Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
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apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.198 | 0.59 | 1000 | 0.2338 | 16.2424 | | 0.0933 | 1.19 | 2000 | 0.2138 | 14.9756 | | 0.082 | 1.78 | 3000 | 0.2024 | 14.2111 | | 0.0452 | 2.38 | 4000 | 0.2065 | 14.3447 |
a428465ce553b7a46ba737861ad6c4d4
apache-2.0
['tabular-classification', 'baseline-trainer']
false
Baseline Model trained on tipsuhtxfu to apply classification on sex **Metrics of the best model:** accuracy 0.647364 average_precision 0.507660 roc_auc 0.625546 recall_macro 0.589832 f1_macro 0.585292 Name: MultinomialNB(), dtype: float64 **See model plot below:** <style>
74b1c46ff94098e20fd58cbdb1a3c7c0
apache-2.0
['tabular-classification', 'baseline-trainer']
false
sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}
d20b161d0f9f0f1566d30684918b531f
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])),(&
0274cf8139ef029d4b1531ca2b38bc61
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;, MultinomialNB())]))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&
94108732e8f5e956124e8cd1657a28f5
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;, MultinomialNB())]))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless total_bill True False False ... False False False tip True False False ... False False False smoker False False False ... False False False day False False False ... False False False time False False False ... False False False size False False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">pipeline: Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&
cfdedc5cc0e4b4253cd8b897acf91799
apache-2.0
['tabular-classification', 'baseline-trainer']
false
x27;, MultinomialNB())])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">MultinomialNB</label><div class="sk-toggleable__content"><pre>MultinomialNB()</pre></div></div></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt
f3af9b34d28df8032fd2f40743d1e23e
apache-2.0
['mobile', 'vison', 'image-classification']
false
Model Details <!-- Give an overview of your model, the relevant research paper, who trained it, etc. --> EfficientFormer-L1, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. This checkpoint of EfficientFormer-L1 was trained for 1000 epochs. - Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren - Language(s): English - License: This model is licensed under the apache-2.0 license - Resources for more information: - [Research Paper](https://arxiv.org/abs/2206.01191) - [GitHub Repo](https://github.com/snap-research/EfficientFormer/) </model_details> <how_to_start>
f2d64cb817f2fb16c9b3373cd51c60af
apache-2.0
['mobile', 'vison', 'image-classification']
false
How to Get Started with the Model Use the code below to get started with the model. ```python import requests import torch from PIL import Image from transformers import EfficientFormerImageProcessor, EfficientFormerForImageClassificationWithTeacher
e1313b272297bdd1d1517d931e35b737
apache-2.0
['mobile', 'vison', 'image-classification']
false
Load preprocessor and pretrained model model_name = "huggingface/efficientformer-l1-300" processor = EfficientFormerImageProcessor.from_pretrained(model_name) model = EfficientFormerForImageClassificationWithTeacher.from_pretrained(model_name)
04c8b210c5b56ca3d0fccc176f1b0cfc
apache-2.0
['mobile', 'vison', 'image-classification']
false
Print the top ImageNet1k class prediction logits = outputs.logits scores = torch.nn.functional.softmax(logits, dim=1) top_pred_class = torch.argmax(scores, dim=1) print(f"Predicted class: {top_pred_class}") ``` </how_to_start> <uses>
e458b8d27a06a8eeb46126e66fd65774
apache-2.0
['generated_from_trainer', 'summarization']
false
mt5-small-finetuned-arxiv-cs-finetuned-arxiv-cs-full This model is a fine-tuned version of [shamikbose89/mt5-small-finetuned-arxiv-cs](https://huggingface.co/shamikbose89/mt5-small-finetuned-arxiv-cs) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4037 - Rouge1: 39.8923 - Rouge2: 20.9831 - Rougel: 35.8642 - Rougelsum: 35.8511
3153b8fd361aa48d40951fbf8523d82d
apache-2.0
['generated_from_trainer', 'summarization']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10
97e550f511357db8ef32f419ebfeb288
apache-2.0
['generated_from_trainer', 'summarization']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 1.9675 | 1.0 | 500 | 1.5573 | 36.4989 | 18.4839 | 33.2984 | 33.2917 | | 1.7523 | 2.0 | 1000 | 1.4972 | 37.7911 | 19.0357 | 33.5725 | 33.6058 | | 1.6611 | 3.0 | 1500 | 1.4593 | 38.5822 | 19.4928 | 34.215 | 34.2531 | | 1.6187 | 4.0 | 2000 | 1.4492 | 39.1219 | 20.8705 | 35.1969 | 35.2255 | | 1.5864 | 5.0 | 2500 | 1.4289 | 39.7304 | 21.0654 | 35.6602 | 35.6667 | | 1.5553 | 6.0 | 3000 | 1.4184 | 40.0696 | 21.0883 | 35.9536 | 35.9132 | | 1.5215 | 7.0 | 3500 | 1.4163 | 39.1956 | 20.6757 | 35.5016 | 35.5196 | | 1.5038 | 8.0 | 4000 | 1.4148 | 39.2373 | 20.3114 | 35.1676 | 35.1532 | | 1.4929 | 9.0 | 4500 | 1.4064 | 39.9249 | 21.0155 | 35.8247 | 35.7937 | | 1.4791 | 10.0 | 5000 | 1.4037 | 39.8923 | 20.9831 | 35.8642 | 35.8511 |
8a47f73eddc1b6647c9dec82d126fc2c
apache-2.0
['generated_from_trainer']
false
wav2vec2-large-xls-r-300m-urdu This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m). It achieves the following results on the evaluation set: - Loss: 0.5285 - Wer: 0.1702
362d8948d16f5218ca31fd2b5d9a8a88
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 35 - mixed_precision_training: Native AMP
fadf07cf7795e4f1b550fd60025cec83
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.9618 | 0.74 | 32 | 15.0745 | 1.0 | | 9.1928 | 1.49 | 64 | 5.9361 | 1.0 | | 4.9307 | 2.23 | 96 | 4.2924 | 1.0 | | 3.8917 | 2.98 | 128 | 3.5873 | 1.0 | | 3.3867 | 3.72 | 160 | 3.2594 | 1.0 | | 3.2107 | 4.47 | 192 | 3.1718 | 1.0 | | 3.1395 | 5.21 | 224 | 3.1281 | 1.0 | | 3.115 | 5.95 | 256 | 3.1238 | 1.0 | | 3.0801 | 6.7 | 288 | 3.0674 | 1.0 | | 2.9725 | 7.44 | 320 | 2.8277 | 1.0 | | 2.4159 | 8.19 | 352 | 1.7186 | 0.9036 | | 1.3377 | 8.93 | 384 | 1.0271 | 0.6433 | | 0.8591 | 9.67 | 416 | 0.8087 | 0.5441 | | 0.726 | 10.42 | 448 | 0.7263 | 0.4634 | | 0.6242 | 11.16 | 480 | 0.6783 | 0.4156 | | 0.5417 | 11.91 | 512 | 0.6611 | 0.4305 | | 0.4784 | 12.65 | 544 | 0.6300 | 0.3926 | | 0.4198 | 13.4 | 576 | 0.5646 | 0.3499 | | 0.3798 | 14.14 | 608 | 0.5919 | 0.3229 | | 0.3356 | 14.88 | 640 | 0.5715 | 0.3369 | | 0.2954 | 15.63 | 672 | 0.5325 | 0.2728 | | 0.264 | 16.37 | 704 | 0.5535 | 0.2689 | | 0.2535 | 17.12 | 736 | 0.5467 | 0.2366 | | 0.2277 | 17.86 | 768 | 0.5219 | 0.2345 | | 0.2141 | 18.6 | 800 | 0.5314 | 0.2487 | | 0.2036 | 19.35 | 832 | 0.5382 | 0.2236 | | 0.2021 | 20.09 | 864 | 0.5038 | 0.1922 | | 0.1676 | 20.84 | 896 | 0.5238 | 0.2033 | | 0.1544 | 21.58 | 928 | 0.5069 | 0.1866 | | 0.1512 | 22.33 | 960 | 0.5045 | 0.1965 | | 0.1512 | 23.07 | 992 | 0.5167 | 0.1862 | | 0.1399 | 23.81 | 1024 | 0.5236 | 0.1840 | | 0.1291 | 24.56 | 1056 | 0.5234 | 0.1957 | | 0.1274 | 25.3 | 1088 | 0.5348 | 0.1943 | | 0.127 | 26.05 | 1120 | 0.4978 | 0.1719 | | 0.1105 | 26.79 | 1152 | 0.5067 | 0.1767 | | 0.1069 | 27.53 | 1184 | 0.5150 | 0.1758 | | 0.1058 | 28.28 | 1216 | 0.5218 | 0.1844 | | 0.0999 | 29.02 | 1248 | 0.5375 | 0.1852 | | 0.0964 | 29.77 | 1280 | 0.5373 | 0.1843 | | 0.0971 | 30.51 | 1312 | 0.5190 | 0.1776 | | 0.0906 | 31.26 | 1344 | 0.5217 | 0.1747 | | 0.0909 | 32.0 | 1376 | 0.5204 | 0.1778 | | 0.0784 | 32.74 | 1408 | 0.5336 | 0.1756 | | 0.0823 | 33.49 | 1440 | 0.5281 | 0.1699 | | 0.0834 | 34.23 | 1472 | 0.5292 | 0.1700 | | 0.0827 | 34.98 | 1504 | 0.5285 | 0.1702 |
0c35ba967ac5878c95808e5f6e1d466f
apache-2.0
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
false
Model description The **roberta-large-bne-capitel-pos** is a Part-of-speech-tagging (POS) model for the Spanish language fine-tuned from the [roberta-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) large model pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text, processed for this work, compiled from the web crawlings performed by the [National Library of Spain (Biblioteca Nacional de España)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.
4b41816cf93287846c96c284212cc475
apache-2.0
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
false
Intended uses and limitations **roberta-large-bne-capitel-pos** model can be used to Part-of-speech-tagging (POS) a text. The model is limited by its training dataset and may not generalize well for all use cases.
3eafca233326198074d46cef9af6d345
apache-2.0
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
false
How to use Here is how to use this model: ```python from transformers import pipeline from pprint import pprint nlp = pipeline("token-classification", model="PlanTL-GOB-ES/roberta-large-bne-capitel-pos") example = "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto." pos_results = nlp(example) pprint(pos_results) ```
f8fa01773906242d081b1d95347160fe
apache-2.0
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
false
Training procedure The model was trained with a batch size of 16 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
591b69e556cb0dd4d1ea4d39b3ff16c6
apache-2.0
['national library of spain', 'spanish', 'bne', 'capitel', 'pos']
false
Evaluation results We evaluated the **roberta-large-bne-capitel-pos** on the CAPITEL-POS test set against standard multilingual and monolingual baselines: | Model | CAPITEL-POS (F1) | | ------------|:----| | roberta-large-bne-capitel-pos | **98.56** | | roberta-base-bne-capitel-pos | 98.46 | | BETO | 98.36 | | mBERT | 98.39 | | BERTIN | 98.47 | | ELECTRA | 98.16 | For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
c36af50c99b28f2524d398178078e735
mit
['generated_from_trainer']
false
nbme-xlnet-large-cased This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7151
322f6d57962d7ec3d3dc25a9d82d9967
mit
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0
3ffd659d81e7afbb748166f8040bc157
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2931 | 1.0 | 1850 | 1.9915 | | 1.9467 | 2.0 | 3700 | 1.7866 | | 1.7983 | 3.0 | 5550 | 1.6919 |
1dea65a732af59eb72f77e42fe2fc386
mit
['generated_from_trainer']
false
xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2527 - F1: 0.8086
c878d81f4b96ba9b47fcb42d0af462a5
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8319 | 1.0 | 70 | 0.3179 | 0.7474 | | 0.2959 | 2.0 | 140 | 0.2695 | 0.7916 | | 0.2036 | 3.0 | 210 | 0.2527 | 0.8086 |
8606c457fc346bdcecb3ba03057ac40b
['apache-2.0']
[]
false
```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer = pre_tokenizer def _tokenize(self, text, *arg, **kwargs): split_tokens = [] for text in self.pre_tokenizer(text): if text in self.vocab: split_tokens.append(text) else: split_tokens.extend(super()._tokenize(text)) return split_tokens model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-tiny-1024') tokenizer = JiebaTokenizer.from_pretrained('Lowin/chinese-bigbird-tiny-1024') ``` https://github.com/LowinLi/chinese-bigbird
6eb4877bb08f8b1cfbcbdfd0d0b33479
apache-2.0
['generated_from_trainer']
false
flan-t5-base-squad-swe2 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the squad_v2_sv dataset. It achieves the following results on the evaluation set: - Loss: 1.4248
3852dceaf3c8968ea0efda574499a6bf
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - num_epochs: 10
34ed3148047c36783fed46d918d66450
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0881 | 1.0 | 890 | 1.6422 | | 1.7772 | 2.0 | 1780 | 1.5586 | | 1.6763 | 3.0 | 2670 | 1.5153 | | 1.6215 | 4.0 | 3560 | 1.4852 | | 1.5912 | 5.0 | 4450 | 1.4629 | | 1.5651 | 6.0 | 5340 | 1.4481 | | 1.5407 | 7.0 | 6230 | 1.4374 | | 1.5278 | 8.0 | 7120 | 1.4308 | | 1.5137 | 9.0 | 8010 | 1.4269 | | 1.5116 | 10.0 | 8900 | 1.4248 |
9726d74c2472e9bfde373dd510e67723
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Model description This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset.
9e129157da7d60d59cdc5e801b1b983d
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Benchmark WER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 15.74 | 25.10 | |with 4-grams LM| 15.37 | 16.09 |
adb402133236ea12939a7651be53ea9c
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Benchmark CER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 9.51 | 9.95 | |with 4-grams LM| 6.91 | 7.15 |
cff9b2f9d92418f2112497c5cb5f7e49
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Evaluation Please use the eval.py file to run the evaluation: ```python python eval.py --model_id vutankiet2901/wav2vec2-large-xlsr-53-ja --dataset mozilla-foundation/common_voice_7_0 --config ja --split test --log_outputs ```
ed9b91f10d334ca874424287703ddb2d
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP
44f23f916004a60e23c8a8a9294092c7
apache-2.0
['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 4.7776 | 4.73 | 1500 | 2.9540 | 0.9772 | 0.8489 | | 1.9076 | 9.46 | 3000 | 0.7146 | 0.5371 | 0.2484 | | 1.507 | 14.2 | 4500 | 0.5843 | 0.4689 | 0.2196 | | 1.3742 | 18.93 | 6000 | 0.5286 | 0.4321 | 0.1988 | | 1.2776 | 23.66 | 7500 | 0.5007 | 0.4056 | 0.1870 | | 1.2003 | 28.39 | 9000 | 0.4676 | 0.3848 | 0.1802 | | 1.1281 | 33.12 | 10500 | 0.4524 | 0.3694 | 0.1720 | | 1.0657 | 37.85 | 12000 | 0.4449 | 0.3590 | 0.1681 | | 1.0129 | 42.59 | 13500 | 0.4266 | 0.3423 | 0.1617 | | 0.9691 | 47.32 | 15000 | 0.4214 | 0.3375 | 0.1587 |
390df9a93e84f5dad798431975aa514a
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Versatile Diffusion V1.0 Model Card We built **Versatile Diffusion (VD), the first unified multi-flow multimodal diffusion framework**, as a step towards **Universal Generative AI**. Versatile Diffusion can natively support image-to-text, image-variation, text-to-image, and text-variation, and can be further extended to other applications such as semantic-style disentanglement, image-text dual-guided generation, latent image-to-text-to-image editing, and more. Future versions will support more modalities such as speech, music, video and 3D. Resources for more information: [GitHub](https://github.com/SHI-Labs/Versatile-Diffusion), [arXiv](https://arxiv.org/abs/2211.08332).
1589ff856665ec788f1f15142d07d45a
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Model Details One single flow of Versatile Diffusion contains a VAE, a diffuser, and a context encoder, and thus handles one task (e.g., text-to-image) under one data type (e.g., image) and one context type (e.g., text). The multi-flow structure of Versatile Diffusion shows in the following diagram: <p align="center"> <img src="https://huggingface.co/shi-labs/versatile-diffusion-model/resolve/main/assets/figures/vd_combined.png" width="99%"> </p> - **Developed by:** Xingqian Xu, Atlas Wang, Eric Zhang, Kai Wang, and Humphrey Shi - **Model type:** Diffusion-based multimodal generation model - **Language(s):** English - **License:** MIT - **Resources for more information:** [GitHub Repository](https://github.com/SHI-Labs/Versatile-Diffusion), [Paper](https://arxiv.org/abs/2211.08332). - **Cite as:** ``` @article{xu2022versatile, title = {Versatile Diffusion: Text, Images and Variations All in One Diffusion Model}, author = {Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi}, year = 2022, url = {https://arxiv.org/abs/2211.08332}, eprint = {2211.08332}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ```
47f7a085f4c5dc6c1f54e96f6c0314ba
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Usage You can use the model both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [SHI-Labs Versatile Diffusion codebase](https://github.com/SHI-Labs/Versatile-Diffusion).
a04104b1173f47d02ef176b2f595fcd5
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
🧨 Diffusers Diffusers let's you both use a unified and more memory-efficient, task-specific pipelines. **Make sure to install `transformers` from `"main"` in order to use this model.**: ``` pip install git+https://github.com/huggingface/transformers ```
99b13344f4410ce3c159647784ec48d4
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
VersatileDiffusionPipeline To use Versatile Diffusion for all tasks, it is recommend to use the [`VersatileDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion
3860bb2a0e1eee01539cfbbeefef89f3
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
! pip install git+https://github.com/huggingface/transformers diffusers torch from diffusers import VersatileDiffusionPipeline import torch import requests from io import BytesIO from PIL import Image pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda")
be979ff1a990757a0c617fc4c578547f
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Task Specific The task specific pipelines load only the weights that are needed onto GPU. You can find all task specific pipelines [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/versatile_diffusion
b3101e6a53508ddf6292212e96c13be3
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Text to Image ```py from diffusers import VersatileDiffusionTextToImagePipeline import torch pipe = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe("an astronaut riding on a horse on mars", generator=generator).images[0] image.save("./astronaut.png") ```
17293878abdf0ece14c08d58567fb4bc
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") pipe = VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) image = pipe(image, generator=generator).images[0] image.save("./car_variation.png") ```
e18b6a89700d8d7b631de422c88c6630
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
download an initial image url = "https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg" response = requests.get(url) image = Image.open(BytesIO(response.content)).convert("RGB") text = "a red car in the sun" pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16) pipe.remove_unused_weights() pipe = pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(0) text_to_image_strength = 0.75 image = pipe(prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator).images[0] image.save("./red_car.png") ```
7a6c0c7a16168891d2c363b829a25cb8
mit
['generation', 'text-to-image', 'image-variation', 'image-to-text', 'image-editing', 'vision']
false
Cautions, Biases, and Content Acknowledgment We would like the raise the awareness of users of this demo of its potential issues and concerns. Like previous large foundation models, Versatile Diffusion could be problematic in some cases, partially due to the imperfect training data and pretrained network (VAEs / context encoders) with limited scope. In its future research phase, VD may do better on tasks such as text-to-image, image-to-text, etc., with the help of more powerful VAEs, more sophisticated network designs, and more cleaned data. So far, we have kept all features available for research testing both to show the great potential of the VD framework and to collect important feedback to improve the model in the future. We welcome researchers and users to report issues with the HuggingFace community discussion feature or email the authors. Beware that VD may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography, and violence. VD was trained on the LAION-2B dataset, which scraped non-curated online images and text, and may contain unintended exceptions as we removed illegal content. VD in this demo is meant only for research purposes.
300dab854020c593d04b02fa67935102
apache-2.0
['generated_from_trainer']
false
finetuned_sentence_itr0_3e-05_editorials_27_02_2022-19_46_22 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0890 - Accuracy: 0.9750 - F1: 0.9873
fc3e310fc7a8bf1cf7d52cb029dbef99
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5
641b271d09069435813cc9f569c14609
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 104 | 0.0485 | 0.9885 | 0.9942 | | No log | 2.0 | 208 | 0.0558 | 0.9857 | 0.9927 | | No log | 3.0 | 312 | 0.0501 | 0.9828 | 0.9913 | | No log | 4.0 | 416 | 0.0593 | 0.9828 | 0.9913 | | 0.04 | 5.0 | 520 | 0.0653 | 0.9828 | 0.9913 |
24da40438aa151975830caa73ffe0b4e
apache-2.0
['generated_from_trainer']
false
bert-base-uncased-finetuned-effectiveFeedback This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001
123305de10cc8ddd7513bc7b054ecea4
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 361 | 0.0003 | | 0.0139 | 2.0 | 722 | 0.0001 | | 0.0002 | 3.0 | 1083 | 0.0001 |
c28e63b8732d0949301bda13555bbe91
gpl-3.0
['bicleaner-ai']
false
Bicleaner AI full model for en-sq Bicleaner AI is a tool that aims at detecting noisy sentence pairs in a parallel corpus. It indicates the likelihood of a pair of sentences being mutual translations (with a value near to 1) or not (with a value near to 0). Sentence pairs considered very noisy are scored with 0. Find out at our repository for further instructions on how to use it: https://github.com/bitextor/bicleaner-ai
3f00d5cdcac9a58d5706f5671d990e2b
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-4']
false
MultiBERTs Seed 4 Checkpoint 300k (uncased) Seed 4 intermediate checkpoint 300k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-4](https://hf.co/multberts-seed-4). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani).
d8e9dec6e4719533261ad43747c38f2c
apache-2.0
['exbert', 'multiberts', 'multiberts-seed-4']
false
How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-4-300k') model = BertModel.from_pretrained("multiberts-seed-4-300k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
c8d225dd594fb5e833e42ac68741ba37
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Wav2Vec2-Large-XLSR-Indonesian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Indonesian Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181). When using this model, make sure that your speech input is sampled at 16kHz.
6c0ce5a68d176547fe32ae33e08f0b86
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "id", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") resampler = torchaudio.transforms.Resample(48_000, 16_000)
c6f785918b3f80fec645fa5f2a732a96
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset[:2]["sentence"]) ```
8086de4c24d27acdf08d60ac6aeddc67
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Evaluation The model can be evaluated as follows on the Indonesian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "id", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000)
03a760ce224fbf54b0b9544551b9e356
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 51.69 %
536bc54c01c990f4caa7f5c3f8004413
apache-2.0
['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week']
false
Training The Artificial Common Voice `train`, `validation`, and ... datasets were used for training. The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition) (will be available soon)
8fd1d74e4f633d1b61ad4451d80f89c8
mit
[]
false
Swedish BERT models for sentiment analysis [Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) model and has been fine-tuned to solve a multi-label sentiment analysis task. The models have been fine-tuned for the sentiments fear and violence. The models output three floats corresponding to the labels "Negative", "Weak sentiment", and "Strong Sentiment" at the respective indexes. The models have been trained on Swedish data with a conversational focus, collected from various internet sources and forums. The models are only trained on Swedish data and only supports inference of Swedish input texts. The models inference metrics for all non-Swedish inputs are not defined, these inputs are considered as out of domain data. The current models are supported at Transformers version >= 4.3.3 and Torch version 1.8.0, compatibility with older versions are not verified.
7d7bce522bfbe12a6a5012d5567f953c
mit
[]
false
Swedish-Sentiment-Fear The model can be imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear") classifier_fear= BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Fear") When the model and tokenizer are initialized the model can be used for inference.
35aead6bde63e0606816343b2dde5d80
mit
[]
false
Verification metrics During training, the model had maximized validation metrics at the following classification breakpoint. | Classification Breakpoint | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.45 | 0.8754 | 0.8618 | 0.8895 |
ca7c269abe7f1f1f30f3ced6949da987
mit
[]
false
Swedish-Sentiment-Violence The model be can imported from the transformers library by running from transformers import BertForSequenceClassification, BertTokenizerFast tokenizer = BertTokenizerFast.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence") classifier_violence = BertForSequenceClassification.from_pretrained("RecordedFuture/Swedish-Sentiment-Violence") When the model and tokenizer are initialized the model can be used for inference.
5e481f9e7bac99501d8855208f4ad7f7
mit
[]
false
Verification metrics During training, the model had maximized validation metrics at the following classification breakpoint. | Classification Breakpoint | F-score | Precision | Recall | |:-------------------------:|:-------:|:---------:|:------:| | 0.35 | 0.7677 | 0.7456 | 0.791 |
8136d1bb5d8747430cd34187987a6b99
apache-2.0
[]
false
distilbert-base-en-ja-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf).
1ebb6de0c6e95f89ef9ba872f1626223
apache-2.0
[]
false
How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-ja-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-ja-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers).
cc1ff0091ff5cd16c4669fad13e3fd5f
apache-2.0
['translation']
false
ita-cat * source group: Italian * target group: Catalan * OPUS readme: [ita-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md) * model: transformer-align * source language(s): ita * target language(s): cat * model: transformer-align * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.eval.txt)
c460805b34446be4c1fc8208802f6183
apache-2.0
['translation']
false
System Info: - hf_name: ita-cat - source_languages: ita - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['it', 'ca'] - src_constituents: {'ita'} - tgt_constituents: {'cat'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-cat/opus-2020-06-16.test.txt - src_alpha3: ita - tgt_alpha3: cat - short_pair: it-ca - chrF2_score: 0.706 - bleu: 52.5 - brevity_penalty: 0.993 - ref_len: 2074.0 - src_name: Italian - tgt_name: Catalan - train_date: 2020-06-16 - src_alpha2: it - tgt_alpha2: ca - prefer_old: False - long_pair: ita-cat - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
8740cf585fd83e4013e5769c232bad67
apache-2.0
['generated_from_trainer']
false
mobilebert_add_GLUE_Experiment_logit_kd_pretrain_stsb This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: nan - Mse: nan
f36311be3a06f925ae24988841aff6c9
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Mse | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0 | 1.0 | 45 | nan | nan | | 0.0 | 2.0 | 90 | nan | nan | | 0.0 | 3.0 | 135 | nan | nan | | 0.0 | 4.0 | 180 | nan | nan | | 0.0 | 5.0 | 225 | nan | nan | | 0.0 | 6.0 | 270 | nan | nan |
3f84f5e6e785b12809ddd0985e99c837
cc-by-4.0
['answer extraction']
false
Model Card of `lmqg/mt5-small-jaquad-ae` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for answer extraction on the [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
a4a1e7608095d096b3f68c4e836042de
cc-by-4.0
['answer extraction']
false
Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ja - **Training data:** [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
38311e7e382bacf97d76621f6b6de686
cc-by-4.0
['answer extraction']
false
model prediction answers = model.generate_a("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-ae") output = pipe("『クマのプーさん』の物語はまず1925年12月24日、『イヴニング・ニュース』紙のクリスマス特集号に短編作品として掲載された。これは『クマのプーさん』の第一章にあたる作品で、このときだけは挿絵をJ.H.ダウドがつけている。その後作品10話と挿絵が整い、刊行に先駆けて「イーヨーの誕生日」のエピソードが1926年8月に『ロイヤルマガジン』に、同年10月9日に『ニューヨーク・イヴニング・ポスト』紙に掲載されたあと、同年10月14日にロンドンで(メシュエン社)、21日にニューヨークで(ダットン社)『クマのプーさん』が刊行された。<hl>前著『ぼくたちがとてもちいさかったころ』がすでに大きな成功を収めていたこともあり、イギリスでは初版は前著の7倍に当たる3万5000部が刷られた。<hl>他方のアメリカでもその年の終わりまでに15万部を売り上げている。ただし依然として人気のあった前著を売り上げで追い越すには数年の時間を要した。") ```
8c1253d3a7b2095cb4429f1419464dd7
cc-by-4.0
['answer extraction']
false
Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_jaquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 23.99 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | AnswerF1Score | 24.01 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | BERTScore | 75.65 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_1 | 30.11 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_2 | 27.39 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_3 | 25.24 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | Bleu_4 | 23.53 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | METEOR | 25.23 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | MoverScore | 62.71 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | | ROUGE_L | 31.89 | default | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) |
f7e0186023a117cf4c994f16de9e066f
cc-by-4.0
['answer extraction']
false
Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_jaquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 32 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-jaquad-ae/raw/main/trainer_config.json).
9000dd432633f3c483c65be3c2006606
cc-by-sa-4.0
['generated_from_trainer']
false
AkeyLegalBert6 This model is a fine-tuned version of [hatemestinbejaia/AkeyLegalBert](https://huggingface.co/hatemestinbejaia/AkeyLegalBert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3634
05eb9e71c1ba9a576ff209cc4da5919d
cc-by-sa-4.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3875 | 1.0 | 18422 | 3.5239 | | 3.44 | 2.0 | 36844 | 3.4214 | | 3.4738 | 3.0 | 55266 | 3.3597 |
b76962e4ff320e6754d74f2c3900955e
mit
['audio', 'speech-translation', 'automatic-speech-recognition']
false
S2T-SMALL-COVOST2-FR-EN-ST `s2t-small-covost2-fr-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
525b5f66f77d4d328ea2e6246cde965c
mit
['audio', 'speech-translation', 'automatic-speech-recognition']
false
Intended uses & limitations This model can be used for end-to-end French speech to English text translation. See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
13c8d09b386648f8722088ced4a66b03
mit
['audio', 'speech-translation', 'automatic-speech-recognition']
false
How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure to install the `torchaudio` package before running this example.* You could either install those as extra speech dependancies with `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly with `pip install torchaudio sentencepiece`. ```python import torch from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration from datasets import load_dataset import soundfile as sf model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-fr-en-st") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-fr-en-st") def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch ds = load_dataset( "patrickvonplaten/librispeech_asr_dummy", "clean", split="validation" ) ds = ds.map(map_to_array) inputs = processor( ds["speech"][0], sampling_rate=48_000, return_tensors="pt" ) generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"]) translation = processor.batch_decode(generated_ids, skip_special_tokens=True) ```
90da0fc0e9e8b97eaa5f106a8ec0bd14
mit
['audio', 'speech-translation', 'automatic-speech-recognition']
false
Training data The s2t-small-covost2-fr-en-st is trained on French-English subset of [CoVoST2](https://github.com/facebookresearch/covost). CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster ST research with the largest ever open dataset
7420e045854abaf193b0993837b31f1b
apache-2.0
['generated_from_trainer']
false
mobilebert_add_GLUE_Experiment_sst2_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.6814 - Accuracy: 0.5562
c265156953f37da49cf2b7e316de69fe
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6662 | 1.0 | 527 | 0.6814 | 0.5562 | | 0.5954 | 2.0 | 1054 | 0.7090 | 0.5493 | | 0.5689 | 3.0 | 1581 | 0.7150 | 0.5596 | | 0.5546 | 4.0 | 2108 | 0.6893 | 0.5539 | | 0.5473 | 5.0 | 2635 | 0.7051 | 0.5872 | | 0.5421 | 6.0 | 3162 | 0.6983 | 0.5872 |
a783934adfce3d6ae6ed083b32d9f643
mit
['generated_from_trainer']
false
bert-base-historic-dutch-cased-squad-nl This model is a fine-tuned version of [dbmdz/bert-base-historic-dutch-cased](https://huggingface.co/dbmdz/bert-base-historic-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5392
010d4edeab1d2401bd098d27d69bc58f
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8534 | 1.0 | 4268 | 1.6793 | | 1.4998 | 2.0 | 8536 | 1.5392 |
dacc795d8b40c7e6814cf0d9faa846c4
apache-2.0
['generated_from_trainer']
false
bert-large-uncased-finetuned-JD_CV This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 7.3896
5cd56fb467791945549bc0564ee21259
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 8.2520 | | No log | 2.0 | 2 | 7.5931 | | No log | 3.0 | 3 | 7.3896 |
2c0fed0c6ac0d24a3e1e1fb34fff2bdc
mit
['generated_from_keras_callback']
false
sachinsahu/Paper-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2563 - Train End Logits Accuracy: 0.9132 - Train Start Logits Accuracy: 0.9306 - Validation Loss: 1.4623 - Validation End Logits Accuracy: 0.5 - Validation Start Logits Accuracy: 0.75 - Epoch: 0
47d1d2123fe05aad657c37369f075820
mit
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32
ba69a596ccd86aad8ae47f1e3867b596
mit
['generated_from_keras_callback']
false
Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.2563 | 0.9132 | 0.9306 | 1.4623 | 0.5 | 0.75 | 0 |
5bd77349ccaef3fbd6060d2fed6d64df
apache-2.0
['translation']
false
eng-gem * source group: English * target group: Germanic languages * OPUS readme: [eng-gem](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gem/README.md) * model: transformer * source language(s): eng * target language(s): afr ang_Latn dan deu enm_Latn fao frr fry gos got_Goth gsw isl ksh ltz nds nld nno nob nob_Hebr non_Latn pdc sco stq swe swg yid * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gem/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gem/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gem/opus2m-2020-08-01.eval.txt)
e89b7aae1c56803230444eebb46e7d22
apache-2.0
['translation']
false
Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-engdeu.eng.deu | 20.9 | 0.521 | | news-test2008-engdeu.eng.deu | 21.1 | 0.511 | | newstest2009-engdeu.eng.deu | 20.5 | 0.516 | | newstest2010-engdeu.eng.deu | 22.5 | 0.526 | | newstest2011-engdeu.eng.deu | 20.5 | 0.508 | | newstest2012-engdeu.eng.deu | 20.8 | 0.507 | | newstest2013-engdeu.eng.deu | 24.6 | 0.534 | | newstest2015-ende-engdeu.eng.deu | 27.9 | 0.569 | | newstest2016-ende-engdeu.eng.deu | 33.2 | 0.607 | | newstest2017-ende-engdeu.eng.deu | 26.5 | 0.560 | | newstest2018-ende-engdeu.eng.deu | 39.4 | 0.648 | | newstest2019-ende-engdeu.eng.deu | 35.0 | 0.613 | | Tatoeba-test.eng-afr.eng.afr | 56.5 | 0.745 | | Tatoeba-test.eng-ang.eng.ang | 6.7 | 0.154 | | Tatoeba-test.eng-dan.eng.dan | 58.0 | 0.726 | | Tatoeba-test.eng-deu.eng.deu | 40.3 | 0.615 | | Tatoeba-test.eng-enm.eng.enm | 1.4 | 0.215 | | Tatoeba-test.eng-fao.eng.fao | 7.2 | 0.304 | | Tatoeba-test.eng-frr.eng.frr | 5.5 | 0.159 | | Tatoeba-test.eng-fry.eng.fry | 19.4 | 0.433 | | Tatoeba-test.eng-gos.eng.gos | 1.0 | 0.182 | | Tatoeba-test.eng-got.eng.got | 0.3 | 0.012 | | Tatoeba-test.eng-gsw.eng.gsw | 0.9 | 0.130 | | Tatoeba-test.eng-isl.eng.isl | 23.4 | 0.505 | | Tatoeba-test.eng-ksh.eng.ksh | 1.1 | 0.141 | | Tatoeba-test.eng-ltz.eng.ltz | 20.3 | 0.379 | | Tatoeba-test.eng.multi | 46.5 | 0.641 | | Tatoeba-test.eng-nds.eng.nds | 20.6 | 0.458 | | Tatoeba-test.eng-nld.eng.nld | 53.4 | 0.702 | | Tatoeba-test.eng-non.eng.non | 0.6 | 0.166 | | Tatoeba-test.eng-nor.eng.nor | 50.3 | 0.679 | | Tatoeba-test.eng-pdc.eng.pdc | 3.9 | 0.189 | | Tatoeba-test.eng-sco.eng.sco | 33.0 | 0.542 | | Tatoeba-test.eng-stq.eng.stq | 2.3 | 0.274 | | Tatoeba-test.eng-swe.eng.swe | 57.9 | 0.719 | | Tatoeba-test.eng-swg.eng.swg | 1.2 | 0.171 | | Tatoeba-test.eng-yid.eng.yid | 7.2 | 0.304 |
73430726dd08bbf533f5734fe1c538e6
apache-2.0
['translation']
false
System Info: - hf_name: eng-gem - source_languages: eng - target_languages: gem - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-gem/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl', 'is', 'lb', 'yi', 'gem'] - src_constituents: {'eng'} - tgt_constituents: {'ksh', 'enm_Latn', 'got_Goth', 'stq', 'dan', 'swe', 'afr', 'pdc', 'gos', 'nno', 'fry', 'gsw', 'fao', 'deu', 'swg', 'sco', 'nob', 'nld', 'isl', 'eng', 'ltz', 'nob_Hebr', 'ang_Latn', 'frr', 'non_Latn', 'yid', 'nds'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gem/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-gem/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: gem - short_pair: en-gem - chrF2_score: 0.6409999999999999 - bleu: 46.5 - brevity_penalty: 0.9790000000000001 - ref_len: 73328.0 - src_name: English - tgt_name: Germanic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: gem - prefer_old: False - long_pair: eng-gem - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
4bc89499681871ed3f2b37b81606c472
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1
cccee7af987677bd0301e486664dd5f4
apache-2.0
['audio', 'speech', 'wav2vec2', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch']
false
Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Russian [Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Russian using a single-speaker dataset.
3bb3456993c7dce7bd0ee0de54dbba78
apache-2.0
['audio', 'speech', 'wav2vec2', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch']
false
Use this model ```python from transformers import AutoTokenizer, Wav2Vec2ForCTC tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-russian") model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-russian") ```
b45ab8dff81388bd46d0c435ae8db60a
apache-2.0
['audio', 'speech', 'wav2vec2', 'Russian-speech-corpus', 'automatic-speech-recognition', 'speech', 'PyTorch']
false
Example test with Common Voice Dataset ```python dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21") resampler = torchaudio.transforms.Resampl(orig_freq=48_000, new_freq=16_000) def map_to_array(batch): speech, _ = torchaudio.load(batch["path"]) batch["speech"] = resampler.forward(speech.squeeze(0)).numpy() batch["sampling_rate"] = resampler.new_freq batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'") return batch ``` ```python ds = dataset.map(map_to_array) result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys())) print(wer.compute(predictions=result["predicted"], references=result["target"])) ```
3ec03ade7be2a9c35d410f6396a02cf4