Datasets:
davanstrien
/
model_cards_with_readmes_sections

Dataset card Data Studio Files
xet
Community
1
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: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP
599670989eac6b0911994970b4e847a5
mit
['roberta']
false
Indo-roberta-indonli Indo-roberta-indonli is natural language inference classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLI](https://github.com/ir-nlp-csui/indonli/tree/main/data/indonli) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to a natural inference classifier model. The model are tested using the validation, test_layer and test_expert dataset given in the github repository. The results are shown below.
97ef7ab2d4bf7677b877cbaaf41c5fca
mit
['roberta']
false
Result | Dataset | Accuracy | F1 | Precision | Recall | |-------------|----------|---------|-----------|---------| | Test Lay | 0.74329 | 0.74075 | 0.74283 | 0.74133 | | Test Expert | 0.6115 | 0.60543 | 0.63924 | 0.61742 |
e2fa1007b9cfce9a18061524da092ff7
mit
['roberta']
false
Model The model was trained on with 5 epochs, batch size 16, learning rate 2e-5 and weight decay 0.01. Achieved different metrics as shown below. | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |-------|---------------|-----------------|----------|----------|-----------|----------| | 1 | 0.942500 | 0.658559 | 0.737369 | 0.735552 | 0.735488 | 0.736679 | | 2 | 0.649200 | 0.645290 | 0.761493 | 0.759593 | 0.762784 | 0.759642 | | 3 | 0.437100 | 0.667163 | 0.766045 | 0.763979 | 0.765740 | 0.763792 | | 4 | 0.282000 | 0.786683 | 0.764679 | 0.761802 | 0.762011 | 0.761684 | | 5 | 0.193500 | 0.925717 | 0.765134 | 0.763127 | 0.763560 | 0.763489 |
37c4481cc29f2649d83f36be5935b03f
mit
['roberta']
false
As NLI Classifier ```python from transformers import pipeline pretrained_name = "StevenLimcorn/indonesian-roberta-indonli" nlp = pipeline( "zero-shot-classification", model=pretrained_name, tokenizer=pretrained_name ) nlp("Amir Sjarifoeddin Harahap lahir di Kota Medan, Sumatera Utara, 27 April 1907. Ia meninggal di Surakarta, Jawa Tengah, pada 19 Desember 1948 dalam usia 41 tahun. </s></s> Amir Sjarifoeddin Harahap masih hidup.") ```
a9ba84ce9b452f491b02d422da85612a
mit
['roberta']
false
Author Indonesian RoBERTa Base IndoNLI was trained and evaluated by [Steven Limcorn](https://github.com/stevenlimcorn). All computation and development are done on Google Colaboratory using their free GPU access.
d9e30e745671c9877f30acdc26ba3ffd
mit
['roberta']
false
Reference The dataset we used is by IndoNLI. ``` @inproceedings{indonli, title = "IndoNLI: A Natural Language Inference Dataset for Indonesian", author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", publisher = "Association for Computational Linguistics", } ```
59c2364d4a1bfdc665d7f833779429a4
other
['generated_from_trainer']
false
FoodAds_text-generator_opt350m This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9823
26d4bc78d2e641db84ad52e76154685c
other
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 27 | 4.5522 | | No log | 2.0 | 54 | 4.1730 | | No log | 3.0 | 81 | 4.0330 | | No log | 4.0 | 108 | 3.9801 | | No log | 5.0 | 135 | 3.9823 |
0e1849193915be12875fafd1de506932
apache-2.0
['generated_from_trainer']
false
Tagged_Uni_500v9_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni500v9_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2209 - Precision: 0.7117 - Recall: 0.7177 - F1: 0.7146 - Accuracy: 0.9351
1470fbafad58eee4f4a6a297bbd19eab
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 165 | 0.2693 | 0.5953 | 0.5249 | 0.5579 | 0.9126 | | No log | 2.0 | 330 | 0.2203 | 0.6916 | 0.6853 | 0.6884 | 0.9313 | | No log | 3.0 | 495 | 0.2209 | 0.7117 | 0.7177 | 0.7146 | 0.9351 |
bc9cf5e6ca730665e71a8952e012633d
apache-2.0
['generated_from_trainer']
false
recipe-lr1e05-wd0.02-bs8 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2781 - Rmse: 0.5273 - Mse: 0.2781 - Mae: 0.4279
93830dc9fe2bdb0edee969d4ff21078f
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2766 | 1.0 | 2490 | 0.2740 | 0.5234 | 0.2740 | 0.4172 | | 0.2738 | 2.0 | 4980 | 0.2783 | 0.5276 | 0.2783 | 0.4297 | | 0.2724 | 3.0 | 7470 | 0.2781 | 0.5273 | 0.2781 | 0.4279 |
d7924ec7ed16c534935c7b46ff71e943
apache-2.0
['generated_from_trainer']
false
bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0575 - Precision: 0.9322 - Recall: 0.9505 - F1: 0.9413 - Accuracy: 0.9860
cb1d48ba9fcebeb1184463f5949c3fb3
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2219 | 1.0 | 878 | 0.0716 | 0.9076 | 0.9288 | 0.9181 | 0.9808 | | 0.0453 | 2.0 | 1756 | 0.0597 | 0.9297 | 0.9477 | 0.9386 | 0.9852 | | 0.0239 | 3.0 | 2634 | 0.0575 | 0.9322 | 0.9505 | 0.9413 | 0.9860 |
9cc5ff54918ee696ec268f723da59d3f
mit
['spacy', 'token-classification']
false
This is a Spacy multilingual (Catalan & Spanish) anonimization model, for use with BSC's AnonymizationPipeline at: https://github.com/TeMU-BSC/AnonymizationPipeline. pip install https://huggingface.co/PlanTL-GOB-ES/es_anonimization_core_lg/resolve/main/es_anonimization_core_lg-any-py3-none-any.whl The anonymization pipeline is a library for performing sensitive data identification and ultimately anonymization of the detected data in Spanish and Catalan user generated plain text. This is not a standalone model and is meant to work within the pipeline. The model can detect the following entities: `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP` | Feature | Description | | --- | --- | | **Name** | `ca_anonimization_core_lg` | | **Version** | `1.0.0` | | **spaCy** | `>=3.2.3,<4.0.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) | | **Sources** | n/a | | **License** | `MIT` | | **Author** | [Joaquin Silveira](https://github.com/TeMU-BSC/AnonymizationPipeline) |
847f4a7ce40e56e56a0cf77e4b0e4fed
mit
['spacy', 'token-classification']
false
Label Scheme <details> <summary>View label scheme (322 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumForm=Digit\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Comm`, `POS=AUX\|VerbForm=Inf`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Peri`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `POS=SCONJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=SYM`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|Polarity=Neg`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Ind`, `POS=PUNCT`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Semi`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Dash`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Colo`, `Gender=Masc\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Quot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `POS=VERB`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `POS=X`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `NumForm=Digit\|NumType=Ord\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=PRON\|PronType=Int`, `Foreign=Yes\|Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Foreign=Yes\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Comm`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Comm`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `AdvType=Tim\|Degree=Cmp\|POS=ADV`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Pre\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `POS=INTJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=SCONJ`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=SYM`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|Punta d'aignctType=Brck`, `Degree=Cmp\|POS=ADJ`, `AdvType=Tim\|POS=SYM`, `Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` | | **`ner`** | `EMAIL`, `FINANCIAL`, `ID`, `LOC`, `MISC`, `ORG`, `PER`, `TELEPHONE`, `VEHICLE`, `ZIP` | </details>
b7b7e508bee065c6187b7c4730647735
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7710 - Accuracy: 0.9177
6cb771b18d2c958f0d2a42ae0d9e8c4f
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2892 | 1.0 | 318 | 3.2830 | 0.7432 | | 2.627 | 2.0 | 636 | 1.8728 | 0.8403 | | 1.5429 | 3.0 | 954 | 1.1554 | 0.8910 | | 1.0089 | 4.0 | 1272 | 0.8530 | 0.9129 | | 0.7938 | 5.0 | 1590 | 0.7710 | 0.9177 |
a0f4dc6becf4737060cf4d95ab059355
mit
['generated_from_trainer']
false
japanese-roberta-base-finetuned-wikitext2 This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2302
7a96652850ad486895912c6f008ba08c
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 18 | 3.4128 | | No log | 2.0 | 36 | 3.1374 | | No log | 3.0 | 54 | 3.2285 |
184fe45b42d06fa0198a0946feb8bd82
mit
['generated_from_trainer']
false
ru_t5absum_for_legaltext This model is a fine-tuned version of [cointegrated/rut5-base-absum](https://huggingface.co/cointegrated/rut5-base-absum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 1.4175 - Rouge2: 0.0 - Rougel: 1.4476 - Rougelsum: 1.4302 - Gen Len: 17.2133
d564c7248721733796646256066fd59f
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 157 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | No log | 2.0 | 314 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | No log | 3.0 | 471 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 4.0 | 628 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 5.0 | 785 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 6.0 | 942 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 7.0 | 1099 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 8.0 | 1256 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 9.0 | 1413 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 | | 0.0 | 10.0 | 1570 | nan | 1.4175 | 0.0 | 1.4476 | 1.4302 | 17.2133 |
7938bb36d9227a392d30ee355b0a4805
mit
[]
false
moebius on Stable Diffusion This is the `<moebius>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<moebius> 0](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/26.jpeg) ![<moebius> 1](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/0.jpeg) ![<moebius> 2](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/31.jpeg) ![<moebius> 3](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/8.jpeg) ![<moebius> 4](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/3.jpeg) ![<moebius> 5](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/5.jpeg) ![<moebius> 6](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/22.jpeg) ![<moebius> 7](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/29.jpeg) ![<moebius> 8](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/6.jpeg) ![<moebius> 9](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/30.jpeg) ![<moebius> 10](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/11.jpeg) ![<moebius> 11](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/27.jpeg) ![<moebius> 12](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/1.jpeg) ![<moebius> 13](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/25.jpeg) ![<moebius> 14](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/21.jpeg) ![<moebius> 15](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/14.jpeg) ![<moebius> 16](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/15.jpeg) ![<moebius> 17](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/23.jpeg) ![<moebius> 18](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/17.jpeg) ![<moebius> 19](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/16.jpeg) ![<moebius> 20](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/10.jpeg) ![<moebius> 21](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/2.jpeg) ![<moebius> 22](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/28.jpeg) ![<moebius> 23](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/12.jpeg) ![<moebius> 24](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/19.jpeg) ![<moebius> 25](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/4.jpeg) ![<moebius> 26](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/7.jpeg) ![<moebius> 27](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/24.jpeg) ![<moebius> 28](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/9.jpeg) ![<moebius> 29](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/20.jpeg) ![<moebius> 30](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/18.jpeg) ![<moebius> 31](https://huggingface.co/sd-concepts-library/moebius/resolve/main/concept_images/13.jpeg)
8ca4f3d654beab42fcd47c2ed8af1f09
apache-2.0
['generated_from_trainer']
false
DistilBert-finetuned-Hackaton 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: 2.1456 - Accuracy: 0.4283 - F1: 0.4344
a913c159f5024f45734e286ae332703b
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 25
fa16939e85a0f6ef29d8a031fdfaa357
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 2.3155 | 1.0 | 338 | 2.6640 | 0.33 | 0.3161 | | 2.2064 | 2.0 | 676 | 2.5991 | 0.3283 | 0.3094 | | 2.0703 | 3.0 | 1014 | 2.5172 | 0.3467 | 0.3347 | | 2.0222 | 4.0 | 1352 | 2.4497 | 0.3567 | 0.3434 | | 1.9197 | 5.0 | 1690 | 2.3951 | 0.375 | 0.3639 | | 1.8334 | 6.0 | 2028 | 2.3398 | 0.375 | 0.3646 | | 1.7327 | 7.0 | 2366 | 2.3231 | 0.3833 | 0.3749 | | 1.6621 | 8.0 | 2704 | 2.3040 | 0.3867 | 0.3787 | | 1.5902 | 9.0 | 3042 | 2.2702 | 0.3883 | 0.3809 | | 1.5554 | 10.0 | 3380 | 2.2230 | 0.4167 | 0.4143 | | 1.5008 | 11.0 | 3718 | 2.2277 | 0.4067 | 0.3999 | | 1.4451 | 12.0 | 4056 | 2.2023 | 0.4033 | 0.4025 | | 1.3788 | 13.0 | 4394 | 2.1953 | 0.41 | 0.4066 | | 1.3418 | 14.0 | 4732 | 2.1774 | 0.4083 | 0.4036 | | 1.2689 | 15.0 | 5070 | 2.1798 | 0.41 | 0.4123 | | 1.2495 | 16.0 | 5408 | 2.1700 | 0.4233 | 0.4228 | | 1.1946 | 17.0 | 5746 | 2.1653 | 0.42 | 0.4241 | | 1.1652 | 18.0 | 6084 | 2.1672 | 0.4283 | 0.4279 | | 1.1428 | 19.0 | 6422 | 2.1631 | 0.4217 | 0.4259 | | 1.1027 | 20.0 | 6760 | 2.1501 | 0.4133 | 0.4189 | | 1.063 | 21.0 | 7098 | 2.1522 | 0.4183 | 0.4244 | | 1.0621 | 22.0 | 7436 | 2.1480 | 0.42 | 0.4258 | | 1.0412 | 23.0 | 7774 | 2.1491 | 0.4217 | 0.4285 | | 1.0311 | 24.0 | 8112 | 2.1493 | 0.4267 | 0.4333 | | 1.0195 | 25.0 | 8450 | 2.1456 | 0.4283 | 0.4344 |
1d225fdef6126564462dac67c8b20fef
apache-2.0
['generated_from_trainer']
false
bert-base-uncased-finetuned-berttokenizer-shards_ext_ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0079
6f6f464e806ab27806be038f9766c103
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 2.1965 | 1.0 | 43009 | 2.0675 | | 1.9985 | 2.0 | 86018 | 2.0185 | | 1.8484 | 3.0 | 129027 | 2.0079 |
6d0e934098f6ab287ecdf409bcb3faf4
apache-2.0
['automatic-speech-recognition', 'sv-SE']
false
exp_w2v2t_sv-se_hubert_s930 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) 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.
de087d4e428d2ac462195811c4a0c839
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
Model description This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`facebook/dino-vitb16` DINO checkpoint](https://huggingface.co/facebook/dino-vitb16).
f72159474f0fd5fda158258332ceccee
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
sk-58b9b78a-229c-4a6a-b67b-244945cdc29d 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;}
5d406b3c54ef3d86e766c2dc444e1dc8
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
sk-58b9b78a-229c-4a6a-b67b-244945cdc29d 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;}
e09e1afbe62ecb8634429411d12fc840
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
sk-58b9b78a-229c-4a6a-b67b-244945cdc29d 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;}
73f117054b151f1a2b438352e59698bd
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
sk-58b9b78a-229c-4a6a-b67b-244945cdc29d div.sk-text-repr-fallback {display: none;}</style><div id="sk-58b9b78a-229c-4a6a-b67b-244945cdc29d" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="d612eebc-39a3-42fc-99e0-37e6f258ac21" type="checkbox" checked><label for="d612eebc-39a3-42fc-99e0-37e6f258ac21" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div>
d37732d36d096ccea32a70bff3aad634
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
load embedding model device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/dino-vitb16') model = AutoModel.from_pretrained('facebook/dino-vitb16').eval().to(device)
261df387162167ce6f869a05c11b7652
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
load logistic regression os.mkdir('emb-gam-dino') hub_utils.download(repo_id='Ramos-Ramos/emb-gam-dino', dst='emb-gam-dino') with open('emb-gam-dino/model.pkl', 'rb') as file: logistic_regression = pickle.load(file)
4c633721bb4ebc62b244389181d5a37e
mit
['sklearn', 'skops', 'tabular-classification', 'visual emb-gam']
false
Citation **BibTeX:** ``` @article{singh2022emb, title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models}, author={Singh, Chandan and Gao, Jianfeng}, journal={arXiv preprint arXiv:2209.11799}, year={2022} } ```
8cd3a91f57cb2883a9f7205ed35a5089
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
Anything V3.1 ![Anime Girl](https://huggingface.co/cag/anything-v3-1/resolve/main/example-images/thumbnail.png) Anything V3.1 is a third-party continuation of a latent diffusion model, Anything V3.0. This model is claimed to be a better version of Anything V3.0 with a fixed VAE model and a fixed CLIP position id key. The CLIP reference was taken from Stable Diffusion V1.5. The VAE was swapped using Kohya's merge-vae script and the CLIP was fixed using Arena's stable-diffusion-model-toolkit webui extensions. Anything V3.2 is supposed to be a resume training of Anything V3.1. The current model has been fine-tuned with a learning rate of 2.0e-6, 50 epochs, and 4 batch sizes on datasets collected from many sources, with 1/4 of them being synthetic datasets. The dataset has been preprocessed using the Aspect Ratio Bucketing Tool so that it can be converted to latents and trained at non-square resolutions. This model is supposed to be a test model to see how the clip fix affects training. Like other anime-style Stable Diffusion models, it also supports Danbooru tags to generate images. e.g. **_1girl, white hair, golden eyes, beautiful eyes, detail, flower meadow, cumulonimbus clouds, lighting, detailed sky, garden_** - Use it with the [`Automatic1111's Stable Diffusion Webui`](https://github.com/AUTOMATIC1111/stable-diffusion-webui) see: ['how-to-use'](
f199cbaa81ce2da5e91e98eff0a7cdf2
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
Model Details - **Currently maintained by:** Cagliostro Research Lab - **Model type:** Diffusion-based text-to-image generation model - **Model Description:** This is a model that can be used to generate and modify anime-themed images based on text prompts. - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Finetuned from model:** Anything V3.1
ae36c9624fcfb14db22802786b0129db
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
How-to-Use - Download `Anything V3.1` [here](https://huggingface.co/cag/anything-v3-1/resolve/main/anything-v3-1.safetensors), or `Anything V3.2` [here](https://huggingface.co/cag/anything-v3-1/resolve/main/anything-v3-2.safetensors), all model are in `.safetensors` format. - You need to adjust your prompt using aesthetic tags to get better result, you can use any generic negative prompt or use the following suggested negative prompt to guide the model towards high aesthetic generationse: ``` lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry ``` - And, the following should also be prepended to prompts to get high aesthetic results: ``` masterpiece, best quality, illustration, beautiful detailed, finely detailed, dramatic light, intricate details ```
a23b205bfccd356373aa9cece4f635d2
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
🧨Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). Pretrained model currently based on Anything V3.1. You should install dependencies below in order to running the pipeline ```bash pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to DPMSolverMultistepScheduler): ```python import torch from torch import autocast from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler model_id = "cag/anything-v3-1"
0978d2c0c20c93795720ad44d8e2e80c
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "masterpiece, best quality, high quality, 1girl, solo, sitting, confident expression, long blonde hair, blue eyes, formal dress" negative_prompt = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry" with autocast("cuda"): image = pipe(prompt, negative_prompt=negative_prompt, width=512, height=728, guidance_scale=12, num_inference_steps=50).images[0] image.save("anime_girl.png") ```
171f0b3b6bb8a4055d7cdd468263b5f4
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
Limitation This model is overfitted and cannot follow prompts well, even after the text encoder has been fixed. This leads to laziness in prompting, as you will only get good results by typing 1girl. Additionally, this model is anime-based and biased towards anime female characters. It is difficult to generate masculine male characters without providing specific prompts. Furthermore, not much has changed compared to the Anything V3.0 base model, as it only involved swapping the VAE and CLIP models and then fine-tuning for 50 epochs with small scale datasets.
a8ec7edfa680e3762272fabfdd81523a
creativeml-openrail-m
['stable-diffusion', 'stable-diffusion-diffusers']
false
Example Here is some cherrypicked samples and comparison between available models ![Anime Girl](https://huggingface.co/cag/anything-v3-1/resolve/main/example-images/1girl.png) ![Anime Boy](https://huggingface.co/cag/anything-v3-1/resolve/main/example-images/1boy.png) ![Aesthetic](https://huggingface.co/cag/anything-v3-1/resolve/main/example-images/aesthetic.png)
03ddbf28133e0d8da9261cf8beb5f220
mit
[]
false
GHOST style on Stable Diffusion This is the `<ghost>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<ghost> 0](https://huggingface.co/sd-concepts-library/ghost-style/resolve/main/concept_images/2.jpeg) ![<ghost> 1](https://huggingface.co/sd-concepts-library/ghost-style/resolve/main/concept_images/0.jpeg) ![<ghost> 2](https://huggingface.co/sd-concepts-library/ghost-style/resolve/main/concept_images/1.jpeg) ![<ghost> 3](https://huggingface.co/sd-concepts-library/ghost-style/resolve/main/concept_images/3.jpeg) ![<ghost> 4](https://huggingface.co/sd-concepts-library/ghost-style/resolve/main/concept_images/4.jpeg)
432ce8eeec0a1f662e5eeb0d62db038e
apache-2.0
['multiberts', 'multiberts-seed_4']
false
MultiBERTs - Seed 4 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model
60110416831a3983744f3f1ecf9b2d1e
apache-2.0
['multiberts', 'multiberts-seed_4']
false
How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4') model = TFBertModel.from_pretrained("google/multiberts-seed_4") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4') model = BertModel.from_pretrained("google/multiberts-seed_4") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ```
81dbf87c0483aea61dd3a1b70437fe33
apache-2.0
['generated_from_trainer', 'text-generation', 'opt', 'non-commercial']
false
OPT-Peter-1.3B-1E > This is an initial checkpoint of the model - the latest version is [here](https://huggingface.co/pszemraj/opt-peter-1.3B) This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on text message data (mine) for 1.6 epochs. It achieves the following results on the evaluation set (at the end of epoch 1): - eval_loss: 3.3595 - eval_runtime: 988.6985 - eval_samples_per_second: 8.803 - eval_steps_per_second: 2.201 - epoch: 1.0 - step: 1235
460db707e6fa051cb5f9ee553654356e
apache-2.0
['generated_from_trainer', 'text-generation', 'opt', 'non-commercial']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1.6
66c67da55790e906283352a01566c50f
mit
['generated_from_trainer']
false
twitter-data-xlm-roberta-base-eng-only-sentiment-finetuned-memes This model is a fine-tuned version of [jayantapaul888/twitter-data-xlm-roberta-base-sentiment-finetuned-memes](https://huggingface.co/jayantapaul888/twitter-data-xlm-roberta-base-sentiment-finetuned-memes) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6286 - Accuracy: 0.8660 - Precision: 0.8796 - Recall: 0.8795 - F1: 0.8795
501dbecc43e908fc22a550110cdf3288
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 378 | 0.3421 | 0.8407 | 0.8636 | 0.8543 | 0.8553 | | 0.396 | 2.0 | 756 | 0.3445 | 0.8496 | 0.8726 | 0.8634 | 0.8631 | | 0.2498 | 3.0 | 1134 | 0.3656 | 0.8585 | 0.8764 | 0.8727 | 0.8723 | | 0.1543 | 4.0 | 1512 | 0.4549 | 0.8600 | 0.8742 | 0.8740 | 0.8741 | | 0.1543 | 5.0 | 1890 | 0.5932 | 0.8645 | 0.8783 | 0.8780 | 0.8780 | | 0.0815 | 6.0 | 2268 | 0.6286 | 0.8660 | 0.8796 | 0.8795 | 0.8795 |
4a35923237e565159af8f006a8fcb65f
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased-finetuned-squad_v2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.3949
f414a276d95a4fa2e9dafd74a26b2280
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1996 | 1.0 | 8235 | 1.2485 | | 0.9303 | 2.0 | 16470 | 1.2147 | | 0.7438 | 3.0 | 24705 | 1.3949 |
3aea705ff1eaeb179289715621f5b2e8
mit
[]
false
Mona (Genshin Impact) on Stable Diffusion This is the Mona concept taught to Stable Diffusion via Textual Inversion. You can load this concept into a Stable Diffusion fork such as this [repo](https://github.com/AUTOMATIC1111/stable-diffusion-webui) (Instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase
24529aa690657107a208a46a1ef6cdb0
apache-2.0
['roberta', 'NLU', 'Similarity', 'Chinese']
false
模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | Roberta | 330M | 中文-相似度 Similarity |
9a20799a51ce39e1f141442886a70f4c
apache-2.0
['roberta', 'NLU', 'Similarity', 'Chinese']
false
模型信息 Model Information 基于[chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)，我们在收集的20个中文领域的改写数据集，总计2773880个样本上微调了一个Similarity版本。 Based on [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large), we fine-tuned a similarity version on 20 Chinese paraphrase datasets, with totaling 2,773,880 samples.
3889145c9de262bbde178d95c2984110
apache-2.0
['roberta', 'NLU', 'Similarity', 'Chinese']
false
下游效果 Performance | Model | BQ | BUSTM | AFQMC | | :--------: | :-----: | :----: | :-----: | | Erlangshen-Roberta-110M-Similarity | 85.41 | 95.18 | 81.72 | | Erlangshen-Roberta-330M-Similarity | 86.21 | 99.29 | 93.89 | | Erlangshen-MegatronBert-1.3B-Similarity | 86.31 | - | - |
cea8a3aecebe312a25d56346b7e6ce22
apache-2.0
['roberta', 'NLU', 'Similarity', 'Chinese']
false
使用 Usage ``` python from transformers import BertForSequenceClassification from transformers import BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Similarity') model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-330M-Similarity') texta='今天的饭不好吃' textb='今天心情不好' output=model(torch.tensor([tokenizer.encode(texta,textb)])) print(torch.nn.functional.softmax(output.logits,dim=-1)) ```
f5cea9785c263e7123ef6005323961dd
apache-2.0
['generated_from_keras_callback']
false
donyd/distilbert-finetuned-imdb 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: 2.8432 - Validation Loss: 2.6247 - Epoch: 0
327b6980c7de36c63125b00b22924b5e
mit
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
MPNet NLI and STS This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. It uses the [jamescalam/mpnet-snli-negatives](https://huggingface.co/jamescalam/mpnet-snli-negatives) model as a starting point, and is fine-tuned further on the **S**emantic **T**extual **S**imilarity **b**enchmark (STSb) dataset. Returning evaluation scores of ~0.9 cosine Pearson correlation using the STSb test set. Find more info from [James Briggs on YouTube](https://youtube.com/c/jamesbriggs) or in the [**free** NLP for Semantic Search ebook](https://pinecone.io/learn/nlp). <!--- Describe your model here -->
8df321d45cd7012fd22c251ef64fbfcb
mit
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('jamescalam/mpnet-nli-sts') embeddings = model.encode(sentences) print(embeddings) ```
578759c16275f750f42f1601806e7c2d
mit
['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers']
false
Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 180 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 25, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 90, "weight_decay": 0.01 } ```
3d79fcedea2bd325967b75446071585d
other
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 100 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 4 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0
d8aa265900668e97d2aa66104c0f573f
apache-2.0
['generated_from_trainer']
false
bert-base-uncased-lm-all This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8646
472a6a6cb2a31bf780e2b68616ecb22e
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.6625 | 1.0 | 1194 | 1.3270 | | 1.3001 | 2.0 | 2388 | 1.1745 | | 1.1694 | 3.0 | 3582 | 1.1133 | | 1.0901 | 4.0 | 4776 | 1.0547 | | 1.0309 | 5.0 | 5970 | 0.9953 | | 0.9842 | 6.0 | 7164 | 0.9997 | | 0.9396 | 7.0 | 8358 | 0.9707 | | 0.8997 | 8.0 | 9552 | 0.9324 | | 0.8633 | 9.0 | 10746 | 0.9145 | | 0.8314 | 10.0 | 11940 | 0.9047 | | 0.812 | 11.0 | 13134 | 0.8954 | | 0.7841 | 12.0 | 14328 | 0.8940 | | 0.7616 | 13.0 | 15522 | 0.8555 | | 0.7508 | 14.0 | 16716 | 0.8711 | | 0.7333 | 15.0 | 17910 | 0.8351 | | 0.7299 | 16.0 | 19104 | 0.8646 |
4f293193a06c8f6dcabac198951d3724
apache-2.0
['automatic-speech-recognition', 'es']
false
exp_w2v2t_es_vp-100k_s732 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) 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.
897bf2f632e4d850c84516d08e9dcc6f
apache-2.0
['generated_from_trainer']
false
distilbert-base-uncased__hate_speech_offensive__train-32-1 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: 1.0606 - Accuracy: 0.4745
31e2840f9a42c90b6fac0d4a28c7df57
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0941 | 1.0 | 19 | 1.1045 | 0.2 | | 0.9967 | 2.0 | 38 | 1.1164 | 0.35 | | 0.8164 | 3.0 | 57 | 1.1570 | 0.4 | | 0.5884 | 4.0 | 76 | 1.2403 | 0.35 | | 0.3322 | 5.0 | 95 | 1.3815 | 0.35 | | 0.156 | 6.0 | 114 | 1.8102 | 0.3 | | 0.0576 | 7.0 | 133 | 2.1439 | 0.4 | | 0.0227 | 8.0 | 152 | 2.4368 | 0.3 | | 0.0133 | 9.0 | 171 | 2.5994 | 0.4 | | 0.009 | 10.0 | 190 | 2.7388 | 0.35 | | 0.0072 | 11.0 | 209 | 2.8287 | 0.35 |
9e58c0a93e298c1d5864b0fb9c741064
mit
['generated_from_trainer']
false
SentimentBert This model is a fine-tuned version of [cahya/bert-base-indonesian-522M](https://huggingface.co/cahya/bert-base-indonesian-522M) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2005 - Accuracy: 0.965
61627aa53534beae5e846f94dd3d4271
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 275 | 0.7807 | 0.715 | | 0.835 | 2.0 | 550 | 1.0588 | 0.635 | | 0.835 | 3.0 | 825 | 0.2764 | 0.94 | | 0.5263 | 4.0 | 1100 | 0.1913 | 0.97 | | 0.5263 | 5.0 | 1375 | 0.2005 | 0.965 |
164552cb3d469091a5d7f6ce8c0009de
apache-2.0
['automatic-speech-recognition', 'id']
false
exp_w2v2t_id_vp-fr_s27 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (id)](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.
8cfdea2b6c461c41ca9a422f6e5e3125
apache-2.0
['translation']
false
opus-mt-fi-sk * source languages: fi * target languages: sk * OPUS readme: [fi-sk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-sk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-sk/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sk/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-sk/opus-2020-01-08.eval.txt)
f639a0eb3b681400806a1ab763cb5e5d
mit
['generated_from_trainer']
false
roberta-large-finetuned-combined-DS This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2062 - Accuracy: 0.7001 - Precision: 0.6703 - Recall: 0.6700 - F1: 0.6701
cce4a824e8c4678429f14ff67bc44b18
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.8804 | 1.0 | 711 | 0.8517 | 0.6573 | 0.6786 | 0.6253 | 0.6231 | | 0.6949 | 2.0 | 1422 | 0.7444 | 0.6833 | 0.6609 | 0.6647 | 0.6604 | | 0.5674 | 3.0 | 2133 | 0.8379 | 0.6798 | 0.6571 | 0.6659 | 0.6575 | | 0.433 | 3.99 | 2844 | 0.8703 | 0.7079 | 0.6947 | 0.6801 | 0.6809 | | 0.3314 | 4.99 | 3555 | 1.1792 | 0.6861 | 0.6672 | 0.6558 | 0.6569 | | 0.2519 | 5.99 | 4266 | 1.5574 | 0.6966 | 0.6761 | 0.6639 | 0.6662 | | 0.2083 | 6.99 | 4977 | 1.8781 | 0.6952 | 0.6681 | 0.6592 | 0.6619 | | 0.1773 | 7.99 | 5688 | 1.8687 | 0.6959 | 0.6677 | 0.6748 | 0.6675 | | 0.1536 | 8.99 | 6399 | 2.2483 | 0.7037 | 0.6788 | 0.6674 | 0.6694 | | 0.1305 | 9.99 | 7110 | 2.4602 | 0.6875 | 0.6597 | 0.6681 | 0.6612 | | 0.0982 | 10.98 | 7821 | 2.5573 | 0.6994 | 0.6705 | 0.6728 | 0.6709 | | 0.0858 | 11.98 | 8532 | 2.8048 | 0.6994 | 0.6765 | 0.6730 | 0.6737 | | 0.0734 | 12.98 | 9243 | 3.0408 | 0.6945 | 0.6640 | 0.6628 | 0.6626 | | 0.0625 | 13.98 | 9954 | 3.0047 | 0.7037 | 0.6784 | 0.6757 | 0.6764 | | 0.0434 | 14.98 | 10665 | 3.0789 | 0.6987 | 0.6737 | 0.6669 | 0.6691 | | 0.0432 | 15.98 | 11376 | 2.9647 | 0.6945 | 0.6649 | 0.6684 | 0.6663 | | 0.0326 | 16.98 | 12087 | 3.3076 | 0.6931 | 0.6630 | 0.6563 | 0.6583 | | 0.032 | 17.97 | 12798 | 3.1890 | 0.7022 | 0.6737 | 0.6702 | 0.6717 | | 0.0275 | 18.97 | 13509 | 3.1798 | 0.7029 | 0.6738 | 0.6750 | 0.6744 | | 0.0251 | 19.97 | 14220 | 3.2062 | 0.7001 | 0.6703 | 0.6700 | 0.6701 |
fef02d2c50781beb1155b89bc4691180
mit
['generated_from_trainer']
false
tolkien-mythopoeic-gen This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on Tolkien's mythopoeic works, namely The Silmarillion and Unfinished Tales of Numenor and Middle Earth. It achieves the following results on the evaluation set: - Loss: 3.5110
2f0599c26f9a72fb8ff25f3209f7a904
mit
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.5732 | 1.0 | 145 | 3.5110 | | 3.5713 | 2.0 | 290 | 3.5110 | | 3.5718 | 3.0 | 435 | 3.5110 |
fae0a874ddf8ce4b63317a53df32500a
apache-2.0
['generated_from_trainer']
false
t5-small-finetuned-de-en-epochs5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.2040 - Bleu: 5.8913 - Gen Len: 17.5408
87fad4029eeae04c5fd917c673a97435
apache-2.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.3366 | 2.8075 | 17.8188 | | No log | 2.0 | 376 | 2.2557 | 4.8765 | 17.626 | | 2.6928 | 3.0 | 564 | 2.2246 | 5.5454 | 17.5534 | | 2.6928 | 4.0 | 752 | 2.2086 | 5.8511 | 17.5461 | | 2.6928 | 5.0 | 940 | 2.2040 | 5.8913 | 17.5408 |
73316192fc3813a81291342edfc420c9
mit
['generated_from_trainer']
false
discourse_classification_using_robrta_base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0832 - Accuracy: 0.6592 - F1: 0.6592
3a827981c61d5d15374ee59e62d421b8
mit
['summarization', 'translation', 'question-answering']
false
How to use For more details, do check out [our Github repo](https://github.com/vietai/ViT5). [Finetunning Example can be found here](https://github.com/vietai/ViT5/tree/main/finetunning_huggingface). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM ​ tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-large") model = AutoModelForSeq2SeqLM.from_pretrained("VietAI/vit5-large") model.cuda() ```
69b9c38332182443e4a1cc9a3fdf2861
apache-2.0
['vision', 'image-classification']
false
LeViT LeViT-256 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference ](https://arxiv.org/abs/2104.01136) by Graham et al. and first released in [this repository](https://github.com/facebookresearch/LeViT). Disclaimer: The team releasing LeViT did not write a model card for this model so this model card has been written by the Hugging Face team.
3bc081545287318465d6198ffe65e15e
apache-2.0
['vision', 'image-classification']
false
Usage 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 LevitFeatureExtractor, LevitForImageClassificationWithTeacher from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = LevitFeatureExtractor.from_pretrained('facebook/levit-256') model = LevitForImageClassificationWithTeacher.from_pretrained('facebook/levit-256') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits
021d071edde29443f4924d47815fa207
apache-2.0
['automatic-speech-recognition', 'ja']
false
exp_w2v2t_ja_vp-nl_s287 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 (ja)](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.
2a5e8bdbb8f73e4fe1df1c670b816a92
mit
['generated_from_trainer']
false
eloquent_stallman This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets.
c1d16fe2f4b5c80c47101d234f52aad8
mit
['generated_from_trainer']
false
Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'filter_threshold': 0.00078, 'is_split_by_sentences': True, 'skip_tokens': 1661599744}, 'generation': {'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '81a1701e025d2c65ae6e8c2103df559071523ee0'}, 'path_or_name': 'tomekkorbak/goofy_pasteur'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'eloquent_stallman', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 25354, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 1661599744, 'warmup_ratio': 0.01, 'weight_decay': 0.1}}
d35fc4a4f07459c675ec44943721b69d
creativeml-openrail-m
['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard']
false
DreamBooth model for the abrozick concept trained by matallanas on the https://huggingface.co/datasets/matallanas/AbduRozik dataset. This is a Stable Diffusion model fine-tuned on the abrozick concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of abrozick** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part!
8ad0c4d3079a433ef0cc4610f6a2d686
apache-2.0
['generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000
659758054d600795a1c30a8db07a41c0
mit
[]
false
Model Description <!-- Provide a longer summary of what this model is. --> ['Royal_Institute_of_British_Architects', 'Westminster_Abbey', 'England_national_football_team', 'Order_of_the_British_Empire', 'Elizabeth_II', 'Queen_Victoria', 'Buckingham_Palace', 'Royal_Dutch_Shell', 'British_Empire', 'The_Sun_(United_Kingdom)', 'London', 'Labour_Party_(UK)', 'Liberal_Party_of_Australia', 'British_Isles'] - **Developed by:** nandysoham - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** [More Information Needed]
06668c57dff7fdce35c25cab384499ca
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Whisper Small Italian This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1185 - Wer: 17.3916
a5557e44cb2c2266cd3a9e9c17a2c1ee
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 <!-- - seed: 42 --> - gradient_accumulation_steps: 1 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 954 <!-- - mixed_precision_training: Native AMP -->
a4190c555dc6359e7c516eb87fa7232b
apache-2.0
['hf-asr-leaderboard', 'generated_from_trainer']
false
Training results | Training Loss | Step | Validation Loss | Wer | |:-------------:|:----:|:---------------:|:-------:| | 1.4744 | 100 | 1.1852 | 117.6059 | | 0.7241 | 200 | 0.7452 | 79.7386 | | 0.3321 | 300 | 0.3215 | 21.0497 | | 0.2930 | 400 | 0.3030 | 20.2129 | | 0.2698 | 500 | 0.2982 | 19.7635 | | 0.2453 | 600 | 0.2898 | 19.0097 | | 0.2338 | 700 | 0.2768 | 18.7054 | | 0.2402 | 800 | 0.2646 | 18.2214 | | 0.2340 | 900 | 0.2581 | 17.3916 |
fb68efcd00ef3c328ebf939c721a2ed2
mit
[]
false
Nebula on Stable Diffusion This is the `<nebula>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<nebula> 0](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/5.jpeg) ![<nebula> 1](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/4.jpeg) ![<nebula> 2](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/1.jpeg) ![<nebula> 3](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/2.jpeg) ![<nebula> 4](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/3.jpeg) ![<nebula> 5](https://huggingface.co/sd-concepts-library/nebula/resolve/main/concept_images/0.jpeg)
1233ebb3b8513bb42d085ec9b0fb96eb
agpl-3.0
['generated_from_trainer']
false
XLMR-ENIS-finetuned-ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0955 - Precision: 0.8714 - Recall: 0.8423 - F1: 0.8566 - Accuracy: 0.9827
6eb8aa4e7b16e13bdc0808aa7e9942ea
agpl-3.0
['generated_from_trainer']
false
Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0561 | 1.0 | 2904 | 0.0939 | 0.8481 | 0.8205 | 0.8341 | 0.9804 | | 0.031 | 2.0 | 5808 | 0.0917 | 0.8652 | 0.8299 | 0.8472 | 0.9819 | | 0.0186 | 3.0 | 8712 | 0.0955 | 0.8714 | 0.8423 | 0.8566 | 0.9827 |
0e2cf01ccd42992c53c60e77fd4d7f20
mit
[]
false
Beetlejuice Cartoon Style on Stable Diffusion This is the `<beetlejuice-cartoon>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<beetlejuice-cartoon> 0](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/4.jpeg) ![<beetlejuice-cartoon> 1](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/1.jpeg) ![<beetlejuice-cartoon> 2](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/2.jpeg) ![<beetlejuice-cartoon> 3](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/6.jpeg) ![<beetlejuice-cartoon> 4](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/7.jpeg) ![<beetlejuice-cartoon> 5](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/0.jpeg) ![<beetlejuice-cartoon> 6](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/8.jpeg) ![<beetlejuice-cartoon> 7](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/5.jpeg) ![<beetlejuice-cartoon> 8](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/9.jpeg) ![<beetlejuice-cartoon> 9](https://huggingface.co/sd-concepts-library/beetlejuice-cartoon-style/resolve/main/concept_images/3.jpeg)
d2fa7cfad758bfc3a1ee6d47ba55f2ef
apache-2.0
['generated_from_keras_callback']
false
Zynovia/vit-base-patch16-224-in21k-wwwwii This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8976 - Train Accuracy: 0.8813 - Train Top-3-accuracy: 0.9721 - Validation Loss: 1.6144 - Validation Accuracy: 0.5845 - Validation Top-3-accuracy: 0.7845 - Epoch: 4
fe704051248eb7d20703224b03db09fa
apache-2.0
['generated_from_keras_callback']
false
Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 6e-05, 'decay_steps': 4122, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16
7d6fbb6308d9a1a180656a089b51dc4b
apache-2.0
['generated_from_keras_callback']
false
Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 3.4972 | 0.1475 | 0.3067 | 3.0825 | 0.3240 | 0.5178 | 0 | | 2.7352 | 0.4129 | 0.6613 | 2.4838 | 0.4543 | 0.6930 | 1 | | 2.0429 | 0.6153 | 0.8315 | 1.9934 | 0.5690 | 0.7550 | 2 | | 1.4246 | 0.7672 | 0.9166 | 1.6714 | 0.5876 | 0.8016 | 3 | | 0.8976 | 0.8813 | 0.9721 | 1.6144 | 0.5845 | 0.7845 | 4 |
2f9460ce2a1e2c535343fde952e81ec4
apache-2.0
['translation']
false
opus-mt-de-guw * source languages: de * target languages: guw * OPUS readme: [de-guw](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-guw/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/de-guw/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-guw/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-guw/opus-2020-01-20.eval.txt)
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