README.md

---
language:
- en
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
widget:
- text: She was badly wounded already. Another spear would take her down.
base_model: microsoft/deberta-v3-large
model-index:
- name: deberta-v3-large-mnli-2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE MNLI
      type: glue
      args: mnli
    metrics:
    - type: accuracy
      value: 0.8949349064279902
      name: Accuracy
  - task:
      type: natural-language-inference
      name: Natural Language Inference
    dataset:
      name: glue
      type: glue
      config: mnli
      split: validation_matched
    metrics:
    - type: accuracy
      value: 0.9000509424350484
      name: Accuracy
      verified: true
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    - type: precision
      value: 0.9000452542826349
      name: Precision Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2EyMWYxY2ZlNTFhYWRhNjA4MzYxOTI4NDAzMjQwMmI4MTJmMWE3ZWEzZTQwMmMyZTM1MzIxYWEyYzVhNDlmMCIsInZlcnNpb24iOjF9.iq2CgF4ik1_DjPlbmFgxvscryy1NNQjTatCJhDu95sXMdlWkekPS6on3NyEaSDwptKyuTQiF4wh8WZDrfhO_Dw
    - type: precision
      value: 0.9000509424350484
      name: Precision Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmY5NmE1MjU1Yzg3Mzk3MDJiNGUyMzM5NmYxYjljZjY1OTQ3NWE0MWM2MTZhYjQ4ZWFmY2FkODc4OThkMzIxMCIsInZlcnNpb24iOjF9.yN_8lq_IjeLU1WJknAkoj75MQajxLvsIsf_pOPFT0_Q77Vfhu0AsIdy1WDJcsAw08ziJoNpN_2LGDMBYJmwzCQ
    - type: precision
      value: 0.9014585350976404
      name: Precision Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTBkYWM4YTE3N2Q5ZmY5ZTRiMGQ1MDc5ODk2NjQwZDc0ODNkMjk3MjdjMjRlZDU2Yzk1MTliMzhmNjYzYzY2ZCIsInZlcnNpb24iOjF9.f9_fAM_a9LwSBwFgwaO5rdAYzV3wkhHq6yquugL1djRlbISZdpzZFWfJHcS-fvgMayYsklBK_ezbu0f7u7tyDg
    - type: recall
      value: 0.900253092056111
      name: Recall Macro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTkwZTRmYzhjNDMyMDllNzFiYTNkMDdjN2E2NmEzOTdjMzAxNjdmMzg3OTFmN2IwZTlmYWY5MWQyMDUyNWRlMSIsInZlcnNpb24iOjF9.aWtX33vOHaGpePRZwO9dfTfWoWyXYCVAf8W1AlHXZto6Ve2HX9RLISTsALRMfNzX-7B6LYLh6qzusjf2xQ20Bw
    - type: recall
      value: 0.9000509424350484
      name: Recall Micro
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFhYzVlZjQ3M2YyYjY1NTBiMGI4NmI4MTgwY2QzY2I3YmMyNjc3YmFhMDU1ZjNlY2FkMjQxOTg3YWYyYTU3ZiIsInZlcnNpb24iOjF9.wPD0-SL1vdG3_bi7cKh_hgVwVr1yV6zRYBzpGe6bDEzV5BYb5lCQoAebS5U1o2H4E4qi7zr2YNFEToNCRTqPBA
    - type: recall
      value: 0.9000509424350484
      name: Recall Weighted
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNThmNjQ4MDY2ZTM3NjQyODQzMTZkNjgyMGNkNDE5MDMwOWJmMzhjZmZjNzllYjA4NmJiZDU3MzU3ODE0YjFhMyIsInZlcnNpb24iOjF9.yN9hb5VWX5ICIXdPBc0OD0BFHnzWv8rmmD--OEh6h1agGiRiyCdROo4saN5CQKiVlPBsHPliaoXra45Xi4gVAg
    - type: f1
      value: 0.8997940135019421
      name: F1 Macro
      verified: true
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    - type: f1
      value: 0.9000509424350484
      name: F1 Micro
      verified: true
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    - type: f1
      value: 0.9003949466748086
      name: F1 Weighted
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    - type: loss
      value: 0.6493226289749146
      name: loss
      verified: true
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---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# DeBERTa-v3-large fine-tuned on MNLI

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6763
- Accuracy: 0.8949

## Model description

[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. 

In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa,  our V3 version significantly improves the model performance on downstream tasks.  You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).

Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.

The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters  with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.

## 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: 3e-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
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.3676        | 1.0   | 24544  | 0.3761          | 0.8681   |
| 0.2782        | 2.0   | 49088  | 0.3605          | 0.8881   |
| 0.1986        | 3.0   | 73632  | 0.4672          | 0.8894   |
| 0.1299        | 4.0   | 98176  | 0.5248          | 0.8967   |
| 0.0643        | 5.0   | 122720 | 0.6489          | 0.8999   |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3