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# roberta-base-rte

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5446
- Accuracy: 0.7978

## 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: 1e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 156  | 0.7023          | 0.4729   |
| No log        | 2.0   | 312  | 0.6356          | 0.6895   |
| No log        | 3.0   | 468  | 0.5177          | 0.7617   |
| 0.6131        | 4.0   | 624  | 0.6238          | 0.7473   |
| 0.6131        | 5.0   | 780  | 0.5446          | 0.7978   |
| 0.6131        | 6.0   | 936  | 0.9697          | 0.7545   |
| 0.2528        | 7.0   | 1092 | 1.1004          | 0.7690   |
| 0.2528        | 8.0   | 1248 | 1.1937          | 0.7726   |
| 0.2528        | 9.0   | 1404 | 1.3313          | 0.7726   |
| 0.1073        | 10.0  | 1560 | 1.3534          | 0.7726   |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1

{"language": ["en"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-rte", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE RTE", "type": "glue", "args": "rte"}, "metrics": [{"type": "accuracy", "value": 0.7978339350180506, "name": "Accuracy"}]}, {"task": {"type": "natural-language-inference", "name": "Natural Language Inference"}, "dataset": {"name": "glue", "type": "glue", "config": "rte", "split": "validation"}, "metrics": [{"type": "accuracy", "value": 0.7906137184115524, "name": "Accuracy", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWVhOWZkNGYyMWRmNzdmZTM5MTVmNzFhNjVlMzA1NWU4YjJjODk5ZjM4MTY1Yjg0MTc0MmRmZTNkMzIwZDAzNyIsInZlcnNpb24iOjF9.nFZpFXDSLEIcO-_Z43_5b08GIVQiU9hFUEZpTftW3h6_zqIYZSuM7jOIuDYS3YYWMz42NoH_kosEpJg7TK15Bg"}, {"type": "precision", "value": 0.7552447552447552, "name": "Precision", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDYxZTkzZjk1NDU0MjhmNzYxM2IzNzJjNjE1Y2UxYTQ0MTJmNjJlMmUzNGY3MDdiMDAyZjQ2MmE4ODExYjYxNiIsInZlcnNpb24iOjF9.98rxE2rgU5ECIv4MGzMnaPRRYg3kGLsG4pZbMuYeAFEfXqBU1K0i_G-_cU7oxIqGypNmMhYVhVxZfC7wS_saAw"}, {"type": "recall", "value": 0.8244274809160306, "name": "Recall", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTNhNDZiZjMzOWM0ZGJkODMzM2VmOGYxMTYyZDNjYTgwN2NiMDFlOGI4NzM5NjQ5ODc4MWM2YmM5MTZjMWFiOCIsInZlcnNpb24iOjF9.C9aEgIz392h-zFSd98CSmzQ7Y6N0Xq3VmGIMEq9aP3dQPPrtUfl9Ms_QMSgSyWMPDYHup3SAGAP0JmkiVeOoBg"}, {"type": "auc", "value": 0.8564258078008994, "name": "AUC", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNWVlNGVhOTRkNjUxMGMwZmE0YzBjZDQ0YzQ0ODRmYTc0YjI0MDQ2NTNkOWQ2YjU3MmI5NzI4ZWIwMzBlNTQ1NyIsInZlcnNpb24iOjF9.hSyJjOktSt3AItNnVtgWO9jgHwtNbhv4_KrWEV1r_ywopvbpNmSG4yzaI9PZ_bQQ-4ZSmFM8zUYxCl656TWoDQ"}, {"type": "f1", "value": 0.7883211678832117, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGI4Mzk1MTkyZGJkZjQ1MWZkZDIyZTA3OTU0YmZhNjI4NGUxMjk4ZGZhNjZkN2JmZWRmZGU3OWM5Zjc0ODg4NyIsInZlcnNpb24iOjF9.gkQh5Y4dm8NimTtI0i-gHAYTxFRNlOtdgz-NJW8EvNKeFNWYXqa495Q-KEnSBRv88RKiNQXBp-3fyttjhX2HCw"}, {"type": "loss", "value": 0.5560466051101685, "name": "loss", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjczNTgxODRlN2Q4NmUyOTdjNzE0ZTZkOWVjZDgzNTdhODAyNGVkM2M1M2I4MGM2ZWMyMDE0ODdhMzQ0N2E1NCIsInZlcnNpb24iOjF9.TfXjqAGtiIQ62HzMkEQmKMMcL9a9bvfBTJARVmTPlIdOOxxF-xuVLXSyFqq2ajhDJXmUEETXBcFzSon_zbHTCQ"}]}]}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer en dataset:glue license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

roberta-base-rte
================


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


* Loss: 0.5446
* Accuracy: 0.7978


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: 1e-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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #dataset-glue #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-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 4.20.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.1.0
* Tokenizers 0.12.1