0.4674          | 0.8165   |
| 0.4014        | 1.97  | 24200 | 0.4679          | 0.8161   |
| 0.3951        | 1.99  | 24400 | 0.4681          | 0.8157   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xnli"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned-nli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "xnli", "type": "xnli", "config": "en", "split": "train", "args": "en"}, "metrics": [{"type": "accuracy", "value": 0.8156626506024096, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer dataset:xnli license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-multilingual-cased-finetuned-nli
==========================================


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


* Loss: 0.4681
* Accuracy: 0.8157


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.21.0
* Pytorch 1.12.0+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-xnli #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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-xnli #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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu102
* Datasets 2.4.0
* Tokenizers 0.12.1