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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-63 | ed71b5008fc38974652a46f32e52084423957079 | 2021-08-01T08:43:57.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-avg-sst2-2e-5-63 | 5 | null | transformers | 16,300 | Entry not found |
TehranNLP-org/bert-base-uncased-cls-hatexplain | 29993d30bb3c9d06f8e28ca8b56d5088f8290121 | 2022-05-02T14:26:26.000Z | [
"pytorch",
"tf",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-cls-hatexplain | 5 | null | transformers | 16,301 | Entry not found |
TehranNLP-org/bert-base-uncased-qqp-2e-5-42 | 830e2f5dd4e063172c98668d951c74f9eb4d3eef | 2021-08-20T05:11:28.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/bert-base-uncased-qqp-2e-5-42 | 5 | null | transformers | 16,302 | Entry not found |
TehranNLP-org/electra-base-avg-cola-2e-5-21 | 70c82c2b6168d8f233526e332d3ab9b79a533b63 | 2021-07-23T19:00:00.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-cola-2e-5-21 | 5 | null | transformers | 16,303 | Entry not found |
TehranNLP-org/electra-base-avg-cola-2e-5-42 | 4b0c88fee4f30d56380e1de0c24e417eff5cce92 | 2021-07-23T19:22:45.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-cola-2e-5-42 | 5 | null | transformers | 16,304 | Entry not found |
TehranNLP-org/electra-base-avg-cola | 627b64187f985d6363a120deffdcf885db684a2d | 2021-06-27T21:07:40.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-cola | 5 | null | transformers | 16,305 | The uploaded model is from epoch 9 with Matthews Correlation of 66.77
"best_metric": 0.667660908939119,<br>
"best_model_checkpoint": "/content/output_dir/checkpoint-2412",<br>
"epoch": 10.0,<br>
"global_step": 2680,<br>
"is_hyper_param_search": false,<br>
"is_local_process_zero": true,<br>
"is_world_process_zero": true,<br>
"max_steps": 2680,<br>
"num_train_epochs": 10,<br>
"total_flos": 7189983634007040.0,<br>
"trial_name": null,<br>
"trial_params": null<br>
<table class="table table-bordered table-hover table-condensed">
<thead><tr><th title="Field #1">epoch</th>
<th title="Field #2">eval_loss</th>
<th title="Field #3">eval_matthews_correlation</th>
<th title="Field #4">eval_runtime</th>
<th title="Field #5">eval_samples_per_second</th>
<th title="Field #6">eval_steps_per_second</th>
<th title="Field #7">step</th>
<th title="Field #8">learning_rate</th>
<th title="Field #9">loss</th>
</tr></thead>
<tbody><tr>
<td align="right">1</td>
<td align="right">0.5115634202957153</td>
<td align="right">0.5385290213636863</td>
<td align="right">7.985</td>
<td align="right">130.62</td>
<td align="right">16.406</td>
<td align="right">268</td>
<td align="right">0.00009280492497114274</td>
<td align="right">0.4622</td>
</tr>
<tr>
<td align="right">2</td>
<td align="right">0.4201788902282715</td>
<td align="right">0.6035894895952164</td>
<td align="right">8.0283</td>
<td align="right">129.916</td>
<td align="right">16.317</td>
<td align="right">536</td>
<td align="right">0.00008249326664101577</td>
<td align="right">0.2823</td>
</tr>
<tr>
<td align="right">3</td>
<td align="right">0.580650806427002</td>
<td align="right">0.5574138665741355</td>
<td align="right">8.1314</td>
<td align="right">128.268</td>
<td align="right">16.11</td>
<td align="right">804</td>
<td align="right">0.00007218160831088881</td>
<td align="right">0.1804</td>
</tr>
<tr>
<td align="right">4</td>
<td align="right">0.4439031779766083</td>
<td align="right">0.6557697896854868</td>
<td align="right">8.1435</td>
<td align="right">128.078</td>
<td align="right">16.087</td>
<td align="right">1072</td>
<td align="right">0.00006186994998076183</td>
<td align="right">0.1357</td>
</tr>
<tr>
<td align="right">5</td>
<td align="right">0.5736830830574036</td>
<td align="right">0.6249925495853809</td>
<td align="right">8.0533</td>
<td align="right">129.512</td>
<td align="right">16.267</td>
<td align="right">1340</td>
<td align="right">0.00005155829165063486</td>
<td align="right">0.0913</td>
</tr>
<tr>
<td align="right">6</td>
<td align="right">0.7729296684265137</td>
<td align="right">0.6188970025554703</td>
<td align="right">8.081</td>
<td align="right">129.068</td>
<td align="right">16.211</td>
<td align="right">1608</td>
<td align="right">0.000041246633320507885</td>
<td align="right">0.065</td>
</tr>
<tr>
<td align="right">7</td>
<td align="right">0.7351673245429993</td>
<td align="right">0.6405767700619004</td>
<td align="right">8.1372</td>
<td align="right">128.176</td>
<td align="right">16.099</td>
<td align="right">1876</td>
<td align="right">0.00003093497499038092</td>
<td align="right">0.0433</td>
</tr>
<tr>
<td align="right">8</td>
<td align="right">0.7900031208992004</td>
<td align="right">0.6565021466238845</td>
<td align="right">8.1095</td>
<td align="right">128.615</td>
<td align="right">16.154</td>
<td align="right">2144</td>
<td align="right">0.000020623316660253942</td>
<td align="right">0.0199</td>
</tr>
<tr>
<td align="right">9</td>
<td align="right">0.8539554476737976</td>
<td align="right">0.667660908939119</td>
<td align="right">8.1204</td>
<td align="right">128.442</td>
<td align="right">16.132</td>
<td align="right">2412</td>
<td align="right">0.000010311658330126971</td>
<td align="right">0.0114</td>
</tr>
<tr>
<td align="right">10</td>
<td align="right">0.9261117577552795</td>
<td align="right">0.660301076782038</td>
<td align="right">8.0088</td>
<td align="right">130.231</td>
<td align="right">16.357</td>
<td align="right">2680</td>
<td align="right">0</td>
<td align="right">0.0066</td>
</tr>
</tbody></table> |
TehranNLP-org/electra-base-avg-mnli-2e-5-63 | 10d567724a17c7c077ecb11ae14f38dfa9381de3 | 2021-07-22T08:47:05.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-mnli-2e-5-63 | 5 | null | transformers | 16,306 | Entry not found |
TehranNLP-org/electra-base-avg-mnli-2e-5 | d2d0b134a1f6193fa297ea6edfb9de5de1d65525 | 2021-07-09T13:14:53.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-mnli-2e-5 | 5 | null | transformers | 16,307 | Entry not found |
TehranNLP-org/electra-base-avg-sst2-2e-5-42 | c43290699f62e6876bcf270546530f60b2ab2bb1 | 2021-07-31T15:00:19.000Z | [
"pytorch",
"electra",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/electra-base-avg-sst2-2e-5-42 | 5 | null | transformers | 16,308 | Entry not found |
TehranNLP-org/roberta-base-mrpc-2e-5-42 | 74bf5db65a0d331399d123ae92c8183680f54b61 | 2021-08-18T18:39:16.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/roberta-base-mrpc-2e-5-42 | 5 | null | transformers | 16,309 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-42 | 708878ada9f3087628f8a158b0dc8e4573e90f23 | 2021-07-23T14:33:01.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-cola-2e-5-42 | 5 | null | transformers | 16,310 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5-21 | 8b7f89d80160e805d971c829898452b07dc9bc10 | 2021-07-21T18:25:47.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5-21 | 5 | null | transformers | 16,311 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5-63 | 485315e8478910d09c77fbee2836a3df05d9e33e | 2021-07-22T17:29:57.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5-63 | 5 | null | transformers | 16,312 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5 | 2902ccdcdb0a9442f4a003265050091cae8cff7a | 2021-07-09T12:17:44.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-mnli-2e-5 | 5 | null | transformers | 16,313 | Entry not found |
TehranNLP-org/xlnet-base-cased-avg-mnli | 64d4525b96435622896a858e799e37653e5e8e06 | 2021-07-06T18:34:10.000Z | [
"pytorch",
"xlnet",
"text-classification",
"transformers"
]
| text-classification | false | TehranNLP-org | null | TehranNLP-org/xlnet-base-cased-avg-mnli | 5 | null | transformers | 16,314 | Entry not found |
Tejas3/distillbert_base_uncased_80 | 84d682bf851a51585050a879a885c9d9a3362923 | 2021-07-06T12:26:22.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | Tejas3 | null | Tejas3/distillbert_base_uncased_80 | 5 | null | transformers | 16,315 | Entry not found |
TomO/xlm-roberta-base-finetuned-marc-en | dd5b669d7079a87ba5ccc1df5fa147682f93a0e4 | 2021-12-16T14:31:13.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | TomO | null | TomO/xlm-roberta-base-finetuned-marc-en | 5 | null | transformers | 16,316 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- 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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9237
- Mae: 0.5122
## 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: 16
- 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 | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1089 | 1.0 | 235 | 0.9380 | 0.4878 |
| 0.9546 | 2.0 | 470 | 0.9237 | 0.5122 |
### Framework versions
- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
TransQuest/microtransquest-en_de-wiki | d3fd4a0ff7fe2fb1c3e5809428b6a3d5ae27d75b | 2021-06-04T08:21:18.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"en-de",
"transformers",
"Quality Estimation",
"microtransquest",
"license:apache-2.0",
"autotrain_compatible"
]
| token-classification | false | TransQuest | null | TransQuest/microtransquest-en_de-wiki | 5 | null | transformers | 16,317 | ---
language: en-de
tags:
- Quality Estimation
- microtransquest
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
from transquest.algo.word_level.microtransquest.run_model import MicroTransQuestModel
import torch
model = MicroTransQuestModel("xlmroberta", "TransQuest/microtransquest-en_de-wiki", labels=["OK", "BAD"], use_cuda=torch.cuda.is_available())
source_tags, target_tags = model.predict([["if not , you may not be protected against the diseases . ", "ja tā nav , Jūs varat nepasargāt no slimībām . "]])
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/monotransquest-hter-en_cs-pharmaceutical | 8ff222e3b902defbe930651fd67dabb339162a2e | 2021-06-04T08:01:17.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-cs",
"transformers",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0"
]
| text-classification | false | TransQuest | null | TransQuest/monotransquest-hter-en_cs-pharmaceutical | 5 | null | transformers | 16,318 | ---
language: en-cs
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-hter-en_cs-pharmaceutical", num_labels=1, use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/monotransquest-hter-en_lv-it-smt | d2064110b7b7a907d79317583c88f65d9399044b | 2021-06-04T08:05:12.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"en-lv",
"transformers",
"Quality Estimation",
"monotransquest",
"hter",
"license:apache-2.0"
]
| text-classification | false | TransQuest | null | TransQuest/monotransquest-hter-en_lv-it-smt | 5 | null | transformers | 16,319 | ---
language: en-lv
tags:
- Quality Estimation
- monotransquest
- hter
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel
model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-hter-en_lv-it-smt", num_labels=1, use_cuda=torch.cuda.is_available())
predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/siamesetransquest-da-multilingual | a623e018b04867b506588f93238156183e74a6b8 | 2021-06-04T11:15:44.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"multilingual-multilingual",
"transformers",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0"
]
| feature-extraction | false | TransQuest | null | TransQuest/siamesetransquest-da-multilingual | 5 | null | transformers | 16,320 | ---
language: multilingual-multilingual
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-multilingual")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/siamesetransquest-da-ne_en-wiki | ff2fca29a8fa21e3cffdec1f0eb374ebb855c361 | 2021-06-04T11:20:50.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"ne-en",
"transformers",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0"
]
| feature-extraction | false | TransQuest | null | TransQuest/siamesetransquest-da-ne_en-wiki | 5 | null | transformers | 16,321 | ---
language: ne-en
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-ne_en-wiki")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TransQuest/siamesetransquest-da-ro_en-wiki | 51e12e62da0498c08d6498c04c76e342a9ffd579 | 2021-06-04T08:14:24.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"ro-en",
"transformers",
"Quality Estimation",
"siamesetransquest",
"da",
"license:apache-2.0"
]
| feature-extraction | false | TransQuest | null | TransQuest/siamesetransquest-da-ro_en-wiki | 5 | null | transformers | 16,322 | ---
language: ro-en
tags:
- Quality Estimation
- siamesetransquest
- da
license: apache-2.0
---
# TransQuest: Translation Quality Estimation with Cross-lingual Transformers
The goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.
With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task in [WMT 2020](http://www.statmt.org/wmt20/quality-estimation-task.html). TransQuest outperforms current open-source quality estimation frameworks such as [OpenKiwi](https://github.com/Unbabel/OpenKiwi) and [DeepQuest](https://github.com/sheffieldnlp/deepQuest).
## Features
- Sentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.
- Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.
- Outperform current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.
- Pre-trained quality estimation models for fifteen language pairs are available in [HuggingFace.](https://huggingface.co/TransQuest)
## Installation
### From pip
```bash
pip install transquest
```
### From Source
```bash
git clone https://github.com/TharinduDR/TransQuest.git
cd TransQuest
pip install -r requirements.txt
```
## Using Pre-trained Models
```python
import torch
from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel
model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-ro_en-wiki")
predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]])
print(predictions)
```
## Documentation
For more details follow the documentation.
1. **[Installation](https://tharindudr.github.io/TransQuest/install/)** - Install TransQuest locally using pip.
2. **Architectures** - Checkout the architectures implemented in TransQuest
1. [Sentence-level Architectures](https://tharindudr.github.io/TransQuest/architectures/sentence_level_architectures/) - We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.
2. [Word-level Architecture](https://tharindudr.github.io/TransQuest/architectures/word_level_architecture/) - We have released MicroTransQuest to perform word level quality estimation.
3. **Examples** - We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.
1. [Sentence-level Examples](https://tharindudr.github.io/TransQuest/examples/sentence_level_examples/)
2. [Word-level Examples](https://tharindudr.github.io/TransQuest/examples/word_level_examples/)
4. **Pre-trained Models** - We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-level
1. [Sentence-level Models](https://tharindudr.github.io/TransQuest/models/sentence_level_pretrained/)
2. [Word-level Models](https://tharindudr.github.io/TransQuest/models/word_level_pretrained/)
5. **[Contact](https://tharindudr.github.io/TransQuest/contact/)** - Contact us for any issues with TransQuest
## Citations
If you are using the word-level architecture, please consider citing this paper which is accepted to [ACL 2021](https://2021.aclweb.org/).
```bash
@InProceedings{ranasinghe2021,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics},
year = {2021}
}
```
If you are using the sentence-level architectures, please consider citing these papers which were presented in [COLING 2020](https://coling2020.org/) and in [WMT 2020](http://www.statmt.org/wmt20/) at EMNLP 2020.
```bash
@InProceedings{transquest:2020a,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
year = {2020}
}
```
```bash
@InProceedings{transquest:2020b,
author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan},
title = {TransQuest at WMT2020: Sentence-Level Direct Assessment},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
year = {2020}
}
```
|
TuhinColumbia/dutchpoetrymany | b14e912dbd7e54a0a1e52f37234d2f8c7dcf8a6a | 2021-09-06T17:18:27.000Z | [
"pytorch",
"mbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | TuhinColumbia | null | TuhinColumbia/dutchpoetrymany | 5 | null | transformers | 16,323 | Entry not found |
Unbabel/XLM-R-18L | 441c0cba3edef0e19bbdbc26124b3dcbc877d2be | 2022-01-05T20:49:12.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | false | Unbabel | null | Unbabel/XLM-R-18L | 5 | null | transformers | 16,324 | Entry not found |
Unbabel/XLM-R-4L | 9a4a46f853952b85fc21feb13b926d02a75be992 | 2022-01-05T19:10:49.000Z | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"transformers"
]
| feature-extraction | false | Unbabel | null | Unbabel/XLM-R-4L | 5 | null | transformers | 16,325 | Entry not found |
V3RX2000/distilbert-base-uncased-finetuned-ner | ccf14ef0e43026d56c8721b2ac5f1dc6a3604286 | 2021-10-13T02:30:36.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | V3RX2000 | null | V3RX2000/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 16,326 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9272043367629162
- name: Recall
type: recall
value: 0.9375769101689228
- name: F1
type: f1
value: 0.932361775503393
- name: Accuracy
type: accuracy
value: 0.984193051297123
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.9272
- Recall: 0.9376
- F1: 0.9324
- Accuracy: 0.9842
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2495 | 1.0 | 878 | 0.0701 | 0.9191 | 0.9229 | 0.9210 | 0.9815 |
| 0.0526 | 2.0 | 1756 | 0.0613 | 0.9216 | 0.9350 | 0.9283 | 0.9832 |
| 0.0312 | 3.0 | 2634 | 0.0612 | 0.9272 | 0.9376 | 0.9324 | 0.9842 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
VaibhS/quantized_model_update | d591c8d0a56b02a13f174bebb7965d0fa69a2000 | 2022-01-04T10:38:03.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | VaibhS | null | VaibhS/quantized_model_update | 5 | null | transformers | 16,327 | Entry not found |
Vasudev/discharge_albert | 4961d8f010d95d46663251577f95a76b264d2a52 | 2021-05-17T10:37:47.000Z | [
"pytorch",
"albert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | Vasudev | null | Vasudev/discharge_albert | 5 | null | transformers | 16,328 | Entry not found |
Vibharkchauhan/distilbert-base-uncased-finetuned-ner | 0cb1b54d9a1c0fb40997383505363c454c38f9ca | 2022-01-24T10:30:44.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | Vibharkchauhan | null | Vibharkchauhan/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 16,329 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9192622045504749
- name: Recall
type: recall
value: 0.9310884886452623
- name: F1
type: f1
value: 0.9251375534930251
- name: Accuracy
type: accuracy
value: 0.9823820039080496
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0626
- Precision: 0.9193
- Recall: 0.9311
- F1: 0.9251
- Accuracy: 0.9824
## 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: 16
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2393 | 1.0 | 878 | 0.0732 | 0.9052 | 0.9207 | 0.9129 | 0.9801 |
| 0.0569 | 2.0 | 1756 | 0.0626 | 0.9193 | 0.9311 | 0.9251 | 0.9824 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
Vilnius-Lithuania-iGEM/Albumin | b4ff0481da84a277b5a303cf5f04133690384da8 | 2021-09-13T18:15:57.000Z | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | Vilnius-Lithuania-iGEM | null | Vilnius-Lithuania-iGEM/Albumin | 5 | null | transformers | 16,330 | # Albumin-15s
## Model description
This is a version of [Albert-base-v2](https://huggingface.co/albert-base-v2) for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.
The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.
More information can be found in our [github]() and our iGEMs [wiki]().
## Intended uses & limitations
You can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.
#### How to use
This model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.
```python
from transformers import AutoTokenizer, BertModel
mname = "Vilnius-Lithuania-iGEM/Albumin"
model = BertModel.from_pretrained(mname)
```
To predict batches of sequences you have to employ custom functions shown in [git/prediction.ipynb]()
#### Limitations and bias
It seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).
## Eval results
accuracy : 0.8601
precision: 0.8515
recall : 0.8725
f1 : 0.8618
roc_auc : 0.9388
The score was calculated using sklearn.metrics.
|
Wasabi42/Joker_Model | 1624a5140c7289e1474842c381dd0ad5b7ad5115 | 2022-02-24T01:57:46.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | Wasabi42 | null | Wasabi42/Joker_Model | 5 | null | transformers | 16,331 | Entry not found |
Wataru/sentence-roberta-tiny | 03fc6795eeab903c529dfbecd0d3e0a3aee641bb | 2021-12-06T03:39:45.000Z | [
"pytorch",
"feature-extraction",
"transformers"
]
| feature-extraction | false | Wataru | null | Wataru/sentence-roberta-tiny | 5 | null | transformers | 16,332 | Entry not found |
WikinewsSum/t5-base-with-title-multi-de-wiki-news | 1c49fa27ae4f9863b70bde4f12c417537eff833c | 2021-06-23T10:43:35.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | WikinewsSum | null | WikinewsSum/t5-base-with-title-multi-de-wiki-news | 5 | null | transformers | 16,333 | Entry not found |
WikinewsSum/t5-base-with-title-multi-fr-wiki-news | 959d0282c317f87a9116612591928f9addecf700 | 2021-06-23T10:46:06.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | WikinewsSum | null | WikinewsSum/t5-base-with-title-multi-fr-wiki-news | 5 | null | transformers | 16,334 | Entry not found |
Win-Win-option/RUT5-for-salaries | 10d70cd17e34dd1923f1431ff878d30bc215ca96 | 2021-10-16T14:08:38.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | Win-Win-option | null | Win-Win-option/RUT5-for-salaries | 5 | null | transformers | 16,335 | Entry not found |
Yanjie/message-preamble | 1ce1760fbde7eb67fe697e62b91dfb4fc771a928 | 2022-03-21T18:33:28.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | Yanjie | null | Yanjie/message-preamble | 5 | null | transformers | 16,336 | This is the concierge preamble model. Fined tuned on DistilBert uncased model. |
Zane/Ricky | 06a3764500d9d85866547850884b1f70f2ca5eb8 | 2021-07-29T14:20:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational",
"license:mit"
]
| conversational | false | Zane | null | Zane/Ricky | 5 | null | transformers | 16,337 | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Zane/Ricky")
model = AutoModelWithLMHead.from_pretrained("Zane/Ricky")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("NekuBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
``` |
aXhyra/demo_emotion_42 | e59b2fcc8e0c41342c6123ebf725d3471c997f93 | 2021-12-13T18:13:57.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/demo_emotion_42 | 5 | null | transformers | 16,338 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_emotion_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: F1
type: f1
value: 0.7348035780583043
---
<!-- 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. -->
# demo_emotion_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9818
- F1: 0.7348
## 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: 7.551070618629693e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 204 | 0.7431 | 0.6530 |
| No log | 2.0 | 408 | 0.6943 | 0.7333 |
| 0.5176 | 3.0 | 612 | 0.8456 | 0.7326 |
| 0.5176 | 4.0 | 816 | 0.9818 | 0.7348 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/demo_irony_42 | 480c9db07a03cf28662c0572c8b7cd9b063825b6 | 2021-12-13T17:51:38.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/demo_irony_42 | 5 | null | transformers | 16,339 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: demo_irony_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.685764300192161
---
<!-- 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. -->
# demo_irony_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2905
- F1: 0.6858
## 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: 2.7735294032820418e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 358 | 0.5872 | 0.6786 |
| 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 |
| 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 |
| 0.3417 | 4.0 | 1432 | 1.2905 | 0.6858 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/hate_trained_1234567 | c9e6bbd1d744c149c9817637a231c53d4ce9c02f | 2021-12-12T13:02:26.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/hate_trained_1234567 | 5 | null | transformers | 16,340 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_1234567
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7750768993843997
---
<!-- 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. -->
# hate_trained_1234567
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7912
- F1: 0.7751
## 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: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1234567
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4835 | 1.0 | 563 | 0.4881 | 0.7534 |
| 0.3236 | 2.0 | 1126 | 0.5294 | 0.7610 |
| 0.219 | 3.0 | 1689 | 0.6095 | 0.7717 |
| 0.1409 | 4.0 | 2252 | 0.7912 | 0.7751 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/hate_trained_31415 | 9b522e9daec11d579cd9eb8c8a7f9846377a11e2 | 2021-12-12T12:57:50.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/hate_trained_31415 | 5 | null | transformers | 16,341 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_31415
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7729447444817463
---
<!-- 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. -->
# hate_trained_31415
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8568
- F1: 0.7729
## 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: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 31415
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.482 | 1.0 | 563 | 0.4973 | 0.7672 |
| 0.3316 | 2.0 | 1126 | 0.4931 | 0.7794 |
| 0.2308 | 3.0 | 1689 | 0.7073 | 0.7593 |
| 0.1444 | 4.0 | 2252 | 0.8568 | 0.7729 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/hate_trained_42 | acca7d0a1b77fadec778000f6d796a0dc8228b98 | 2021-12-12T12:46:30.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/hate_trained_42 | 5 | null | transformers | 16,342 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: hate_trained_42
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: hate
metrics:
- name: F1
type: f1
value: 0.7712319060633668
---
<!-- 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. -->
# hate_trained_42
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8994
- F1: 0.7712
## 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: 2.7272339744854407e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4835 | 1.0 | 563 | 0.4855 | 0.7556 |
| 0.3277 | 2.0 | 1126 | 0.5354 | 0.7704 |
| 0.2112 | 3.0 | 1689 | 0.6870 | 0.7751 |
| 0.1384 | 4.0 | 2252 | 0.8994 | 0.7712 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aXhyra/sentiment_temp | f876fc75711c809710e14b6f52f916ab8520336c | 2021-12-11T01:36:38.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | aXhyra | null | aXhyra/sentiment_temp | 5 | null | transformers | 16,343 | Entry not found |
aXhyra/test_irony_trained_test | f966877b19a954c35b2b02a61862c9e57e8711fe | 2021-12-12T17:02:51.000Z | [
"pytorch",
"distilbert",
"text-classification",
"dataset:tweet_eval",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | aXhyra | null | aXhyra/test_irony_trained_test | 5 | null | transformers | 16,344 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- f1
model-index:
- name: test_irony_trained_test
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: irony
metrics:
- name: F1
type: f1
value: 0.6680395323922843
---
<!-- 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. -->
# test_irony_trained_test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7674
- F1: 0.6680
## 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: 9.207906329883037e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 358 | 0.6655 | 0.5924 |
| 0.684 | 2.0 | 716 | 0.6889 | 0.6024 |
| 0.5826 | 3.0 | 1074 | 0.7085 | 0.6488 |
| 0.5826 | 4.0 | 1432 | 0.7674 | 0.6680 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
aapot/wav2vec2-xlsr-300m-finnish-lm | ee8bd1801a504ba85c3ef6c9216d55f326f38520 | 2022-03-28T17:22:08.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"dataset:mozilla-foundation/common_voice_7_0",
"arxiv:2111.09296",
"transformers",
"finnish",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | aapot | null | aapot/wav2vec2-xlsr-300m-finnish-lm | 5 | null | transformers | 16,345 | ---
license: apache-2.0
language: fi
metrics:
- wer
- cer
tags:
- automatic-speech-recognition
- fi
- finnish
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xlsr-300m-finnish-lm
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: fi
metrics:
- name: Test WER
type: wer
value: 8.16
- name: Test CER
type: cer
value: 1.97
---
# Wav2Vec2 XLS-R for Finnish ASR
This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data. Wav2Vec2 XLS-R was introduced in
[this paper](https://arxiv.org/abs/2111.09296) and first released at [this page](https://github.com/pytorch/fairseq/tree/main/examples/wav2vec#wav2vec-20).
This repository also includes Finnish KenLM language model used in the decoding phase with the acoustic model.
**Note**: this model is exactly the same as the [Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm](https://huggingface.co/Finnish-NLP/wav2vec2-xlsr-300m-finnish-lm) model so this model has just been copied/moved to the `Finnish-NLP` Hugging Face organization.
## Model description
Wav2Vec2 XLS-R is Facebook AI's large-scale multilingual pretrained model for speech. It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages.
You can read more about the pretrained model from [this blog](https://ai.facebook.com/blog/xls-r-self-supervised-speech-processing-for-128-languages) and [this paper](https://arxiv.org/abs/2111.09296).
This model is fine-tuned version of the pretrained model (300 million parameter variant) for Finnish ASR.
## Intended uses & limitations
You can use this model for Finnish ASR (speech-to-text) task.
### How to use
Check the [run-finnish-asr-models.ipynb](https://huggingface.co/aapot/wav2vec2-xlsr-300m-finnish-lm/blob/main/run-finnish-asr-models.ipynb) notebook in this repository for an detailed example on how to use this model.
### Limitations and bias
This model was fine-tuned with audio samples which maximum length was 20 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
A vast majority of the data used for fine-tuning was from the Finnish Parliament dataset so this model may not generalize so well to very different domains like common daily spoken Finnish with dialects etc. In addition, audios of the datasets tend to be adult male dominated so this model may not work as well for speeches of children and women, for example.
The Finnish KenLM language model used in the decoding phase has been trained with text data from the audio transcriptions and from a subset of Finnish Wikipedia. Thus, the decoder's language model may not generalize to very different language, for example to spoken daily language with dialects (because especially the Wikipedia contains mostly formal Finnish language). It may be beneficial to train your own KenLM language model for your domain language and use that in the decoding.
## Training data
This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
| Dataset | Hours | % of total hours |
|:------------------------------------------------------------------------------------------------------------------------------ |:--------:|:----------------:|
| [Common Voice 7.0 Finnish train + evaluation + other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
Datasets were filtered to include maximum length of 20 seconds long audio samples.
## Training procedure
This model was trained during [Robust Speech Challenge Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614) organized by Hugging Face. Training was done on a Tesla V100 GPU, sponsored by OVHcloud.
Training script was provided by Hugging Face and it is available [here](https://github.com/huggingface/transformers/blob/main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py). We only modified its data loading for our custom datasets.
For the KenLM language model training, we followed the [blog post tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) provided by Hugging Face. Training data for the 5-gram KenLM were text transcriptions of the audio training data and 100k random samples of cleaned [Finnish Wikipedia](https://huggingface.co/datasets/wikipedia) (August 2021) dataset.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
The pretrained `facebook/wav2vec2-xls-r-300m` model was initialized with following hyperparameters:
- attention_dropout: 0.094
- hidden_dropout: 0.047
- feat_proj_dropout: 0.04
- mask_time_prob: 0.082
- layerdrop: 0.041
- activation_dropout: 0.055
- ctc_loss_reduction: "mean"
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.973 | 0.17 | 500 | 0.5750 | 0.6844 |
| 0.713 | 0.34 | 1000 | 0.3356 | 0.4518 |
| 0.6563 | 0.5 | 1500 | 0.3007 | 0.4039 |
| 0.642 | 0.67 | 2000 | 0.2619 | 0.3674 |
| 0.6203 | 0.84 | 2500 | 0.2488 | 0.3558 |
| 0.6016 | 1.01 | 3000 | 0.2795 | 0.3835 |
| 0.5423 | 1.17 | 3500 | 0.2652 | 0.3310 |
| 0.5639 | 1.34 | 4000 | 0.2479 | 0.3462 |
| 0.586 | 1.51 | 4500 | 0.2409 | 0.3295 |
| 0.5169 | 1.68 | 5000 | 0.2728 | 0.3352 |
| 0.5176 | 1.84 | 5500 | 0.2254 | 0.3149 |
| 0.4983 | 2.01 | 6000 | 0.2169 | 0.3009 |
| 0.4982 | 2.18 | 6500 | 0.2215 | 0.3079 |
| 0.4898 | 2.35 | 7000 | 0.2174 | 0.3023 |
| 0.4922 | 2.51 | 7500 | 0.2217 | 0.3081 |
| 0.5025 | 2.68 | 8000 | 0.2002 | 0.2710 |
| 0.4745 | 2.85 | 8500 | 0.1935 | 0.2783 |
| 0.4377 | 3.02 | 9000 | 0.1859 | 0.2742 |
| 0.4511 | 3.18 | 9500 | 0.2038 | 0.2786 |
| 0.4411 | 3.35 | 10000 | 0.1863 | 0.2651 |
| 0.4501 | 3.52 | 10500 | 0.1948 | 0.2605 |
| 0.4557 | 3.69 | 11000 | 0.1872 | 0.2695 |
| 0.4493 | 3.85 | 11500 | 0.1888 | 0.2632 |
| 0.4047 | 4.02 | 12000 | 0.1818 | 0.2559 |
| 0.4319 | 4.19 | 12500 | 0.1896 | 0.2648 |
| 0.4162 | 4.36 | 13000 | 0.1953 | 0.2595 |
| 0.4046 | 4.52 | 13500 | 0.1864 | 0.2606 |
| 0.4195 | 4.69 | 14000 | 0.1843 | 0.2467 |
| 0.4146 | 4.86 | 14500 | 0.1686 | 0.2450 |
| 0.378 | 5.03 | 15000 | 0.1731 | 0.2401 |
| 0.3792 | 5.19 | 15500 | 0.1676 | 0.2325 |
| 0.3855 | 5.36 | 16000 | 0.1740 | 0.2326 |
| 0.4029 | 5.53 | 16500 | 0.1674 | 0.2345 |
| 0.386 | 5.7 | 17000 | 0.1735 | 0.2280 |
| 0.3811 | 5.86 | 17500 | 0.1692 | 0.2258 |
| 0.3607 | 6.03 | 18000 | 0.1797 | 0.2279 |
| 0.3604 | 6.2 | 18500 | 0.1651 | 0.2206 |
| 0.3362 | 6.37 | 19000 | 0.1627 | 0.2199 |
| 0.3611 | 6.53 | 19500 | 0.1652 | 0.2172 |
| 0.3671 | 6.7 | 20000 | 0.1564 | 0.2140 |
| 0.3769 | 6.87 | 20500 | 0.1525 | 0.2101 |
| 0.3539 | 7.04 | 21000 | 0.1639 | 0.2096 |
| 0.3225 | 7.21 | 21500 | 0.1611 | 0.2087 |
| 0.3323 | 7.37 | 22000 | 0.1633 | 0.2008 |
| 0.3327 | 7.54 | 22500 | 0.1692 | 0.1975 |
| 0.3456 | 7.71 | 23000 | 0.1555 | 0.1991 |
| 0.3058 | 7.88 | 23500 | 0.1590 | 0.1959 |
| 0.3034 | 8.04 | 24000 | 0.1531 | 0.1973 |
| 0.2925 | 8.21 | 24500 | 0.1583 | 0.1978 |
| 0.2967 | 8.38 | 25000 | 0.1546 | 0.1906 |
| 0.2974 | 8.55 | 25500 | 0.1540 | 0.1869 |
| 0.3131 | 8.71 | 26000 | 0.1534 | 0.1850 |
| 0.3306 | 8.88 | 26500 | 0.1482 | 0.1844 |
| 0.2842 | 9.05 | 27000 | 0.1490 | 0.1854 |
| 0.2879 | 9.22 | 27500 | 0.1463 | 0.1799 |
| 0.27 | 9.38 | 28000 | 0.1454 | 0.1798 |
| 0.2874 | 9.55 | 28500 | 0.1504 | 0.1787 |
| 0.2757 | 9.72 | 29000 | 0.1512 | 0.1784 |
| 0.3017 | 9.89 | 29500 | 0.1484 | 0.1800 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
## Evaluation results
Evaluation was done with the [Common Voice 7.0 Finnish test split](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
To evaluate this model, run the `eval.py` script in this repository:
```bash
python3 eval.py --model_id aapot/wav2vec2-xlsr-300m-finnish-lm --dataset mozilla-foundation/common_voice_7_0 --config fi --split test
```
This model (the third row of the table) achieves the following WER (Word Error Rate) and CER (Character Error Rate) results compared to our other models:
| | WER (with LM) | WER (without LM) | CER (with LM) | CER (without LM) |
|-----------------------------------------|---------------|------------------|---------------|------------------|
|aapot/wav2vec2-xlsr-1b-finnish-lm-v2 |**4.09** |**9.73** |**0.88** |**1.65** |
|aapot/wav2vec2-xlsr-1b-finnish-lm |5.65 |13.11 |1.20 |2.23 |
|aapot/wav2vec2-xlsr-300m-finnish-lm |8.16 |17.92 |1.97 |3.36 |
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 |
aarnphm/finetune_emotion_distilroberta | 28d85eb7127fb3e94f12dfcf80e59ed5d9986097 | 2022-02-23T01:58:51.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aarnphm | null | aarnphm/finetune_emotion_distilroberta | 5 | null | transformers | 16,346 | Entry not found |
abdelkader/pegasus-samsum | 633d1e5d630ddb0a45f68f11adec57dd7630edaa | 2022-01-14T21:30:36.000Z | [
"pytorch",
"tensorboard",
"pegasus",
"text2text-generation",
"dataset:samsum",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| text2text-generation | false | abdelkader | null | abdelkader/pegasus-samsum | 5 | null | transformers | 16,347 | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4844
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6936 | 0.54 | 500 | 1.4844 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
abhishek/autonlp-fred2-2682064 | 15eec60f5bf45ed1d9231622a0638d40802fcef7 | 2021-07-30T13:11:02.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:abhishek/autonlp-data-fred2",
"transformers",
"autonlp"
]
| text-classification | false | abhishek | null | abhishek/autonlp-fred2-2682064 | 5 | null | transformers | 16,348 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-fred2
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-fred2-2682064
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
abhishek/autonlp-imdb-roberta-base-3662644 | 7a334902f5d8351db4baaf775183b7e9075817c0 | 2022-02-04T14:25:35.000Z | [
"pytorch",
"roberta",
"text-classification",
"unk",
"dataset:abhishek/autonlp-data-imdb-roberta-base",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | false | abhishek | null | abhishek/autonlp-imdb-roberta-base-3662644 | 5 | null | transformers | 16,349 | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-imdb-roberta-base
co2_eq_emissions: 25.894117734124272
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 3662644
- CO2 Emissions (in grams): 25.894117734124272
## Validation Metrics
- Loss: 0.20277436077594757
- Accuracy: 0.92604
- Precision: 0.9560674830864092
- Recall: 0.89312
- AUC: 0.9814625504000001
- F1: 0.9235223559581421
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-imdb-roberta-base-3662644
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-imdb-roberta-base-3662644", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
adamlin/NCBI_BERT_pubmed_mimic_uncased_large_transformers | 5ebad34642f11f1a96caa2653458b2a23ee98b85 | 2019-12-25T17:08:38.000Z | [
"pytorch",
"transformers"
]
| null | false | adamlin | null | adamlin/NCBI_BERT_pubmed_mimic_uncased_large_transformers | 5 | null | transformers | 16,350 | Entry not found |
adamlin/ml999_wood | 047289659e809a0d9590592ac070132c396841bf | 2021-12-20T16:44:24.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | false | adamlin | null | adamlin/ml999_wood | 5 | null | transformers | 16,351 | Entry not found |
addy88/T5-23-emotions-detections | a973aad4439dcded7964ef106e9c86154ce240a7 | 2022-01-17T12:08:03.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | addy88 | null | addy88/T5-23-emotions-detections | 5 | null | transformers | 16,352 | ### How to use
Here is how to use this model in PyTorch:
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("addy88/T5-23-emotions-detections")
tokenizer = T5Tokenizer.from_pretrained("addy88/T5-23-emotions-detections")
text_to_summarize="emotion: i don't like it this is nonsense."
input_ids = tokenizer.encode(text_to_summarize, return_tensors="pt", add_special_tokens=True)
input_ids = input_ids.to(self.device)
generated_ids = model.generate(
input_ids=input_ids,
num_beams=2,
max_length=512,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
top_p=0.95,
top_k=50,
num_return_sequences=1,
)
preds = [tokenizer.decode(g,skip_special_tokens=True,clean_up_tokenization_spaces=True,)for g in generated_ids]
``` |
addy88/eli5-all-mpnet-base-v2 | 7f5cfab9bb2f77acac3ac71f7cbd15453cd7c771 | 2022-01-14T13:24:40.000Z | [
"pytorch",
"roberta",
"feature-extraction",
"arxiv:1908.10084",
"sentence-transformers",
"sentence-similarity",
"transformers"
]
| sentence-similarity | false | addy88 | null | addy88/eli5-all-mpnet-base-v2 | 5 | null | sentence-transformers | 16,353 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Finetune on [ELI5](https://huggingface.co/datasets/eli5)
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('addy88/eli5-all-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('addy88/eli5-all-mpnet-base-v2')
model = AutoModel.from_pretrained('addy88/eli5-all-mpnet-base-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=addy88/eli5-all-mpnet-base-v2)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 14393 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1439,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
addy88/hubert-base-timit-demo-colab | 6d1b34405db267cbe77c24f21828aa77b6a4b0cd | 2021-12-12T12:13:30.000Z | [
"pytorch",
"tensorboard",
"hubert",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | addy88 | null | addy88/hubert-base-timit-demo-colab | 5 | null | transformers | 16,354 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: hubert-base-timit-demo-colab
results: []
---
<!-- 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. -->
# hubert-base-timit-demo-colab
This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1092
- Wer: 0.1728
## 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: 0.0001
- train_batch_size: 32
- 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_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.4664 | 4.0 | 500 | 2.3026 | 0.9866 |
| 0.8171 | 8.0 | 1000 | 0.0980 | 0.1885 |
| 0.2983 | 12.0 | 1500 | 0.0943 | 0.1750 |
| 0.1769 | 16.0 | 2000 | 0.0990 | 0.1737 |
| 0.1823 | 20.0 | 2500 | 0.1068 | 0.1757 |
| 0.0761 | 24.0 | 3000 | 0.1041 | 0.1719 |
| 0.0993 | 28.0 | 3500 | 0.1092 | 0.1728 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
addy88/wav2vec2-urdu-stt | 225f8cb920cbfa29d20f7a54e7968c6f8c3c7372 | 2021-12-19T15:47:47.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | addy88 | null | addy88/wav2vec2-urdu-stt | 5 | null | transformers | 16,355 | ## Usage
The model can be used directly (without a language model) as follows:
```python
import soundfile as sf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import argparse
def parse_transcription(wav_file):
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-urdu-stt")
model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-urdu-stt")
# load audio
audio_input, sample_rate = sf.read(wav_file)
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
print(transcription)
``` |
aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-roberta-base | 39afb411f83e8de719fa93dc4c6e4e357883ec0a | 2021-11-22T15:17:03.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-iitp_pdt_review-additionalpretrained-roberta-base | 5 | null | transformers | 16,356 | Entry not found |
aditeyabaral/finetuned-iitp_pdt_review-distilbert-hinglish-small | e0cd4a5381930b70921b627e60986a1927ef5d93 | 2021-11-26T17:29:29.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-iitp_pdt_review-distilbert-hinglish-small | 5 | null | transformers | 16,357 | Entry not found |
aditeyabaral/finetuned-iitp_pdt_review-roberta-base | d6064bf6b140bf5fa7f8d70678568844e60aa265 | 2021-11-25T20:49:26.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-iitp_pdt_review-roberta-base | 5 | null | transformers | 16,358 | Entry not found |
aditeyabaral/finetuned-iitp_pdt_review-roberta-hinglish-big | c864b45ece278694dd789ef64776d4df2683693d | 2021-11-26T18:05:10.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-iitp_pdt_review-roberta-hinglish-big | 5 | null | transformers | 16,359 | Entry not found |
aditeyabaral/finetuned-iitpmovie-additionalpretrained-bert-base-cased | 1ebbdfc5497dd1a9c78d9605a13c727ac44fab4a | 2021-11-23T17:24:12.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-iitpmovie-additionalpretrained-bert-base-cased | 5 | null | transformers | 16,360 | Entry not found |
aditeyabaral/finetuned-sail2017-additionalpretrained-bert-base-cased | a1e2dd903b7e963d1d9b96936dff0c02c165d0af | 2021-11-14T15:51:34.000Z | [
"pytorch",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-additionalpretrained-bert-base-cased | 5 | null | transformers | 16,361 | Entry not found |
aditeyabaral/finetuned-sail2017-additionalpretrained-distilbert-base-cased | 8f95adf562e1162bba2dfbe0ac156663ea421f59 | 2021-11-14T15:30:29.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-additionalpretrained-distilbert-base-cased | 5 | null | transformers | 16,362 | Entry not found |
aditeyabaral/finetuned-sail2017-additionalpretrained-indic-bert | b668d2b21d34e3daea16710926e79d6a4b463f2c | 2021-11-14T16:18:10.000Z | [
"pytorch",
"albert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-additionalpretrained-indic-bert | 5 | null | transformers | 16,363 | Entry not found |
aditeyabaral/finetuned-sail2017-additionalpretrained-roberta-base | bac621e711127ecf382364adbbb1d2b0a3a7bf8a | 2021-11-14T15:28:30.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-additionalpretrained-roberta-base | 5 | null | transformers | 16,364 | Entry not found |
aditeyabaral/finetuned-sail2017-additionalpretrained-xlm-roberta-base | 31ec516cb63868ff07d94abbef5a3d59f3983daa | 2021-11-14T15:37:12.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-additionalpretrained-xlm-roberta-base | 5 | null | transformers | 16,365 | Entry not found |
aditeyabaral/finetuned-sail2017-distilbert-base-cased | 9ae7348221c0c63cae75c43c771b08f7046cc46b | 2021-11-14T15:25:13.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-distilbert-base-cased | 5 | null | transformers | 16,366 | Entry not found |
aditeyabaral/finetuned-sail2017-roberta-base | ad2f5ce1679e437cb58b018baca938a859fc1a92 | 2021-11-14T15:23:20.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-roberta-base | 5 | null | transformers | 16,367 | Entry not found |
aditeyabaral/finetuned-sail2017-xlm-roberta-base | 4684e229c1f56edb6a833968aee2e16f6e617d68 | 2021-11-14T15:47:32.000Z | [
"pytorch",
"xlm-roberta",
"text-classification",
"transformers"
]
| text-classification | false | aditeyabaral | null | aditeyabaral/finetuned-sail2017-xlm-roberta-base | 5 | null | transformers | 16,368 | Entry not found |
aditi2222/t5_paraphrase_updated | 147dc4dd84adb913a196fcbe8f846d8dba564d19 | 2021-11-30T07:57:43.000Z | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | aditi2222 | null | aditi2222/t5_paraphrase_updated | 5 | null | transformers | 16,369 | Entry not found |
adp12/cs410finetune1 | 20a716aa165a89275c5dc2e7852ae48e1e0fb563 | 2021-12-09T03:15:54.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | adp12 | null | adp12/cs410finetune1 | 5 | null | transformers | 16,370 | Entry not found |
adriansyahdr/adrBert-base-p2 | 80378d49709b6baef526cd46d50e7f74ff3c1235 | 2021-05-18T23:11:14.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"transformers"
]
| null | false | adriansyahdr | null | adriansyahdr/adrBert-base-p2 | 5 | null | transformers | 16,371 | Entry not found |
aicast/bert_finetuning_test | 43de0e55785c005164942cb716c2f029d961e94f | 2021-05-18T23:17:12.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | aicast | null | aicast/bert_finetuning_test | 5 | null | transformers | 16,372 | Entry not found |
aidj/distilbert-base-uncased-finetuned-ner | 03ad6ebbbf48a70a5ea51b5788928911ef7aa3d3 | 2022-02-07T07:19:58.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | aidj | null | aidj/distilbert-base-uncased-finetuned-ner | 5 | null | transformers | 16,373 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9260322366968425
- name: Recall
type: recall
value: 0.9383599955252265
- name: F1
type: f1
value: 0.9321553592265377
- name: Accuracy
type: accuracy
value: 0.9834146186474335
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0607
- Precision: 0.9260
- Recall: 0.9384
- F1: 0.9322
- Accuracy: 0.9834
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2545 | 1.0 | 878 | 0.0711 | 0.9096 | 0.9214 | 0.9154 | 0.9800 |
| 0.0555 | 2.0 | 1756 | 0.0593 | 0.9185 | 0.9356 | 0.9270 | 0.9827 |
| 0.0297 | 3.0 | 2634 | 0.0607 | 0.9260 | 0.9384 | 0.9322 | 0.9834 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
airKlizz/mt5-base-wikinewssum-spanish | ef63dd1e25ba9d328bb4fad0ae8d86577212fab3 | 2021-12-25T23:19:15.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| summarization | false | airKlizz | null | airKlizz/mt5-base-wikinewssum-spanish | 5 | null | transformers | 16,374 | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-base-wikinewssum-spanish
results: []
---
<!-- 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. -->
# mt5-base-wikinewssum-spanish
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2394
- Rouge1: 7.9732
- Rouge2: 3.5041
- Rougel: 6.6713
- Rougelsum: 7.5229
## 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: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 528 | 2.3707 | 6.687 | 2.9169 | 5.6793 | 6.2978 |
| No log | 2.0 | 1056 | 2.3140 | 7.9518 | 3.4529 | 6.7265 | 7.4984 |
| No log | 3.0 | 1584 | 2.2848 | 7.9708 | 3.5344 | 6.7272 | 7.534 |
| No log | 4.0 | 2112 | 2.2668 | 8.0252 | 3.5323 | 6.7319 | 7.5819 |
| 3.2944 | 5.0 | 2640 | 2.2532 | 8.0143 | 3.534 | 6.7155 | 7.582 |
| 3.2944 | 6.0 | 3168 | 2.2399 | 7.9525 | 3.4849 | 6.6716 | 7.5155 |
| 3.2944 | 7.0 | 3696 | 2.2376 | 7.9405 | 3.4661 | 6.6559 | 7.5043 |
| 3.2944 | 8.0 | 4224 | 2.2394 | 7.9732 | 3.5041 | 6.6713 | 7.5229 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
airKlizz/t5-base-with-title-multi-en-wiki-news | d6ee01f5be4af0baa32c33b3ce7fdc48d7c8e6f8 | 2021-06-23T10:59:29.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | airKlizz | null | airKlizz/t5-base-with-title-multi-en-wiki-news | 5 | null | transformers | 16,375 | Entry not found |
alexbrandsen/ArcheoBERTje | 67f9329ca61ab9756dcb1255c37085caa6fce9b7 | 2021-05-18T23:22:51.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | alexbrandsen | null | alexbrandsen/ArcheoBERTje | 5 | null | transformers | 16,376 | # ArcheoBERTje
A Dutch BERT model for the Archaeology domain
This model is based on the Dutch BERTje model by wietsedv (https://github.com/wietsedv/bertje).
We further finetuned BERTje with a corpus of roughly 60k Dutch excavation reports (~650 million tokens) from the DANS data archive (https://easy.dans.knaw.nl/ui/home). |
ali2066/distilbert-base-uncased-finetuned-sst-2-english-finetuned-argmining | 5f134309fcd55bb011757a3dc3bd65895d498aaf | 2022-02-25T20:27:49.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | ali2066 | null | ali2066/distilbert-base-uncased-finetuned-sst-2-english-finetuned-argmining | 5 | null | transformers | 16,377 | Entry not found |
alireza7/ARMAN-SH-persian-base-parsinlu-textual-entailment | 50e180aae86396406525ca508ac22ab7a7172226 | 2021-09-29T19:19:02.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | alireza7 | null | alireza7/ARMAN-SH-persian-base-parsinlu-textual-entailment | 5 | null | transformers | 16,378 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
alireza7/ARMAN-SS-100-persian-base-perkey-summary | 70022911b9bb0ca5882725b1c267bb0cf9e78d07 | 2021-09-29T19:21:12.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | alireza7 | null | alireza7/ARMAN-SS-100-persian-base-perkey-summary | 5 | null | transformers | 16,379 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
alireza7/ARMAN-SS-80-persian-base-parsinlu-textual-entailment | 586c3428fa12df99920421c0aa63178b80adc796 | 2021-09-29T19:23:20.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | alireza7 | null | alireza7/ARMAN-SS-80-persian-base-parsinlu-textual-entailment | 5 | null | transformers | 16,380 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
alireza7/PEGASUS-persian-base-parsinlu-qqp | b95d38988987f76c5fce5ca97390b7a2f7f843c5 | 2021-09-29T19:25:17.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | alireza7 | null | alireza7/PEGASUS-persian-base-parsinlu-qqp | 5 | null | transformers | 16,381 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
alireza7/TRANSFORMER-persian-base-voa-title | 73cf128592be0055624ac5a65494c42c5cb5500b | 2021-09-29T19:26:59.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | alireza7 | null | alireza7/TRANSFORMER-persian-base-voa-title | 5 | null | transformers | 16,382 | More information about models is available [here](https://github.com/alirezasalemi7/ARMAN). |
allenai/dsp_roberta_base_dapt_news_tapt_ag_115K | 45442e6edbc6c5cdc5fbd13f70a44cd6c40e3db9 | 2021-05-20T13:10:52.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
]
| null | false | allenai | null | allenai/dsp_roberta_base_dapt_news_tapt_ag_115K | 5 | null | transformers | 16,383 | Entry not found |
allenai/dsp_roberta_base_dapt_reviews_tapt_imdb_20000 | e4c43c9dd34f5983b649bcae673c5b15df5453e8 | 2021-05-20T13:15:59.000Z | [
"pytorch",
"jax",
"roberta",
"transformers"
]
| null | false | allenai | null | allenai/dsp_roberta_base_dapt_reviews_tapt_imdb_20000 | 5 | null | transformers | 16,384 | Entry not found |
ami-wav2vec2/wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8 | 3f199022468646fe157f3e5ff604f426fdd5be9b | 2021-11-24T13:10:27.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"en",
"transformers",
"ami",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | ami-wav2vec2 | null | ami-wav2vec2/wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8 | 5 | null | transformers | 16,385 | ---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- ami
- generated_from_trainer
model-index:
- name: wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8
results: []
---
<!-- 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. -->
# wav2vec2-large-lv60-ami_multi-tune_dropout_0.0001_8
This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AMI-IHM dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4880
- Wer: 0.4295
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.8417 | 0.86 | 1000 | 2.6883 | 0.9997 |
| 1.5626 | 1.72 | 2000 | 1.4253 | 0.4517 |
| 1.4476 | 2.59 | 3000 | 1.3356 | 0.4157 |
| 1.3874 | 3.45 | 4000 | 1.2814 | 0.4073 |
| 1.3391 | 4.31 | 5000 | 1.2700 | 0.4044 |
| 1.2983 | 5.17 | 6000 | 1.2423 | 0.3967 |
| 1.2618 | 6.03 | 7000 | 1.2429 | 0.3879 |
| 1.2414 | 6.9 | 8000 | 1.2290 | 0.3878 |
| 1.2286 | 7.76 | 9000 | 1.2301 | 0.3882 |
| 1.2254 | 8.62 | 10000 | 1.2140 | 0.3885 |
| 1.2257 | 9.48 | 11000 | 1.2154 | 0.3840 |
### Framework versions
- Transformers 4.12.0.dev0
- Pytorch 1.9.1
- Datasets 1.12.2.dev0
- Tokenizers 0.10.3
|
anantoj/wav2vec2-adult-child-cls | d4268c509d83d15b1bde05efd00b13ea17d12b4c | 2022-02-23T14:29:03.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| audio-classification | false | anantoj | null | anantoj/wav2vec2-adult-child-cls | 5 | null | transformers | 16,386 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: wav2vec2-adult-child-cls
results: []
---
<!-- 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. -->
# wav2vec2-adult-child-cls
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1713
- Accuracy: 0.9460
- F1: 0.9509
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.323 | 1.0 | 96 | 0.2699 | 0.9026 | 0.9085 |
| 0.2003 | 2.0 | 192 | 0.2005 | 0.9234 | 0.9300 |
| 0.1808 | 3.0 | 288 | 0.1780 | 0.9377 | 0.9438 |
| 0.1537 | 4.0 | 384 | 0.1673 | 0.9441 | 0.9488 |
| 0.1135 | 5.0 | 480 | 0.1713 | 0.9460 | 0.9509 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
|
anas-awadalla/bert-medium-pretrained-finetuned-squad | 9cfffda54a415e7e3b134cf9dee2d0820823468c | 2022-01-27T06:07:11.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| question-answering | false | anas-awadalla | null | anas-awadalla/bert-medium-pretrained-finetuned-squad | 5 | null | transformers | 16,387 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert_medium_pretrain_squad
results: []
---
<!-- 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. -->
# bert_medium_pretrain_squad
This model is a fine-tuned version of [anas-awadalla/bert-medium-pretrained-on-squad](https://huggingface.co/anas-awadalla/bert-medium-pretrained-on-squad) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0973
- "exact_match": 77.95648060548723
- "f1": 85.85300366384631
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-small-finetuned-squad | 7aba00b039646623c5f50e03c284f761b657dc0b | 2022-01-24T19:25:29.000Z | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| question-answering | false | anas-awadalla | null | anas-awadalla/bert-small-finetuned-squad | 5 | null | transformers | 16,388 | ---
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-small-finetuned-squad
results: []
---
<!-- 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. -->
# bert-small-finetuned-squad
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the squad dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3138
- eval_runtime: 46.6577
- eval_samples_per_second: 231.13
- eval_steps_per_second: 14.446
- epoch: 4.0
- step: 22132
{'exact_match': 71.05960264900662, 'f1': 80.8260245470904}
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
anas-awadalla/bert-small-pretrained-finetuned-squad | 82a5c28c328a7ea82d957bf639e38222b753da1e | 2022-01-27T06:09:41.000Z | [
"pytorch",
"bert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| question-answering | false | anas-awadalla | null | anas-awadalla/bert-small-pretrained-finetuned-squad | 5 | null | transformers | 16,389 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-small-pretrained-finetuned-squad
results: []
---
<!-- 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. -->
# bert-small-pretrained-finetuned-squad
This model is a fine-tuned version of [anas-awadalla/bert-small-pretrained-on-squad](https://huggingface.co/anas-awadalla/bert-small-pretrained-on-squad) on the squad dataset.
- "exact_match": 72.20435193945127
- "f1": 81.31832229156294
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat | ba2d357fb43d754b2cb950907dbd81f35ca3a825 | 2021-09-20T15:46:27.000Z | [
"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"dataset:mit_restaurant",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible"
]
| question-answering | false | andi611 | null | andi611/bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat | 5 | null | transformers | 16,390 | ---
language:
- en
license: cc-by-4.0
tags:
- generated_from_trainer
datasets:
- squad_v2
- mit_restaurant
model_index:
- name: bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: squad_v2
type: squad_v2
- task:
name: Token Classification
type: token-classification
dataset:
name: mit_restaurant
type: mit_restaurant
---
<!-- 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. -->
# bert-large-uncased-whole-word-masking-squad2-with-ner-mit-restaurant-with-neg-with-repeat
This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_restaurant datasets.
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
anditya/xlm-roberta-base-finetuned-marc-en | 25301a51800344df2e251ac73aed639f8f88a70e | 2021-10-22T11:18:11.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | anditya | null | anditya/xlm-roberta-base-finetuned-marc-en | 5 | null | transformers | 16,391 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-en
results: []
---
<!-- 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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8885
- Mae: 0.4390
## 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: 16
- 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 | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1089 | 1.0 | 235 | 0.9027 | 0.4756 |
| 0.9674 | 2.0 | 470 | 0.8885 | 0.4390 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
anel/autonlp-cml-412010597 | ff4d72074c7ba21147ef783b134426acdb49fe5e | 2021-12-13T03:11:37.000Z | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:anel/autonlp-data-cml",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | false | anel | null | anel/autonlp-cml-412010597 | 5 | null | transformers | 16,392 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- anel/autonlp-data-cml
co2_eq_emissions: 10.411685187181709
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 412010597
- CO2 Emissions (in grams): 10.411685187181709
## Validation Metrics
- Loss: 0.12585781514644623
- Accuracy: 0.9475446428571429
- Precision: 0.9454660748256183
- Recall: 0.964424320827943
- AUC: 0.990229573862156
- F1: 0.9548511047070125
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/anel/autonlp-cml-412010597
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("anel/autonlp-cml-412010597", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
anirudh21/albert-large-v2-finetuned-mrpc | 9b0636de195a7ae2612195a2406e3906e1a12aa3 | 2022-01-28T04:21:22.000Z | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"transformers"
]
| text-classification | false | anirudh21 | null | anirudh21/albert-large-v2-finetuned-mrpc | 5 | null | transformers | 16,393 | Entry not found |
anirudh21/distilbert-base-uncased-finetuned-cola | 677e73d4925d713d7eb70fe03aa7e3e2783c1055 | 2022-01-12T07:24:56.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | anirudh21 | null | anirudh21/distilbert-base-uncased-finetuned-cola | 5 | null | transformers | 16,394 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5224154837835395
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8623
- Matthews Correlation: 0.5224
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5278 | 1.0 | 535 | 0.5223 | 0.4007 |
| 0.3515 | 2.0 | 1070 | 0.5150 | 0.4993 |
| 0.2391 | 3.0 | 1605 | 0.6471 | 0.5103 |
| 0.1841 | 4.0 | 2140 | 0.7640 | 0.5153 |
| 0.1312 | 5.0 | 2675 | 0.8623 | 0.5224 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anirudh21/distilbert-base-uncased-finetuned-sst2 | e8ee7b975881293dc9d25b4e07561b972b9b2ac2 | 2022-01-12T14:17:06.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | anirudh21 | null | anirudh21/distilbert-base-uncased-finetuned-sst2 | 5 | null | transformers | 16,395 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.908256880733945
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4028
- Accuracy: 0.9083
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.188 | 1.0 | 4210 | 0.3127 | 0.9037 |
| 0.1299 | 2.0 | 8420 | 0.3887 | 0.9048 |
| 0.0845 | 3.0 | 12630 | 0.4028 | 0.9083 |
| 0.0691 | 4.0 | 16840 | 0.3924 | 0.9071 |
| 0.052 | 5.0 | 21050 | 0.5047 | 0.9002 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anirudh21/electra-base-discriminator-finetuned-wnli | 8885ec7ae5e6598f1ef9e118a72dca768a7b0713 | 2022-01-25T04:41:03.000Z | [
"pytorch",
"tensorboard",
"electra",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | anirudh21 | null | anirudh21/electra-base-discriminator-finetuned-wnli | 5 | null | transformers | 16,396 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: electra-base-discriminator-finetuned-wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# electra-base-discriminator-finetuned-wnli
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6893
- Accuracy: 0.5634
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 0.6893 | 0.5634 |
| No log | 2.0 | 80 | 0.7042 | 0.4225 |
| No log | 3.0 | 120 | 0.7008 | 0.3803 |
| No log | 4.0 | 160 | 0.6998 | 0.5634 |
| No log | 5.0 | 200 | 0.7016 | 0.5352 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
anthonymirand/haha_2019_primary_task | aa18314ec77bd9be2dd769c914553bfca3591fe3 | 2021-05-18T23:42:53.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | anthonymirand | null | anthonymirand/haha_2019_primary_task | 5 | null | transformers | 16,397 | Entry not found |
anton-l/wav2vec2-base-finetuned-ks | 4bfb6063bc982ffaadd75a7c2957e0ecb2912330 | 2021-10-21T11:04:30.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"dataset:superb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| audio-classification | false | anton-l | null | anton-l/wav2vec2-base-finetuned-ks | 5 | null | transformers | 16,398 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- 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. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0952
- Accuracy: 0.9823
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7908 | 1.0 | 399 | 0.6776 | 0.9009 |
| 0.3202 | 2.0 | 798 | 0.2061 | 0.9763 |
| 0.221 | 3.0 | 1197 | 0.1257 | 0.9785 |
| 0.1773 | 4.0 | 1596 | 0.0990 | 0.9813 |
| 0.1729 | 5.0 | 1995 | 0.0952 | 0.9823 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
aoryabinin/aoryabinin_gpt_ai_dungeon_ru | 80543ad509ac8b4d8b96cc4ffc804628527061d2 | 2021-06-02T17:08:12.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | aoryabinin | null | aoryabinin/aoryabinin_gpt_ai_dungeon_ru | 5 | null | transformers | 16,399 | Entry not found |
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