license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 | bfbe5b5bae95bcf65688cda43b90e322 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-distilbert-base-uncased-label_sexist This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4852 - F1: 0.7874 | a7016c1dd9b22f3439aa073d195d2bfd |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 8 - mixed_precision_training: Native AMP | b9130b014407e8d6631324eb6ad3f8f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4199 | 1.14 | 400 | 0.3911 | 0.7571 | | 0.293 | 2.29 | 800 | 0.3778 | 0.7899 | | 0.2348 | 3.43 | 1200 | 0.4102 | 0.7894 | | 0.1895 | 4.57 | 1600 | 0.4417 | 0.7835 | | 0.1392 | 5.71 | 2000 | 0.4852 | 0.7874 | | 06eee3d957d442cee831a2d85176cb34 |
apache-2.0 | ['generated_from_trainer'] | false | model_syllable_onSet1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1815 - 0 Precision: 1.0 - 0 Recall: 0.9677 - 0 F1-score: 0.9836 - 0 Support: 31 - 1 Precision: 0.9545 - 1 Recall: 1.0 - 1 F1-score: 0.9767 - 1 Support: 21 - 2 Precision: 1.0 - 2 Recall: 1.0 - 2 F1-score: 1.0 - 2 Support: 30 - 3 Precision: 1.0 - 3 Recall: 1.0 - 3 F1-score: 1.0 - 3 Support: 16 - Accuracy: 0.9898 - Macro avg Precision: 0.9886 - Macro avg Recall: 0.9919 - Macro avg F1-score: 0.9901 - Macro avg Support: 98 - Weighted avg Precision: 0.9903 - Weighted avg Recall: 0.9898 - Weighted avg F1-score: 0.9898 - Weighted avg Support: 98 - Wer: 0.7883 - Mtrix: [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | 2fd348d18bc0e3eaf0431a99178b5413 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 200 - num_epochs: 70 - mixed_precision_training: Native AMP | 5d440deb2b73680e1e06c9c3f48bdd60 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | 0 Precision | 0 Recall | 0 F1-score | 0 Support | 1 Precision | 1 Recall | 1 F1-score | 1 Support | 2 Precision | 2 Recall | 2 F1-score | 2 Support | 3 Precision | 3 Recall | 3 F1-score | 3 Support | Accuracy | Macro avg Precision | Macro avg Recall | Macro avg F1-score | Macro avg Support | Weighted avg Precision | Weighted avg Recall | Weighted avg F1-score | Weighted avg Support | Wer | Mtrix | |:-------------:|:-----:|:----:|:---------------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:-----------:|:--------:|:----------:|:---------:|:--------:|:-------------------:|:----------------:|:------------------:|:-----------------:|:----------------------:|:-------------------:|:---------------------:|:--------------------:|:------:|:--------------------------------------------------------------------------------------:| | 1.6949 | 4.16 | 100 | 1.6177 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 1.5778 | 8.33 | 200 | 1.3535 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 1.2861 | 12.49 | 300 | 1.0938 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 0.954 | 16.65 | 400 | 0.9480 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 0.8849 | 20.82 | 500 | 0.9231 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 0.8674 | 24.98 | 600 | 0.8767 | 1.0 | 0.2581 | 0.4103 | 31 | 0.0 | 0.0 | 0.0 | 21 | 0.3333 | 1.0 | 0.5 | 30 | 0.0 | 0.0 | 0.0 | 16 | 0.3878 | 0.3333 | 0.3145 | 0.2276 | 98 | 0.4184 | 0.3878 | 0.2828 | 98 | 0.9655 | [[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]] | | 0.7921 | 29.16 | 700 | 0.7519 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9545 | 1.0 | 0.9767 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9898 | 0.9886 | 0.9919 | 0.9901 | 98 | 0.9903 | 0.9898 | 0.9898 | 98 | 1.0 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 0.7851 | 33.33 | 800 | 0.8212 | 1.0 | 0.9032 | 0.9492 | 31 | 0.84 | 1.0 | 0.9130 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 0.9375 | 0.9677 | 16 | 0.9592 | 0.96 | 0.9602 | 0.9575 | 98 | 0.9657 | 0.9592 | 0.9600 | 98 | 1.0 | [[0, 1, 2, 3], [0, 28, 3, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 1, 0, 15]] | | 0.7657 | 37.49 | 900 | 0.7504 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9130 | 1.0 | 0.9545 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 0.9375 | 0.9677 | 16 | 0.9796 | 0.9783 | 0.9763 | 0.9765 | 98 | 0.9814 | 0.9796 | 0.9798 | 98 | 1.0 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 1, 0, 15]] | | 0.688 | 41.65 | 1000 | 0.6897 | 1.0 | 1.0 | 1.0 | 31 | 0.9130 | 1.0 | 0.9545 | 21 | 1.0 | 0.9667 | 0.9831 | 30 | 1.0 | 0.9375 | 0.9677 | 16 | 0.9796 | 0.9783 | 0.9760 | 0.9763 | 98 | 0.9814 | 0.9796 | 0.9798 | 98 | 0.7008 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 21, 0, 0], [2, 0, 1, 29, 0], [3, 0, 1, 0, 15]] | | 0.4415 | 45.82 | 1100 | 0.1917 | 1.0 | 1.0 | 1.0 | 31 | 1.0 | 1.0 | 1.0 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 1.0 | 1.0 | 1.0 | 1.0 | 98 | 1.0 | 1.0 | 1.0 | 98 | 0.6974 | [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 0.3074 | 49.98 | 1200 | 0.1865 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9545 | 1.0 | 0.9767 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9898 | 0.9886 | 0.9919 | 0.9901 | 98 | 0.9903 | 0.9898 | 0.9898 | 98 | 0.6686 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 0.2069 | 54.16 | 1300 | 0.1821 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9545 | 1.0 | 0.9767 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9898 | 0.9886 | 0.9919 | 0.9901 | 98 | 0.9903 | 0.9898 | 0.9898 | 98 | 0.7043 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 0.1791 | 58.33 | 1400 | 0.1866 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9130 | 1.0 | 0.9545 | 21 | 1.0 | 0.9667 | 0.9831 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9796 | 0.9783 | 0.9836 | 0.9803 | 98 | 0.9814 | 0.9796 | 0.9799 | 98 | 0.6893 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 1, 29, 0], [3, 0, 0, 0, 16]] | | 0.1717 | 62.49 | 1500 | 0.1839 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9545 | 1.0 | 0.9767 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9898 | 0.9886 | 0.9919 | 0.9901 | 98 | 0.9903 | 0.9898 | 0.9898 | 98 | 0.7848 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 0.1571 | 66.65 | 1600 | 0.1799 | 1.0 | 0.9677 | 0.9836 | 31 | 0.9545 | 1.0 | 0.9767 | 21 | 1.0 | 1.0 | 1.0 | 30 | 1.0 | 1.0 | 1.0 | 16 | 0.9898 | 0.9886 | 0.9919 | 0.9901 | 98 | 0.9903 | 0.9898 | 0.9898 | 98 | 0.7929 | [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]] | | 7acf056f7d45cc74ef83923ee3a4791a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | whisper-base-uk This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3201 - eval_wer: 10.2869 | 6d5f789d97e5322d5b58f3663cc872b6 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP | 190bf89651be4795c55ca31709151b0e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-5_england-5_s334 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 7eb3a35720500e41998c078faa9befe5 |
other | ['generated_from_keras_callback'] | false | TheNateTCY/fulltrain_optmodel This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.8560 - Validation Loss: 1.2171 - Epoch: 0 | 036cc92afd3191597846eeb680218574 |
other | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 8375, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 14e408ae7b30a6a3442118629e857ae4 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on irisg444_4c0 to apply classification on Species **Metrics of the best model:** accuracy 0.953333 recall_macro 0.953333 precision_macro 0.956229 f1_macro 0.953216 Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64 **See model plot below:** <style> | 527a4ae4344dfc9d8e342f55061d9e4c |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | sk-container-id-2 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;} | d3ed564c952ec8960b4337166b2512fb |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float ... free_string useless SepalLengthCm True False ... False False SepalWidthCm True False ... False False PetalLengthCm True False ... False False PetalWidthCm True False ... False False[4 rows x 7 columns])),(& | 0ae40a15e737d25be07b1a098a1ebde8 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</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-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(& | 10a5838bc43a55812f31de59ecb52fad |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float ... free_string useless SepalLengthCm True False ... False False SepalWidthCm True False ... False False PetalLengthCm True False ... False False PetalWidthCm True False ... False False[4 rows x 7 columns])</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-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=1, class_weight=& | 70fd586d778a7927731e4415f5c0ab36 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, max_iter=1000)</pre></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 | add4638b3f16a699897cbc9fe8cf0202 |
apache-2.0 | ['translation'] | false | opus-mt-fr-bi * source languages: fr * target languages: bi * OPUS readme: [fr-bi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-bi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-bi/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bi/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-bi/opus-2020-01-20.eval.txt) | 314113b76182d036e94d42113e8d644f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-32-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7384 - Accuracy: 0.724 | 907c2ef0f4a96c59105a10b7f1d5f464 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1013 | 1.0 | 19 | 1.0733 | 0.55 | | 1.0226 | 2.0 | 38 | 1.0064 | 0.65 | | 0.8539 | 3.0 | 57 | 0.8758 | 0.75 | | 0.584 | 4.0 | 76 | 0.6941 | 0.7 | | 0.2813 | 5.0 | 95 | 0.5151 | 0.7 | | 0.1122 | 6.0 | 114 | 0.4351 | 0.8 | | 0.0432 | 7.0 | 133 | 0.4896 | 0.85 | | 0.0199 | 8.0 | 152 | 0.5391 | 0.85 | | 0.0126 | 9.0 | 171 | 0.5200 | 0.85 | | 0.0085 | 10.0 | 190 | 0.5622 | 0.85 | | 0.0069 | 11.0 | 209 | 0.5950 | 0.85 | | 0.0058 | 12.0 | 228 | 0.6015 | 0.85 | | 0.0053 | 13.0 | 247 | 0.6120 | 0.85 | | 0.0042 | 14.0 | 266 | 0.6347 | 0.85 | | 0.0039 | 15.0 | 285 | 0.6453 | 0.85 | | 0.0034 | 16.0 | 304 | 0.6660 | 0.85 | | 70f673b91cb2ee8e7aa030969366b5fb |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_no-pretraining_s807 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | cd21c65865e8fd83bdd46b1342431e2b |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua 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). I built a Discord AI chatbot based on this model. [Check out my GitHub repo.](https://github.com/RuolinZheng08/twewy-discord-chatbot) Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-joshua") | 957e1ff94ac90c92c81d9b404a8ca068 |
mit | ['conversational'] | false | 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 ) | be4745473152e9c4abed922d91529a6a |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 | b66dd2f6aee0fd8751da8ca68e2fe6b8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | | bff39ae636dd966f0c4ae244d77c4dfb |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2913 - Accuracy: 0.88 - F1: 0.8808 | 4c174aca373cff5e335538c006e8c899 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | bert-base-cased-finetuned-stsb This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4861 - Pearson: 0.8926 - Spearmanr: 0.8898 - Combined Score: 0.8912 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). | 8e9d1b89cc2687a35bf34e10f9f15680 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash | 078b71aaaa5b1ad9d4ed315e837689de |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | !/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name stsb \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-stsb \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` | d6a7aa4216f7117a87a2cc901cd9bfc4 |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 63658491ba435f3e49dc512e8638f72d |
apache-2.0 | ['generated_from_trainer', 'fnet-bert-base-comparison'] | false | Training results | Training Loss | Epoch | Step | Combined Score | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:--------------:|:---------------:|:-------:|:---------:| | 1.1174 | 1.0 | 360 | 0.8816 | 0.5000 | 0.8832 | 0.8800 | | 0.3835 | 2.0 | 720 | 0.8901 | 0.4672 | 0.8915 | 0.8888 | | 0.2388 | 3.0 | 1080 | 0.8912 | 0.4861 | 0.8926 | 0.8898 | | 6ec0a4a840fb7df8d5a940d9bdc80d0f |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-es-zle Neural machine translation model for translating from Spanish (es) to East Slavic languages (zle). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` | e57e1597a0e9c9e4dd76fb9e43936d57 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-23 * source language(s): spa * target language(s): bel rus ukr * valid target language labels: >>bel<< >>rus<< >>ukr<< * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zle/opusTCv20210807_transformer-big_2022-03-23.zip) * more information released models: [OPUS-MT spa-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-zle/README.md) * more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>bel<<` | 1d424581220af8f5c81f72b605d376a9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Su novela se vendió bien.", ">>ukr<< Quiero ir a Corea del Norte." ] model_name = "pytorch-models/opus-mt-tc-big-es-zle" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) | dd103ca0339371531f0c97fe9944726b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Я хочу поїхати до Північної Кореї. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-es-zle") print(pipe(">>rus<< Su novela se vendió bien.")) | 572dd9b95350c9420dd5f84a88b94b2d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zle/opusTCv20210807_transformer-big_2022-03-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/spa-zle/opusTCv20210807_transformer-big_2022-03-23.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 54d1355fc6953e53f57c06a486071ae9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | spa-bel | tatoeba-test-v2021-08-07 | 0.54506 | 27.5 | 205 | 1259 | | spa-rus | tatoeba-test-v2021-08-07 | 0.68523 | 49.0 | 10506 | 69242 | | spa-ukr | tatoeba-test-v2021-08-07 | 0.63502 | 42.3 | 10115 | 54544 | | spa-rus | flores101-devtest | 0.49913 | 20.2 | 1012 | 23295 | | spa-ukr | flores101-devtest | 0.47772 | 17.4 | 1012 | 22810 | | spa-rus | newstest2012 | 0.52436 | 24.6 | 3003 | 64790 | | spa-rus | newstest2013 | 0.54249 | 26.9 | 3000 | 58560 | | a7aa96d64593099b597552a8b5d53661 |
apache-2.0 | ['question-answering'] | false | Model description This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation. In this version, the following pre-trained models were used: * Student: `distilbert-base-uncased` * Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1` | b61324a447420f363f34209ecca1d742 |
apache-2.0 | ['question-answering'] | false | Training data This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows: ```python from datasets import load_dataset squad = load_dataset('squad') ``` | 2e72869c5aadad444f4397c76b351fe1 |
apache-2.0 | ['question-answering'] | false | Eval results | | Exact Match | F1 | |------------------|-------------|------| | DistilBERT paper | 79.1 | 86.9 | | Ours | 78.4 | 86.5 | The scores were calculated using the `squad` metric from `datasets`. | 70c22dc706382792c026801c82ba1e59 |
apache-2.0 | ['question-answering'] | false | BibTeX entry and citation info ```bibtex @misc{sanh2020distilbert, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, year={2020}, eprint={1910.01108}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | 669eb29c469faf333ac66f139724ead9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becasv3-1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv3 dataset. It achieves the following results on the evaluation set: - Loss: 3.1086 | 3afd47fc4bb2060197d4b846a3e55c1f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 8510dfdcd5e2abd7c9662c0feaafd433 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 5.1063 | | No log | 2.0 | 16 | 4.4615 | | No log | 3.0 | 24 | 3.9351 | | No log | 4.0 | 32 | 3.5490 | | No log | 5.0 | 40 | 3.3299 | | No log | 6.0 | 48 | 3.2148 | | No log | 7.0 | 56 | 3.1292 | | No log | 8.0 | 64 | 3.1086 | | d48fef8daf813fe347dc6d3eca990c12 |
mit | ['generated_from_trainer'] | false | clinical-finetuned-AgitationModel This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9746 - Accuracy: 0.88 - Precision: 0.9178 - Recall: 0.9178 - F1: 0.9178 | ebbd61cf9abd4f54f0c775aca674be3a |
mit | ['generated_from_trainer'] | false | 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 - mixed_precision_training: Native AMP | 478785d1e9524ab05e70926183cada2a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.0949 | 1.0 | 50 | 1.0393 | 0.85 | 0.8816 | 0.9178 | 0.8993 | | 0.0475 | 2.0 | 100 | 1.0619 | 0.85 | 0.8816 | 0.9178 | 0.8993 | | 0.0149 | 3.0 | 150 | 0.9746 | 0.88 | 0.9178 | 0.9178 | 0.9178 | | 23dbb2a1a060d82af9052ffce9f1acb8 |
apache-2.0 | ['generated_from_trainer'] | false | new-test-model2 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1040 - Precision: 0.9722 - Recall: 0.9757 - F1: 0.9739 - Accuracy: 0.9808 | 8bd5038ecd82d29d827c12deddacc02c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 | f9116a2dc106793943a93a59befb6503 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 151 | 0.1819 | 0.9360 | 0.9405 | 0.9382 | 0.9540 | | No log | 2.0 | 302 | 0.1196 | 0.9637 | 0.9639 | 0.9638 | 0.9703 | | No log | 3.0 | 453 | 0.1322 | 0.9614 | 0.9682 | 0.9648 | 0.9711 | | 0.2764 | 4.0 | 604 | 0.1071 | 0.9677 | 0.9725 | 0.9701 | 0.9763 | | 0.2764 | 5.0 | 755 | 0.1084 | 0.9709 | 0.9766 | 0.9737 | 0.9790 | | 0.2764 | 6.0 | 906 | 0.1015 | 0.9717 | 0.9739 | 0.9728 | 0.9791 | | 0.0342 | 7.0 | 1057 | 0.1208 | 0.9686 | 0.9727 | 0.9706 | 0.9785 | | 0.0342 | 8.0 | 1208 | 0.1068 | 0.9680 | 0.9752 | 0.9716 | 0.9798 | | 0.0342 | 9.0 | 1359 | 0.1028 | 0.9719 | 0.9743 | 0.9731 | 0.9807 | | 0.0129 | 10.0 | 1510 | 0.1040 | 0.9722 | 0.9757 | 0.9739 | 0.9808 | | 8c6e031739344c11ab485d2b725b6120 |
apache-2.0 | ['translation'] | false | opus-mt-hy-en * source languages: hy * target languages: en * OPUS readme: [hy-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/hy-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/hy-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/hy-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/hy-en/opus-2019-12-18.eval.txt) | e8789f54bcb5b5971bf90c393996861c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | Demo: How to use in ESPnet2 Follow the [CHiME-7 DASR installation instructions](https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md) if you haven't done that already. ```bash cd espnet git checkout 15646109f254de8b39bbe310827d617da5ac858d | 105260d47726145f372d4c820d87ff93 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | follow installation instruction for CHiME-7 DASR recipe https://github.com/espnet/espnet/blob/master/egs2/chime7_task1/asr1/README.md ./run.sh --decode-only 1 --use-pretrained popcornell/chime7_task1_asr1_baseline --ngpu PUT YOURS ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 4b12573d828bd85aa487533de0b89035 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | Environments - date: `Wed Feb 8 23:41:28 UTC 2023` - python version: `3.9.2 (default, Mar 3 2021, 20:02:32) [GCC 7.3.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.13.1+cu116` - Git hash: `` - Commit date: `` | 53f45e33c46a5bc958fbb28d805de548 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k_raw_en_bpe500_batch_size640_scheduler_confwarmup_steps8000_max_epoch8_optim_conflr0.000500000000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 5 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 44341 dist_launcher: null multiprocessing_distributed: true unused_parameters: true sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 8 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 5 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 640 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe batch_type: folded valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/kaldi/train_all_mdm_ihm_rvb_gss_sp/wav.scp - speech - sound - - dump/raw/kaldi/train_all_mdm_ihm_rvb_gss_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/kaldi/chime6/dev/gss/wav.scp - speech - sound - - dump/raw/kaldi/chime6/dev/gss/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 8000 token_list: - <blank> - <unk> - s - '''' - ▁i - t - ▁it - ▁a - e - ▁you - ▁the - ▁like - ▁yeah - a - d - ▁and - m - ▁that - ▁to - n - i - y - ing - o - u - ▁so - p - ▁of - ▁in - re - ▁was - c - r - ▁just - er - ▁know - ▁oh - ed - ▁but - ▁ummm - ▁we - l - ▁no - ▁they - ▁have - ▁do - g - ▁he - k - ll - ▁uhhh - ▁don - ▁for - h - ▁what - ▁be - ar - ▁is - ▁there - '-' - ▁s - ▁this - in - b - ▁ - en - ▁on - ▁p - ▁can - al - ▁not - w - ▁my - ▁one - ic - f - ▁or - ▁really - ▁go - ▁right - ▁me - an - ▁w - or - le - ▁f - ▁think - ▁okay - ▁all - ▁then - ▁with - ▁are - ▁get - it - ▁t - ▁st - ve - ▁hmmm - ▁g - ▁if - ce - 'on' - ▁she - ▁good - ▁e - es - ▁well - v - ▁re - th - ter - ch - ▁out - ▁up - ly - ▁b - ▁ma - il - ▁would - ▁at - ▁want - ▁mean - ▁ch - ▁your - ▁people - ur - ▁how - ▁k - ▁co - ▁about - ▁tr - ▁ba - ▁kind - ▁when - ▁mi - ▁because - ro - ▁had - ▁ho - ▁gonna - ▁time - ▁more - ▁got - ▁some - ▁two - ▁did - ▁see - ▁now - ▁pa - ra - ▁de - ▁lot - ▁actually - ▁o - ▁too - ate - ▁here - ▁cuz - ▁sp - ▁where - ▁going - ▁j - ▁from - ▁bo - ▁them - ▁bu - ▁put - ▁thing - ng - ▁were - ▁n - ▁sh - ▁work - el - ▁something - ▁se - ▁say - ke - ow - ▁ca - ▁fa - ▁need - sh - ▁di - ▁po - ▁make - la - ▁br - ▁v - ▁an - ▁who - ion - ▁y - ▁look - ▁didn - ▁could - ▁little - ver - ▁c - ▁mo - ▁much - ▁very - ir - ▁sa - ▁play - ▁pretty - ▁been - ▁d - ▁other - ▁year - and - ▁mm - ▁stuff - ▁dr - ▁why - ▁con - ▁su - ▁back - ▁ex - ting - ▁take - ▁li - ▁even - ▁should - ▁her - ally - lo - ation - ▁way - ▁guess - ▁has - z - ▁three - ry - ▁ha - ies - is - x - ▁ro - ▁yes - ▁th - ▁use - ▁down - ous - ▁over - ▁probably - ▁guys - ▁maybe - ▁still - ▁cr - ▁which - ▁nice - und - ▁sure - ▁l - ▁off - ▁la - ▁cu - est - ▁any - ▁fi - ▁these - ▁ra - ▁went - ▁things - ment - ▁doing - ▁day - ▁un - ▁lo - ▁da - ▁only - igh - ▁come - ▁big - ▁those - ▁wanna - ▁bit - ▁never - ▁us - ol - ▁though - ▁first - ive - ▁their - ▁let - ▁start - ▁his - ▁four - ▁le - ▁eat - ist - ▁school - us - ▁into - ▁yep - uck - ▁than - ▁him - ▁hi - ▁also - ▁five - side - ▁new - ▁comp - ▁cool - ▁talk - ▁said - ▁pro - ▁r - ▁always - ▁ri - ▁cl - ▁long - able - ▁sc - ▁gra - ▁by - ▁friend - age - ▁different - ▁live - ▁doesn - ▁place - ▁sorry - ▁will - ▁feel - ▁does - ▁part - ▁wait - ▁six - ▁watch - ▁anything - ▁man - ▁our - ▁car - ▁huh - ▁whatever - ▁last - ▁give - ▁ten - ▁before - ▁thought - ▁after - ▁game - ▁card - ▁fl - ▁every - cause - ▁same - ▁around - ▁cook - ▁week - ▁hu - ▁everything - ▁fine - ▁many - ▁qu - ▁read - ▁tea - ough - ance - ▁turn - ▁wow - ▁fun - ▁hard - ▁great - ▁love - ▁remember - ▁twenty - ▁whole - ▁happen - ▁seven - ▁keep - ▁food - ▁most - j - ▁might - ▁thank - ▁move - ▁job - ▁eight - ▁mu - ▁sort - ▁better - port - ▁another - ful - ▁point - ▁show - ▁again - ▁high - ize - ▁house - ▁home - ▁person - ▁old - ▁end - ▁through - ▁pick - ▁else - ▁guy - ▁app - ▁find - ▁nine - ▁hand - ▁kid - ▁interesting - ▁city - ▁called - ▁tell - ▁half - ▁name - ▁definitely - ▁made - ▁exactly - ▁came - ▁wood - ▁funny - ▁basically - ▁count - ▁usually - ▁help - ▁someone - ▁already - ▁dunno - ▁enough - ction - ▁own - ▁weird - ▁next - ▁hundred - ▁small - ▁money - ▁couple - ▁while - ▁close - ▁movie - ▁sometimes - ▁everyone - ▁away - ▁true - ▁super - ▁cheese - ▁class - ▁night - ▁life - ▁leave - ▁plan - ▁water - ▁left - ▁thirty - ▁family - ▁phone - ▁build - ▁room - ▁month - ▁open - ▁idea - ▁second - ▁dude - ▁music - ▁each - ▁learn - ▁girl - ▁together - ▁under - ▁run - ▁chicken - ▁having - ▁either - ▁almost - ▁crazy - ▁book - ▁sauce - ▁supposed - ▁course - ▁speak - ▁awesome - ▁anyway - ▁throw - ▁finish - ▁world - ▁reason - ▁check - ▁least - ▁parents - ▁everybody - ▁change - '&' - ä - ' | 0d34aaf127123d26ac97710d43943c50 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition', 'speech separation'] | false | ' - ñ - â - é - ü - ']' - q - î - <sos/eos> init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 aux_ctc_tasks: [] frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true fs: 16k specaug: specaug specaug_conf: apply_time_warp: false time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: false freq_mask_width_range: - 0 - 150 num_freq_mask: 4 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.15 num_time_mask: 3 normalize: utterance_mvn normalize_conf: {} model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false preencoder: linear preencoder_conf: input_size: 1024 output_size: 128 dropout: 0.2 encoder: transformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d2 normalize_before: true postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: input_layer: embed attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.0 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202301' distributed: true ``` </details> | fb986e828c070452d82d0fa451c336bf |
apache-2.0 | ['generated_from_trainer'] | false | test1000v2 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7873 - Wer: 0.6162 | e0b209039caf8825023981e9350044d9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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: 20 - mixed_precision_training: Native AMP | 7bdbd55ce5ca8202f4f9eed47b0c6f11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 7.7913 | 3.22 | 100 | 3.3481 | 1.0 | | 3.3831 | 6.44 | 200 | 3.3229 | 1.0 | | 3.3778 | 9.67 | 300 | 3.3211 | 1.0 | | 3.3671 | 12.89 | 400 | 3.2973 | 1.0 | | 3.3528 | 16.13 | 500 | 3.1349 | 1.0 | | 1.8611 | 19.35 | 600 | 0.7873 | 0.6162 | | b580b506d01d8bd4ff8570f48b7c8155 |
mit | ['exbert'] | false | Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. | b08bdaf5cdc42edd7716f6d701f4f5b0 |
mit | ['exbert'] | false | How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` | 7ded885fce97538f1d2d766d3ab2f53e |
mit | ['exbert'] | false | Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: | 074e57a45f8add8f0a536eac8c057306 |
mit | ['exbert'] | false | BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> | 5eb753caa7446ba88997960337b87fd2 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | koja Dreambooth model trained by Kurapka with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: | 2c793be0a02562cce21d438b96357658 |
apache-2.0 | ['automatic-speech-recognition', 'sv-SE'] | false | exp_w2v2t_sv-se_r-wav2vec2_s418 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 9a442d52f550a9965a4a56c0736c0fee |
unknown | ['stable-diffusion', 'text-to-image'] | false | .safetensor model for automatic1111 webui. Strange_Dedication_v3 is an improvement to Strange_Dedication_v2 using Anything_v4.5. It's better at the cutesexyrobutts style, without having to use a trigger. Also, it's good at shiny_skin and shiny_clothes and artistical backgrounds. I have only used it with "vae-ft-mse-840000-ema-pruned", CLIP-Skip 1 and with danbooru tags. Lately I have started using the negative embed "bad-hands-5" (by an unknown author?), which was used for the example images as well. If you work with those you should be able to prompt images like these (prompts in .png metadata):     v2 version Strange_Dedication_v2 is a model mix I did for myself. It's based mostly on two models, which are specialised in the artists cutesexyrobutts and free_style(yohan1754). I have added some different danbooru and r34 models to increase the affinity with uncommon prompts, can't specify which exactly. I have only used it with "vae-ft-mse-840000-ema-pruned", CLIP-Skip 1 and with danbooru tags. If you work with those you should be able to prompt good images, check out the example folder as well (prompts in .png metadata). The cutesexyrobutts style model had the trigger "by_cutesexyrobutts", which still works. | 9b107d017a9674c092158e4f7c3571ec |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion', 'gakki'] | false | legal & risk ⚠️⚠ It is prohibited to use this model for commercial purposes and any scenarios of illegal acts and purposes. Sample pictures of this concept:    | 934895e587a12357e692b5c6237f6ba8 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_havest_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.5508 - Train Accuracy: 0.0121 - Train Do Wer: 1.0 - Validation Loss: 4.7620 - Validation Accuracy: 0.0121 - Validation Do Wer: 1.0 - Epoch: 14 | fd5ad5d70cbc0c17215302d532ce9c0e |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | ed0e9bb766998d5716ae1931a9b275df |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Train Do Wer | Validation Loss | Validation Accuracy | Validation Do Wer | Epoch | |:----------:|:--------------:|:------------:|:---------------:|:-------------------:|:-----------------:|:-----:| | 9.9191 | 0.0046 | 1.0 | 8.5836 | 0.0067 | 1.0 | 0 | | 8.0709 | 0.0083 | 1.0 | 7.4667 | 0.0089 | 1.0 | 1 | | 7.1652 | 0.0100 | 1.0 | 6.8204 | 0.0112 | 1.0 | 2 | | 6.7196 | 0.0114 | 1.0 | 6.5192 | 0.0114 | 1.0 | 3 | | 6.4115 | 0.0115 | 1.0 | 6.2357 | 0.0115 | 1.0 | 4 | | 6.1085 | 0.0115 | 1.0 | 5.9657 | 0.0115 | 1.0 | 5 | | 5.8206 | 0.0115 | 1.0 | 5.7162 | 0.0115 | 1.0 | 6 | | 5.5567 | 0.0115 | 1.0 | 5.4963 | 0.0115 | 1.0 | 7 | | 5.3223 | 0.0116 | 1.0 | 5.3096 | 0.0116 | 1.0 | 8 | | 5.1222 | 0.0117 | 1.0 | 5.1600 | 0.0117 | 1.0 | 9 | | 4.9580 | 0.0117 | 1.0 | 5.0391 | 0.0118 | 1.0 | 10 | | 4.8251 | 0.0119 | 1.0 | 4.9427 | 0.0118 | 1.0 | 11 | | 4.7171 | 0.0119 | 1.0 | 4.8691 | 0.0119 | 1.0 | 12 | | 4.6284 | 0.0121 | 1.0 | 4.8123 | 0.0120 | 1.0 | 13 | | 4.5508 | 0.0121 | 1.0 | 4.7620 | 0.0121 | 1.0 | 14 | | 0c9642d039f05a5d979c622c7ab8629c |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Small Uncased Generator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the generator model used to sample synthetic text and pretrain the discriminator. Only use this model for retraining and mask-filling. For the actual model for downstream tasks, please refer to the discriminator models. | 043d1299ffbb5eb40a02bf4588b9714f |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | Citations All model details and training setups can be found in our papers. If you use our model or find it useful in your projects, please cite our work: ``` @inproceedings{cruz2021exploiting, title={Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets}, author={Cruz, Jan Christian Blaise and Resabal, Jose Kristian and Lin, James and Velasco, Dan John and Cheng, Charibeth}, booktitle={Pacific Rim International Conference on Artificial Intelligence}, pages={86--99}, year={2021}, organization={Springer} } ``` | 98c8cd28972e108a2891ee9d35eccb1f |
apache-2.0 | ['generated_from_trainer'] | false | nba_pbp_distilgpt2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on text files containing play-by-play descriptions of games played by the Boston Celtics and Golden State Warriors during the 2021-22 NBA season. It achieves the following results on the evaluation set: - Loss: 0.6324 - Accuracy: 0.8117 | a2a13d2378039df174d55c18cdb4401d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 37b549f9cb3f9b65b3370395f58f47d7 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0065 | ca1e94ec4d13ff12054f54189267efd7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3631 | 0.4 | 500 | 5.9145 | | 5.6422 | 0.8 | 1000 | 5.8224 | | 5.4368 | 1.2 | 1500 | 5.6172 | | 5.1539 | 1.6 | 2000 | 5.4872 | | 5.0641 | 2.0 | 2500 | 5.5369 | | 4.9495 | 2.4 | 3000 | 5.3466 | | 4.8947 | 2.8 | 3500 | 5.4592 | | 4.9081 | 3.2 | 4000 | 5.3328 | | 4.7214 | 3.6 | 4500 | 5.3746 | | 4.7341 | 4.0 | 5000 | 5.3417 | | 4.6482 | 4.4 | 5500 | 5.2731 | | 4.628 | 4.8 | 6000 | 5.2716 | | 4.5801 | 5.2 | 6500 | 5.1364 | | 4.4967 | 5.6 | 7000 | 5.2167 | | 4.4984 | 6.0 | 7500 | 5.2133 | | 4.4255 | 6.4 | 8000 | 5.1228 | | 4.4459 | 6.8 | 8500 | 5.1664 | | 4.3732 | 7.2 | 9000 | 5.0800 | | 4.2546 | 7.6 | 9500 | 5.0616 | | 4.351 | 8.0 | 10000 | 5.1500 | | 4.2365 | 8.4 | 10500 | 5.0903 | | 4.2224 | 8.8 | 11000 | 5.0041 | | 4.2549 | 9.2 | 11500 | 5.0711 | | 4.1108 | 9.6 | 12000 | 5.1525 | | 4.1366 | 10.0 | 12500 | 5.0065 | | 76623b9a43eafbcc608b7dbd93c33a5d |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_age_teens-8_sixties-2_s945 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | b8fd0221d92f875aa66e5106d1cb7754 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1547 | 01ee8b557cfe53d4ecb706fb3e7090d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2164 | 1.0 | 5533 | 1.1486 | | 0.9546 | 2.0 | 11066 | 1.1251 | | 0.7573 | 3.0 | 16599 | 1.1547 | | d8c99812008c24b049de3dec504be50b |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | PyTorch This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc). | 351d4f81e741dc7bf03f133246dc6846 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSequenceClassification int8_model = IncQuantizedModelForSequenceClassification.from_pretrained( 'Intel/bert-base-uncased-mrpc-int8-dynamic', ) ``` | f1b5117cd91336c26dd98272c208bf14 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Intel/bert-base-uncased-mrpc](https://huggingface.co/Intel/bert-base-uncased-mrpc). | 475454570558e60e11688fddd3a59672 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'PostTrainingDynamic', 'onnx'] | false | Load ONNX model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained('Intel/bert-base-uncased-mrpc-int8-dynamic') ``` | 6d374f58e178b42c441a0cd1a8a067ae |
apache-2.0 | ['generated_from_trainer'] | false | InstructDial Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks. [Paper](https://arxiv.org/abs/2205.12673) [GIT] (https://github.com/prakharguptaz/Instructdial) | 50b4681dc54aa3ec1d1395eb67170505 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 9 - eval_batch_size: 9 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 72 - total_eval_batch_size: 72 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 4c857c5700de0fc4c0b638c0041cc1e6 |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | Model description LightWeight GAN model for unconditional generation. NFT collection available [here](https://opensea.io/collection/alpacadabraz). Dataset is available [here](https://huggingface.co/datasets/huggingnft/alpacadabraz). Check Space: [link](https://huggingface.co/spaces/AlekseyKorshuk/huggingnft). Project repository: [link](https://github.com/AlekseyKorshuk/huggingnft). [](https://github.com/AlekseyKorshuk/huggingnft) | 74273bb849a98af0f67d7f52b7b43462 |
mit | ['huggingnft', 'nft', 'huggan', 'gan', 'image', 'images', 'unconditional-image-generation'] | false | About *Built by Aleksey Korshuk* [](https://github.com/AlekseyKorshuk) [](https://twitter.com/intent/follow?screen_name=alekseykorshuk) [](https://t.me/joinchat/_CQ04KjcJ-4yZTky) For more details, visit the project repository. [](https://github.com/AlekseyKorshuk/huggingnft) | 2a551b65551c8cee12a719add6afaa5f |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Arabic NER Model for AQMAR dataset Training was conducted over 86 epochs, using a linear decaying learning rate of 2e-05, starting from 0.3 and a batch size of 48 with fastText and Flair forward and backward embeddings. | cebe1a13a46e0a232352d05beae6a59a |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Results: - F1-score (micro) 0.9323 - F1-score (macro) 0.9272 | | True Posititves | False Positives | False Negatives | Precision | Recall | class-F1 | |------|-----|----|----|---------|--------|----------| | LOC | 164 | 7 | 13 | 0.9591 | 0.9266 | 0.9425 | | MISC | 398 | 22 | 37 | 0.9476 | 0.9149 | 0.9310 | | ORG | 65 | 6 | 9 | 0.9155 | 0.8784 | 0.8966 | | PER | 199 | 13 | 13 | 0.9387 | 0.9387 | 0.9387 | --- | 0fddb3c1f870e9512b91fa4adb591331 |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Usage ```python from flair.data import Sentence from flair.models import SequenceTagger import pyarabic.araby as araby from icecream import ic arTagger = SequenceTagger.load('megantosh/flair-arabic-MSA-aqmar') sentence = Sentence('George Washington went to Washington .') arSentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية بالقاهرة .') | 9c0059bcbb20861d3b0b8201bf313a5b |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Model Configuration ```python (embeddings): StackedEmbeddings( (list_embedding_0): WordEmbeddings('ar') (list_embedding_1): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(7125, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=7125, bias=True) ) ) (list_embedding_2): FlairEmbeddings( (lm): LanguageModel( (drop): Dropout(p=0.1, inplace=False) (encoder): Embedding(7125, 100) (rnn): LSTM(100, 2048) (decoder): Linear(in_features=2048, out_features=7125, bias=True) ) ) ) (word_dropout): WordDropout(p=0.05) (locked_dropout): LockedDropout(p=0.5) (embedding2nn): Linear(in_features=4396, out_features=4396, bias=True) (rnn): LSTM(4396, 256, batch_first=True, bidirectional=True) (linear): Linear(in_features=512, out_features=14, bias=True) (beta): 1.0 (weights): None (weight_tensor) None )" 2021-03-31 22:19:50,654 ---------------------------------------------------------------------------------------------------- 2021-03-31 22:19:50,654 Corpus: "Corpus: 3025 train + 336 dev + 373 test sentences" 2021-03-31 22:19:50,654 ---------------------------------------------------------------------------------------------------- 2021-03-31 22:19:50,654 Parameters: 2021-03-31 22:19:50,654 - learning_rate: "0.3" 2021-03-31 22:19:50,654 - mini_batch_size: "48" 2021-03-31 22:19:50,654 - patience: "3" 2021-03-31 22:19:50,654 - anneal_factor: "0.5" 2021-03-31 22:19:50,654 - max_epochs: "150" 2021-03-31 22:19:50,654 - shuffle: "True" 2021-03-31 22:19:50,654 - train_with_dev: "False" 2021-03-31 22:19:50,654 - batch_growth_annealing: "False" 2021-03-31 22:19:50,655 ------------------------------------ ``` Due to the right-to-left in left-to-right context, some formatting errors might occur. and your code might appear like [this](https://ibb.co/ky20Lnq), (link accessed on 2020-10-27) | 3c3d47b724a8477aaf981812151c69c2 |
apache-2.0 | ['flair', 'Text Classification', 'token-classification', 'sequence-tagger-model'] | false | Citation *if you use this model, please consider citing [this work](https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects):* ```latex @unpublished{MMHU21 author = "M. Megahed", title = "Sequence Labeling Architectures in Diglossia", year = {2021}, doi = "10.13140/RG.2.2.34961.10084" url = {https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects} } ``` | 292dd785708b072d9c5a1b0ce026f18e |
apache-2.0 | ['generated_from_trainer'] | false | CTEBMSP_ANAT_DISO This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0909 - Anat Precision: 0.7522 - Anat Recall: 0.7147 - Anat F1: 0.7330 - Anat Number: 361 - Diso Precision: 0.8915 - Diso Recall: 0.8919 - Diso F1: 0.8917 - Diso Number: 2645 - Overall Precision: 0.8755 - Overall Recall: 0.8706 - Overall F1: 0.8731 - Overall Accuracy: 0.9873 | 9341482384c90286ac255c361b827da2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-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: 8 | deb4d4b174a250db5bd6f631abee0b88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Anat Precision | Anat Recall | Anat F1 | Anat Number | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0592 | 1.0 | 2133 | 0.0506 | 0.6950 | 0.4986 | 0.5806 | 361 | 0.8635 | 0.8609 | 0.8622 | 2645 | 0.8484 | 0.8174 | 0.8326 | 0.9843 | | 0.0323 | 2.0 | 4266 | 0.0583 | 0.7899 | 0.6039 | 0.6845 | 361 | 0.8780 | 0.8817 | 0.8798 | 2645 | 0.8697 | 0.8483 | 0.8589 | 0.9858 | | 0.0201 | 3.0 | 6399 | 0.0580 | 0.6565 | 0.7147 | 0.6844 | 361 | 0.8598 | 0.8764 | 0.8680 | 2645 | 0.8339 | 0.8570 | 0.8453 | 0.9851 | | 0.0121 | 4.0 | 8532 | 0.0758 | 0.7240 | 0.6759 | 0.6991 | 361 | 0.8976 | 0.8752 | 0.8863 | 2645 | 0.8776 | 0.8513 | 0.8642 | 0.9863 | | 0.0078 | 5.0 | 10665 | 0.0814 | 0.7219 | 0.7119 | 0.7169 | 361 | 0.8776 | 0.8975 | 0.8875 | 2645 | 0.8595 | 0.8752 | 0.8673 | 0.9862 | | 0.0031 | 6.0 | 12798 | 0.0974 | 0.7599 | 0.6399 | 0.6947 | 361 | 0.8895 | 0.8915 | 0.8905 | 2645 | 0.8761 | 0.8613 | 0.8686 | 0.9867 | | 0.002 | 7.0 | 14931 | 0.0980 | 0.7143 | 0.6787 | 0.6960 | 361 | 0.8813 | 0.8957 | 0.8884 | 2645 | 0.8624 | 0.8696 | 0.8660 | 0.9860 | | 0.0005 | 8.0 | 17064 | 0.0909 | 0.7522 | 0.7147 | 0.7330 | 361 | 0.8915 | 0.8919 | 0.8917 | 2645 | 0.8755 | 0.8706 | 0.8731 | 0.9873 | | fff440d4203fa73dbbf54c137d1b116c |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner-20percent This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6513 - Precision: 0.5252 - Recall: 0.6562 - F1: 0.5834 - Accuracy: 0.8044 | a378918f5353f936aa1c2f57d6d0631b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 69d2ddcf700eb15a31b59e16efe648a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.9155 | 0.3511 | 0.4264 | 0.3851 | 0.7353 | | No log | 2.0 | 30 | 0.7116 | 0.4845 | 0.6321 | 0.5485 | 0.7898 | | No log | 3.0 | 45 | 0.6513 | 0.5252 | 0.6562 | 0.5834 | 0.8044 | | 956c41091aa0f545c6b70d6bea579bce |
mit | ['generated_from_trainer'] | false | Klassifizierung-Heizung This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0936 - F1: 0.9859 | f69e325d937340169e4c532b033ad3d5 |
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