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# bert-base-cased-qnli

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2569
- Accuracy: 0.9087

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6529        | 0.08  | 500   | 0.4945          | 0.7734   |
| 0.4731        | 0.15  | 1000  | 0.3888          | 0.8406   |
| 0.4113        | 0.23  | 1500  | 0.3605          | 0.8523   |
| 0.4006        | 0.31  | 2000  | 0.3175          | 0.8649   |
| 0.3615        | 0.38  | 2500  | 0.3220          | 0.8678   |
| 0.3662        | 0.46  | 3000  | 0.3198          | 0.8667   |
| 0.3578        | 0.53  | 3500  | 0.2970          | 0.8779   |
| 0.3349        | 0.61  | 4000  | 0.2864          | 0.8834   |
| 0.3548        | 0.69  | 4500  | 0.2664          | 0.8946   |
| 0.3254        | 0.76  | 5000  | 0.2651          | 0.8929   |
| 0.3212        | 0.84  | 5500  | 0.2943          | 0.8817   |
| 0.3195        | 0.92  | 6000  | 0.2630          | 0.8960   |
| 0.3044        | 0.99  | 6500  | 0.2766          | 0.8882   |
| 0.245         | 1.07  | 7000  | 0.3268          | 0.9013   |
| 0.2415        | 1.15  | 7500  | 0.2987          | 0.8991   |
| 0.248         | 1.22  | 8000  | 0.3373          | 0.8812   |
| 0.2328        | 1.3   | 8500  | 0.3474          | 0.8808   |
| 0.2427        | 1.37  | 9000  | 0.2986          | 0.8913   |
| 0.243         | 1.45  | 9500  | 0.2835          | 0.8982   |
| 0.2375        | 1.53  | 10000 | 0.2893          | 0.8909   |
| 0.2253        | 1.6   | 10500 | 0.2942          | 0.8960   |
| 0.2429        | 1.68  | 11000 | 0.2852          | 0.9028   |
| 0.238         | 1.76  | 11500 | 0.2672          | 0.9037   |
| 0.2344        | 1.83  | 12000 | 0.2672          | 0.9021   |
| 0.2368        | 1.91  | 12500 | 0.2754          | 0.9019   |
| 0.2424        | 1.99  | 13000 | 0.2569          | 0.9087   |
| 0.1542        | 2.06  | 13500 | 0.3807          | 0.9026   |
| 0.1416        | 2.14  | 14000 | 0.3915          | 0.8980   |
| 0.1445        | 2.21  | 14500 | 0.4292          | 0.8984   |
| 0.1631        | 2.29  | 15000 | 0.4097          | 0.8971   |
| 0.1512        | 2.37  | 15500 | 0.3880          | 0.9012   |
| 0.1624        | 2.44  | 16000 | 0.4083          | 0.8955   |
| 0.1616        | 2.52  | 16500 | 0.3950          | 0.9039   |
| 0.1587        | 2.6   | 17000 | 0.3579          | 0.9103   |
| 0.1615        | 2.67  | 17500 | 0.3931          | 0.9012   |
| 0.1623        | 2.75  | 18000 | 0.3697          | 0.9059   |
| 0.1687        | 2.83  | 18500 | 0.3473          | 0.9037   |
| 0.1627        | 2.9   | 19000 | 0.3851          | 0.8982   |
| 0.1593        | 2.98  | 19500 | 0.4039          | 0.9019   |
| 0.1136        | 3.05  | 20000 | 0.4835          | 0.9024   |
| 0.0965        | 3.13  | 20500 | 0.5061          | 0.9012   |
| 0.0931        | 3.21  | 21000 | 0.5279          | 0.8991   |
| 0.0993        | 3.28  | 21500 | 0.4856          | 0.9019   |
| 0.1187        | 3.36  | 22000 | 0.4883          | 0.9032   |
| 0.1008        | 3.44  | 22500 | 0.5054          | 0.9015   |
| 0.1013        | 3.51  | 23000 | 0.5025          | 0.9023   |
| 0.1092        | 3.59  | 23500 | 0.4485          | 0.8986   |
| 0.1135        | 3.67  | 24000 | 0.5123          | 0.8977   |
| 0.1042        | 3.74  | 24500 | 0.4884          | 0.9010   |
| 0.1178        | 3.82  | 25000 | 0.4130          | 0.9006   |
| 0.1041        | 3.89  | 25500 | 0.4847          | 0.8951   |
| 0.1025        | 3.97  | 26000 | 0.4706          | 0.8986   |
| 0.0833        | 4.05  | 26500 | 0.5018          | 0.9054   |
| 0.0632        | 4.12  | 27000 | 0.5426          | 0.9055   |
| 0.0613        | 4.2   | 27500 | 0.5629          | 0.9021   |
| 0.0703        | 4.28  | 28000 | 0.5763          | 0.9033   |
| 0.0687        | 4.35  | 28500 | 0.5274          | 0.9039   |
| 0.0707        | 4.43  | 29000 | 0.5682          | 0.9019   |
| 0.0717        | 4.51  | 29500 | 0.5382          | 0.9004   |
| 0.0692        | 4.58  | 30000 | 0.5901          | 0.9010   |
| 0.0701        | 4.66  | 30500 | 0.5817          | 0.8990   |
| 0.0708        | 4.73  | 31000 | 0.5580          | 0.9019   |
| 0.07          | 4.81  | 31500 | 0.5640          | 0.8990   |
| 0.0725        | 4.89  | 32000 | 0.5768          | 0.8993   |
| 0.0701        | 4.96  | 32500 | 0.5289          | 0.9035   |
| 0.0441        | 5.04  | 33000 | 0.6401          | 0.9010   |
| 0.0388        | 5.12  | 33500 | 0.6446          | 0.8991   |
| 0.0417        | 5.19  | 34000 | 0.6327          | 0.9039   |
| 0.039         | 5.27  | 34500 | 0.6385          | 0.9048   |
| 0.0407        | 5.35  | 35000 | 0.6510          | 0.9030   |
| 0.0446        | 5.42  | 35500 | 0.5788          | 0.9030   |
| 0.0422        | 5.5   | 36000 | 0.6723          | 0.9024   |
| 0.04          | 5.58  | 36500 | 0.6602          | 0.9037   |
| 0.0514        | 5.65  | 37000 | 0.6407          | 0.9024   |
| 0.0462        | 5.73  | 37500 | 0.6145          | 0.9048   |
| 0.0479        | 5.8   | 38000 | 0.5881          | 0.9008   |
| 0.0503        | 5.88  | 38500 | 0.6001          | 0.9008   |
| 0.0385        | 5.96  | 39000 | 0.6464          | 0.9052   |
| 0.0436        | 6.03  | 39500 | 0.6683          | 0.9039   |
| 0.0296        | 6.11  | 40000 | 0.6965          | 0.9037   |
| 0.0275        | 6.19  | 40500 | 0.7193          | 0.9039   |
| 0.0258        | 6.26  | 41000 | 0.7229          | 0.9039   |
| 0.0223        | 6.34  | 41500 | 0.6974          | 0.9074   |
| 0.0268        | 6.42  | 42000 | 0.7045          | 0.9028   |
| 0.0297        | 6.49  | 42500 | 0.7289          | 0.9055   |
| 0.0295        | 6.57  | 43000 | 0.6810          | 0.9050   |
| 0.0265        | 6.64  | 43500 | 0.6833          | 0.9065   |
| 0.0268        | 6.72  | 44000 | 0.7155          | 0.9035   |
| 0.0293        | 6.8   | 44500 | 0.7632          | 0.9015   |
| 0.0289        | 6.87  | 45000 | 0.7229          | 0.9033   |
| 0.0286        | 6.95  | 45500 | 0.6671          | 0.9054   |
| 0.024         | 7.03  | 46000 | 0.7659          | 0.9033   |
| 0.0141        | 7.1   | 46500 | 0.7981          | 0.9023   |
| 0.0212        | 7.18  | 47000 | 0.7021          | 0.9088   |
| 0.0183        | 7.26  | 47500 | 0.7122          | 0.9096   |
| 0.0221        | 7.33  | 48000 | 0.7080          | 0.9065   |
| 0.0146        | 7.41  | 48500 | 0.7344          | 0.9074   |
| 0.0181        | 7.48  | 49000 | 0.7273          | 0.9105   |
| 0.0161        | 7.56  | 49500 | 0.7332          | 0.9083   |
| 0.0193        | 7.64  | 50000 | 0.7117          | 0.9094   |
| 0.0165        | 7.71  | 50500 | 0.7797          | 0.9070   |
| 0.0173        | 7.79  | 51000 | 0.7128          | 0.9107   |
| 0.0158        | 7.87  | 51500 | 0.7420          | 0.9110   |
| 0.0157        | 7.94  | 52000 | 0.7198          | 0.9129   |
| 0.0142        | 8.02  | 52500 | 0.7033          | 0.9114   |
| 0.0107        | 8.1   | 53000 | 0.7761          | 0.9109   |
| 0.0122        | 8.17  | 53500 | 0.7777          | 0.9110   |
| 0.013         | 8.25  | 54000 | 0.7880          | 0.9076   |
| 0.0092        | 8.32  | 54500 | 0.8070          | 0.9074   |
| 0.0102        | 8.4   | 55000 | 0.8113          | 0.9055   |
| 0.0099        | 8.48  | 55500 | 0.8153          | 0.9079   |
| 0.0087        | 8.55  | 56000 | 0.8045          | 0.9112   |
| 0.0133        | 8.63  | 56500 | 0.8173          | 0.9081   |
| 0.017         | 8.71  | 57000 | 0.7646          | 0.9101   |
| 0.0093        | 8.78  | 57500 | 0.7681          | 0.9120   |
| 0.0085        | 8.86  | 58000 | 0.8067          | 0.9083   |
| 0.0137        | 8.94  | 58500 | 0.7645          | 0.9107   |
| 0.0099        | 9.01  | 59000 | 0.7742          | 0.9123   |
| 0.0074        | 9.09  | 59500 | 0.8052          | 0.9059   |
| 0.0057        | 9.16  | 60000 | 0.7906          | 0.9132   |
| 0.0046        | 9.24  | 60500 | 0.7915          | 0.9136   |
| 0.0036        | 9.32  | 61000 | 0.8114          | 0.9101   |
| 0.0036        | 9.39  | 61500 | 0.8382          | 0.9087   |
| 0.0079        | 9.47  | 62000 | 0.8013          | 0.9110   |
| 0.0079        | 9.55  | 62500 | 0.8116          | 0.9099   |
| 0.0054        | 9.62  | 63000 | 0.8143          | 0.9110   |
| 0.0036        | 9.7   | 63500 | 0.8138          | 0.9114   |
| 0.0053        | 9.78  | 64000 | 0.8158          | 0.9107   |
| 0.0035        | 9.85  | 64500 | 0.8146          | 0.9112   |
| 0.0051        | 9.93  | 65000 | 0.8168          | 0.9107   |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.7.1
- Datasets 1.18.3
- Tokenizers 0.11.6

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-cased-qnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE QNLI", "type": "glue", "args": "qnli"}, "metrics": [{"type": "accuracy", "value": 0.9086582463847702, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

bert-base-cased-qnli
====================


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


* Loss: 0.2569
* Accuracy: 0.9087


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10.0


### Training results



### Framework versions


* Transformers 4.21.3
* Pytorch 1.7.1
* Datasets 1.18.3
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.7.1
* Datasets 1.18.3
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.21.3
* Pytorch 1.7.1
* Datasets 1.18.3
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10.0### Training results### Framework versions


* Transformers 4.21.3
* Pytorch 1.7.1
* Datasets 1.18.3
* Tokenizers 0.11.6