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 | ['automatic-speech-recognition', 'it'] | false | exp_w2v2t_it_vp-nl_s335 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | eae48c58d93e7afcdb72be973a0a8f66 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5603 - Accuracy: 0.7914 | 4bcc3551794764a1c2ca6c440dc1cd55 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.67 | 0.99 | 70 | 0.7920 | 0.7265 | | 0.5856 | 1.99 | 140 | 0.6192 | 0.7804 | | 0.5612 | 2.99 | 210 | 0.5603 | 0.... | be2d6b167a8a9073c68a49809d4b8972 |
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: 1.2052 | 9857894db5b0209125980c2ea7bbd57d |
mit | ['generated_from_keras_callback'] | false | Deep98/Human_Development_Index-clustered This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1876 - Train End Logits Accuracy: 0.9757 - Train Start Logi... | 7a10056d39db5e97a2ac6b4f63013ecd |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 7065c5fd32f98bff7185be014aeef66e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | WhisperForNamedEntityRecognition This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the qmeeus/slue-voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 8.1514 - Wer: 10.4828 | 0a3f6a090bd9186f9c2d7aee9ade94b0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 19204dec2fa8218f0c96cc3d3b93420f |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 31.3741 | 0.06 | 100 | 25.8582 | 10.4828 | | 13.0078 | 1.03 | 200 | 13.4173 | 10.4828 | | 10.3619 | 1.09 | 300 | 10.8540 | 10.482... | e32ede1db790dd7cd5980687a0164e09 |
apache-2.0 | ['generated_from_trainer'] | false | Brain_Tumor_Classification This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1012 - Accuracy: 0.9647 - F1: 0.9647 - Recall: 0.9647 ... | 6b3c844822c638a195af2a99b39ed3eb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.4856 | 0.99 | 83 | 0.3771 | 0.8444 | 0.8444 | 0.8444 | 0.8444 | | 0.3495 | 1.99 |... | eeefc1c7a35bd5db0015b264d6a9bd32 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2t_fr_no-pretraining_s929 Fine-tuned randomly initialized wav2vec2 model for speech recognition using the train split of [Common Voice 7.0 (fr)](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 bee... | 3424997720b11fc200fe00e0b6ae4bbb |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_wavlm_s3 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16k... | 9068544fc8ae94b266c0c1246b54415e |
apache-2.0 | ['generated_from_trainer'] | false | bert-all-squad_ben_tel_context This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5393 | 2444c83eecb4029d752261d48dd013b1 |
mit | ['generated_from_trainer'] | false | outputs This model is a fine-tuned version of [gerulata/slovakbert](https://huggingface.co/gerulata/slovakbert) on the [ju-bezdek/conll2003-SK-NER](https://huggingface.co/datasets/ju-bezdek/conll2003-SK-NER) dataset. It achieves the following results on the evaluation (validation) set: - Loss: 0.1752 - Precision: 0.8... | 8b2cca6718ef5c28c20ebbbea2d2d34a |
mit | ['generated_from_trainer'] | false | Code example ```python: from transformers import pipeline, AutoModel, AutoTokenizer from spacy import displacy import os model_path="ju-bezdek/slovakbert-conll2003-sk-ner" aggregation_strategy="max" ner_pipeline = pipeline(task='ner', model=model_path, aggregation_strategy=aggregation_strategy) input_sentence= "R... | 81231bb2ab60785df8640dff624878a0 |
mit | ['generated_from_trainer'] | false | ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Ruský <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">MISC</span> </mark> premiér <mark class="entity"... | 1e4b46b8fc026197d1acd4601b55670b |
mit | ['generated_from_trainer'] | false | ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Viktor Černomyrdin <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> v piatok povedal, že pre... | 8e988642fac46c14c8cf7d32291c24c7 |
mit | ['generated_from_trainer'] | false | ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Boris Jeľcin, <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> , ktorý je na dovolenke mimo ... | f76fdfb7f25652561605d1af339e9577 |
mit | ['generated_from_trainer'] | false | ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Moskvy <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> </mark> , podporil mierový plán šéfa bezp... | b189ed00516df59bf579c53b35d62e0a |
mit | ['generated_from_trainer'] | false | ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Alexandra Lebedu <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">PER</span> </mark> pre <mark class="e... | cd18bdb2fcfdcb6514acf7fd9d6d13e8 |
mit | ['generated_from_trainer'] | false | ff9561; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Čečensko, <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">LOC</span> </mark> uviedla tlačová agentúra ... | 30e5e36f763893a3fa6c6028e4148524 |
mit | ['generated_from_trainer'] | false | 7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;"> Interfax <span style="font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem">ORG</span> </mark> </div></span> </div> | f93465c5aad334242747f171017c55f4 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 15 | 10fa323e8cdb1ac72568107a7e3c6f1e |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3237 | 1.0 | 878 | 0.2541 | 0.7125 | 0.8059 | 0.7563 | 0.9283 | | 0.1663 | 2.0 ... | 12219655c3203f619941edf30ff269c6 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-16-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 | 4bcf60911e276fd5811a97c9765071fb |
mit | ['generated_from_trainer'] | false | bert-portuguese-squad This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9715 | ca6dd4e906b8ebf2ebbd8f45636ccfc8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.041 | 1.0 | 5578 | 1.1970 | | 0.8267 | 2.0 | 11156 | 1.2215 | | 0.586 | 3.0 | 16734 | 1.3191 | | 0.4251 | 4.0 | 22312 | 1.6129 ... | 5b321612ff492f9d4ac50f1935a7b5a3 |
openrail | [] | false | Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> Python Code (1.05GB) | fe61ba1d7c116851572c8a13539d9ffc |
openrail | [] | false | Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - MLM - python vocab (https://huggingface.co/kkuramitsu/mt5-pytoken) | cd8e87f0b627cb899b5cbf1938b51f36 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5649 - Wer: 30.6374 | c23006f0e9036a67bcba86bd1f9a5a85 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0182 | 7.01 | 1000 | 0.4546 | 31.4735 | | 0.0023 | 14.02 | 2000 | 0.5045 | 31.0910 | | 0.0008 | 22.01 | 3000 | 0.5318 | 30.281... | ff7a831e15a55578243189d4bc13077a |
apache-2.0 | ['generated_from_trainer'] | false | YELP_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - Accuracy: 0.9733 | 1bd0d07331df8da7b3780dde8570c040 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4967 | 0.03 | 50 | 0.1667 | 0.9467 | | 0.3268 | 0.06 | 100 | 0.2106 | 0.9133 | | 0.3413 | 0.1 | 150 | 0.2107 | 0.... | 7940e1a304c751131e0e9fbdc576c2ac |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sche... | 3cfbb6457cc60975a45804c2f3314fec |
apache-2.0 | ['generated_from_keras_callback'] | false | c-x-he/my_awesome_wnut_model 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: - Train Loss: 0.1304 - Validation Loss: 0.2744 - Train Precision: 0.5429 - Train Recall: 0.4007 -... | 7b5fcb468328039728798cd2a16bea1c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| | 0.3493 | 0.3035 | 0.4447 | 0.2309 | 0.3039 | 0.9347 | 0 ... | 27bc9e755c14dce0269676cc109290e2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2259 - Accuracy: 0.924 - F1: 0.9238 | cb985e64df4b7a0f1c50f1502b607862 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8417 | 1.0 | 250 | 0.3291 | 0.9005 | 0.8962 | | 0.2551 | 2.0 | 500 | 0.2259 | 0.924 | 0.9238 | | ef0308826534f21ed93cd2d9752da85c |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | Model description
This model is the [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models:
* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/... | 83800e6eccbee623d4d5b789a615a81b |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | How to use
To use the transformers portion of this model:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_xlmr-base")
model = AutoModel.from_pretrained("liaad/srl-en_xlmr-base")
```
To use the full SRL model (transformers portion + a deco... | b6a3165444defeeaed336bce9865b13d |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
- The models were trained only for 5 epochs.
- The English data was preprocessed to match the Portugue... | 086eaa9c6b005ee1e780e74ab394b4c5 |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | Training procedure
The models were trained on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data. They were tested on the PropBank.Br data set as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info b... | 07084cf6d1abd1d9e58660d011057896 |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | Eval results
| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) |
| --------------- | ------ | ----- |
| `srl-pt_bertimbau-base` | 76.30 | 73.33 |
| `srl-pt_bertimbau-large` | 77.42 | 74.85 |
| `srl-pt_xlmr-base` | 75.22 | 72.82 |
| `srl-pt_xlmr-large` | 77.59... | b9aa4d3f1abef4ad27fe5e35d09bb8ea |
apache-2.0 | ['xlm-roberta-base', 'semantic role labeling', 'finetuned'] | false | BibTeX entry and citation info
```bibtex
@misc{oliveira2021transformers,
title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling},
author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
year={2021},
eprint={2101.01213},
archivePrefix={arX... | b9c739ccf59246cdd649aaacdc8b12f0 |
cc-by-4.0 | ['roberta', 'roberta-base', 'masked-language-modeling', 'masked-lm'] | false | roberta-base for MLM Objective: To make a Roberta Base for the Movie Domain by using various Movie Datasets as simple text for Masked Language Modeling. This is the Movie Roberta to be used in Movie Domain applications. ``` model_name = "thatdramebaazguy/movie-roberta-base" pipeline(model=model_name, tokenizer=m... | e420b24cb98721c43dde934d2ad6088e |
cc-by-4.0 | ['roberta', 'roberta-base', 'masked-language-modeling', 'masked-lm'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** Fill-Mask **Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Eval data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names **Infrastructure**: 4x Tesla v100 ... | d70d70723162f672590f256ca88df94f |
cc-by-4.0 | ['roberta', 'roberta-base', 'masked-language-modeling', 'masked-lm'] | false | Hyperparameters ``` Num examples = 4767233 Num Epochs = 2 Instantaneous batch size per device = 20 Total train batch size (w. parallel, distributed & accumulation) = 80 Gradient Accumulation steps = 1 Total optimization steps = 119182 eval_loss = 1.6153 eval_samples = 20573 perplexity = 5.0296 learning_rate=5e-05 n_g... | ba3a0cb8793dfaf15f5786b46755bac0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion-lr-3e-05-wd-001 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2415 - Accuracy: 0.919 - F1: 0.9191 | 1f6e0bfa1c6eed0d039e9799e83c1a55 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP | 225d4205f67b00f10b0a862ac8d02a01 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.9356 | 1.0 | 125 | 0.3832 | 0.8895 | 0.8855 | | 0.2866 | 2.0 | 250 | 0.2415 | 0.919 | 0.9191 | | f14ee4ebec7b53b21d0c841eaebd42e7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an emtion dataset. It achieves the following results on the evaluation set: - Loss: 0.2254 - Accuracy: 0.925 - F1: 0.9249 | 7f722eee38446dc804ff4e507155aece |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3271 | 0.903 | 0.8983 | | No log | 2.0 | 500 | 0.2254 | 0.925 | 0.9249 | | d6c0a090f3d29951bdfa4eae0e3856ed |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set: - Loss: 0.1957 - Wer: 0.1697 | 451226983ad38dfa077c22f3283f9523 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 1cfc8f8e91f83ff9ff0e618042cc0563 |
apache-2.0 | ['automatic-speech-recognition', 'NbAiLab/NPSC', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.4527 | 0.28 | 250 | 4.0144 | 1.0 | | 3.1828 | 0.56 | 500 | 3.1369 | 1.0 | | 2.9927 | 0.85 | 750 | 3.0183 | 1.0 ... | 4578d0bd046a470203211c1d28f87562 |
apache-2.0 | ['generated_from_keras_callback'] | false | TF-Fine_tuned_T5-base This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2063 - Validation Loss: 0.1893 - Epoch: 4 | 9102cf181f4e99a918efa0095ce7c674 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.6995 | 0.2622 | 0 | | 0.2845 | 0.2256 | 1 | | 0.2471 | 0.2079 | 2 | | 0.2216 | 0.1974 | 3 | | 0.2063 | 0.1893 | 4 | | 44db873ea260c321217eae96b37611f9 |
mit | [] | false | model by Kasuzu This your the Stable Diffusion model fine-tuned the Langel concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **Langel** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/gi... | 1522dbafd84bd7e18b8a6a75b60b0636 |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT_fold_10_ternary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0706 - F1: 0.7748 | 15e54275ca7d237dc07d215e8c68e2fd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.6097 | 0.7290 | | 0.555 | 2.0 | 580 | 0.6106 | 0.7649 | | 0.555 | 3.0 | 870 | 0.6608 | 0.7847 | |... | ee927c18e27b8f55d562e90cb810dfb2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_token_2e-05_16_02_2022-01_55_54 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: 0.1722 - Precision: 0.3378 - R... | 58adfba375c5c922e3a04b57332bd412 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ar-finetuned-en-to-ar This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.8133 - Bleu: 64.6767 - Gen Len: 17.595 | 1c372fabeaaba0f7e440452034de682f |
apache-2.0 | ['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: 16 - mixed_precision_training: Native AMP | 942db99583077344ee1896729f3b75be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 50 | 0.7710 | 64.3416 | 17.4 | | No log | 2.0 | 100 | 0.7569 | 63.9546 | 17.465 | | No log |... | 1900556dc10109f416c3822fc2b85686 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_tr... | fd3570a186641696bdca744efa1be1e2 |
apache-2.0 | ['generated_from_trainer'] | false | inquisitive2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1760 | 06b0a6e3773a5fc935169c48ebbdd815 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - num_epochs: 7.0 | 67305ca8645e1c0d1b579e3b431c5176 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-cynthia-timit This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4888 - Wer: 0.3315 | 60b3c32f00862c487bd5a7433d5e4f90 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.7674 | 1.0 | 500 | 2.8994 | 1.0 | | 1.3538 | 2.01 | 1000 | 0.5623 | 0.5630 | | 0.5416 | 3.01 | 1500 | 0.4595 | 0.476... | 6b3b2e0f2b1351521dcbe4123d88d63b |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-becasv2-4 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset. It achieves the following results on the evaluation set: - Loss: 3.4637 | f6e3a9ba221b1f659541c6eb275e2c5d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 5.3677 | | No log | 2.0 | 12 | 4.6741 | | No log | 3.0 | 18 | 4.2978 | | No log | 4.0 | 24 | 3.9963 ... | c7f32a29de43c98898721a311471470e |
mit | ['sklearn', 'skops', 'text-classification'] | false | Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |---------------------|--------------------------------------------------------------------... | 479c905eccd9d1728b284722c01501ff |
mit | ['sklearn', 'skops', 'text-classification'] | false | sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;} | 73c3aa9fd3c90091dafce05f79ff73dd |
mit | ['sklearn', 'skops', 'text-classification'] | false | sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;} | 55f48750f86a6eda33b9abb7ed368c9f |
mit | ['sklearn', 'skops', 'text-classification'] | false | sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 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. ... | b20f5aa09c4617073414df342224dc82 |
mit | ['sklearn', 'skops', 'text-classification'] | false | sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-8f9616f3-01a7-4784-b5f5-5c31d2b0f7a6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(& | fad7bbb24fe62807d919dfd797b84382 |
mit | ['sklearn', 'skops', 'text-classification'] | false | x27;, MultinomialNB())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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="9caae382... | fdc58d03a3b50c55c9d4840d2524348c |
mit | ['sklearn', 'skops', 'text-classification'] | 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="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" type="checkbox" ><label for="6bf44786-d8ef-4af0-be6a-2ac8b82cf581" class="sk-toggleable_... | 4541ba5135b10137d852e876b638d02f |
mit | ['sklearn', 'skops', 'text-classification'] | false | How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python import pickle with open(pkl_filename, 'rb') as file: clf = pickle.load(file) ``` </details> | eedc3dec91fc15d486a2a4b4d24930fc |
mit | ['generated_from_trainer'] | false | deberta-v3-small-fine-Disaster-Tweets-Part2 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4849 - Accuracy: 0.8275 - F1: 0.8278 | 0b152c98f7b7eecb791ecf07b32a44f2 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_tra... | 943dbf6fb4045f74c7f7579dd33114ec |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 203 | 0.4670 | 0.8511 | 0.8503 | | No log | 2.0 | 406 | 0.4381 | 0.8459 | 0.8455 | | 0.4016 |... | bce2cd7fdfd4cff16d61812ee6dbb82b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-est This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6781 | 683d48034cfea5408a037cb373d21054 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 52 | 4.2576 | | No log | 2.0 | 104 | 3.8075 | | No log | 3.0 | 156 | 3.6781 | | 94945d920421d662ccefe89fb41eb18c |
apache-2.0 | ['generated_from_trainer'] | false | soft-search 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: 0.5558 - F1: 0.5960 - Accuracy: 0.7109 - Precision: 0.5769 -... | 5bfaf4b50e879aa6a3f83a1d923b641b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | df9731a7bd0efa4cf2a88969a9f54a08 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:| | 0.5939 | 1.0 | 71 | 0.5989 | 0.0533 | 0.6635 | 1.0 | 0.0274 | | 0.5903 | 2.0 |... | 07c9789fcf432ca2eeeda814c73fc96c |
other | ['vision', 'image-classification'] | false | MobileNet V2 MobileNet V2 model pre-trained on ImageNet-1k at resolution 96x96. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](h... | 3022aecd9821f81adddeef243b072be2 |
other | ['vision', 'image-classification'] | false | Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embe... | 4758c7f2670746fc6f7c5fc7c48b93ff |
other | ['vision', 'image-classification'] | false | Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) to look for fine-tuned versions on a task that interests you. | f27c5b56e0511089292012c11cecc0a1 |
other | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | f25de968ad6c3aa6b1ce90846367dd94 |
other | ['vision', 'image-classification'] | false | BibTeX entry and citation info ```bibtex @inproceedings{mobilenetv22018, title={MobileNetV2: Inverted Residuals and Linear Bottlenecks}, author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen}, booktitle={CVPR}, year={2018} } ``` | 41eccfb93793b2a638842bf7382e7ad6 |
apache-2.0 | ['generated_from_trainer'] | false | model_broadclass_onSet2.1 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: 0.1459 - 0 Precision: 0.9630 - 0 Recall: 1.0 - 0 F1-score: 0.9811 - 0 Support:... | dd28d8b8d72bb61ba4359cf715084750 |
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 ... | 87af0ef0d99dfc9ebc1e8eeb0f7221cc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-seed-420-finetuned-squad-seed-420 This model is a fine-tuned version of [zates/distilbert-base-uncased-finetuned-squad-seed-420](https://huggingface.co/zates/distilbert-base-uncased-finetuned-squad-seed-420) on the squad_v2 dataset. | 0f2e45f3f53549b0e74390e248bbcdb5 |
apache-2.0 | ['generated_from_trainer'] | false | distilbart-cnn-arxiv-pubmed-v3-e8 This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8329 - Rouge1: 53.3047 - Rouge2: 34.6219 - Rouge... | d5bff24940af801554a3a6e034ad995c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 1.1211 | 50.4753 | 30.5417 | 33.192 | 48.1321 | ... | 65a205d0275397a1a075668688a28957 |
afl-3.0 | [] | false | GPT2 trained to generate ЗНО (Ukrainian exam SAT type of thing) essays Generated texts are not very cohesive yet but I'm working on it. <br /> The Hosted inference API outputs (on the right) are too short for some reason. Trying to fix it. <br /> Use the code from the example below. The model takes "ZNOTITLE: your es... | 77e1d663e65605db7dc68fa8c5f9e6a7 |
afl-3.0 | [] | false | Example of usage: ```python from transformers import AlbertTokenizer, GPT2LMHeadModel tokenizer = AlbertTokenizer.from_pretrained("kyryl0s/gpt2-uk-zno-edition") model = GPT2LMHeadModel.from_pretrained("kyryl0s/gpt2-uk-zno-edition") input_ids = tokenizer.encode("ZNOTITLE: За яку працю треба більше поважати людину - за ... | 6e71332e7c51214b096376e963427909 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.