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gokuls/distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mnli | gokuls | distilbert | 19 | 0 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,648 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sa_GLUE_Experiment_logit_kd_data_aug_mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5076
- Accuracy: 0.6560
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.4734 | 1.0 | 31440 | 0.5068 | 0.6496 |
| 0.3743 | 2.0 | 62880 | 0.5281 | 0.6379 |
| 0.3454 | 3.0 | 94320 | 0.5361 | 0.6354 |
| 0.3333 | 4.0 | 125760 | 0.5399 | 0.6350 |
| 0.3265 | 5.0 | 157200 | 0.5409 | 0.6379 |
| 0.3219 | 6.0 | 188640 | 0.5377 | 0.6413 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
| 12c1cf5b58ecc87af41f4c926d22189b |
Benito/DeTr-TableDetection-1000-images | Benito | detr | 9 | 4 | transformers | 0 | object-detection | true | false | false | apache-2.0 | null | ['table_detection_light'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,416 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DeTr-TableDetection-1000-images
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the table_detection_light dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5143
- Mean Iou: 0.0242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 63 | 0.8696 | 0.0215 |
| 0.9227 | 2.0 | 126 | 0.7547 | 0.0245 |
| 0.9227 | 3.0 | 189 | 0.7170 | 0.0211 |
| 0.6775 | 4.0 | 252 | 0.8319 | 0.0222 |
| 0.6801 | 5.0 | 315 | 0.6943 | 0.0212 |
| 0.6801 | 6.0 | 378 | 0.6622 | 0.0252 |
| 0.604 | 7.0 | 441 | 0.6043 | 0.0234 |
| 0.5467 | 8.0 | 504 | 0.7404 | 0.0249 |
| 0.5467 | 9.0 | 567 | 0.6755 | 0.0242 |
| 0.4347 | 10.0 | 630 | 0.5507 | 0.0232 |
| 0.4347 | 11.0 | 693 | 0.6633 | 0.0277 |
| 0.4202 | 12.0 | 756 | 0.5941 | 0.0256 |
| 0.3508 | 13.0 | 819 | 0.5387 | 0.0238 |
| 0.3508 | 14.0 | 882 | 0.5381 | 0.0256 |
| 0.3223 | 15.0 | 945 | 0.5646 | 0.0254 |
| 0.3058 | 16.0 | 1008 | 0.5460 | 0.0213 |
| 0.3058 | 17.0 | 1071 | 0.5589 | 0.0264 |
| 0.2861 | 18.0 | 1134 | 0.5423 | 0.0257 |
| 0.2861 | 19.0 | 1197 | 0.5207 | 0.0248 |
| 0.2705 | 20.0 | 1260 | 0.5143 | 0.0242 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.5.1
- Tokenizers 0.13.2
| c5d0be88ceed6651bcdd2d3574a9c484 |
explosion/fr_udv25_frenchsequoia_trf | explosion | null | 28 | 5 | spacy | 1 | token-classification | false | false | false | lgpl-lr | ['fr'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['spacy', 'token-classification'] | false | true | true | 16,205 | false | UD v2.5 benchmarking pipeline for UD_French-Sequoia
| Feature | Description |
| --- | --- |
| **Name** | `fr_udv25_frenchsequoia_trf` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) |
| **License** | `LGPL-LR` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (916 labels for 6 components)</summary>
| Component | Labels |
| --- | --- |
| **`experimental_char_ner_tokenizer`** | `TOKEN` |
| **`senter`** | `I`, `S` |
| **`tagger`** | `ADJ`, `ADP`, `ADP_DET`, `ADP_PRON`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` |
| **`morphologizer`** | `POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|Poss=Yes`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|Number=Plur\|POS=ADP\|PronType=Art`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|PronType=Int`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=DET\|Poss=Yes`, `POS=AUX\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PRON\|Person=3\|Reflex=Yes`, `Gender=Masc\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PRON\|Person=3`, `Number=Plur\|POS=NOUN`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=3`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes`, `Gender=Masc\|POS=PRON`, `POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=PRON`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=PRON`, `POS=NUM`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON`, `Number=Plur\|POS=PRON\|Person=3`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Number=Plur\|POS=PRON\|Person=2`, `NumType=Card\|POS=PRON`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NOUN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3`, `Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=DET`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|POS=PRON`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `POS=SYM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `POS=DET`, `Gender=Masc\|Number=Plur\|POS=PRON`, `POS=PART`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Reflex=Yes`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|NumType=Card\|POS=NUM` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:cleft`, `advmod`, `amod`, `appos`, `aux:caus`, `aux:pass`, `aux:tense`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `expl:comp`, `expl:pass`, `expl:subj`, `fixed`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:caus`, `nsubj:pass`, `nummod`, `obj`, `obl:agent`, `obl:arg`, `obl:mod`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`experimental_edit_tree_lemmatizer`** | `0`, `3`, `4`, `6`, `8`, `10`, `12`, `14`, `16`, `20`, `22`, `24`, `26`, `30`, `32`, `34`, `36`, `39`, `40`, `42`, `44`, `45`, `48`, `50`, `52`, `54`, `56`, `58`, `61`, `63`, `66`, `70`, `72`, `74`, `77`, `79`, `81`, `82`, `84`, `86`, `88`, `89`, `91`, `95`, `97`, `99`, `102`, `103`, `106`, `110`, `111`, `113`, `114`, `115`, `118`, `119`, `123`, `125`, `126`, `128`, `130`, `132`, `133`, `134`, `136`, `138`, `139`, `140`, `142`, `143`, `144`, `146`, `148`, `150`, `152`, `155`, `157`, `160`, `161`, `163`, `165`, `167`, `171`, `173`, `174`, `176`, `177`, `179`, `181`, `183`, `185`, `187`, `189`, `191`, `192`, `195`, `197`, `198`, `200`, `202`, `203`, `205`, `208`, `210`, `211`, `212`, `214`, `217`, `218`, `221`, `225`, `227`, `229`, `230`, `232`, `234`, `236`, `238`, `240`, `242`, `243`, `245`, `247`, `248`, `251`, `253`, `255`, `257`, `258`, `260`, `261`, `264`, `267`, `268`, `269`, `272`, `273`, `276`, `277`, `278`, `279`, `284`, `287`, `288`, `291`, `293`, `295`, `298`, `299`, `301`, `304`, `306`, `307`, `309`, `310`, `313`, `315`, `318`, `319`, `322`, `324`, `325`, `327`, `329`, `330`, `332`, `333`, `336`, `339`, `341`, `342`, `344`, `346`, `347`, `350`, `351`, `353`, `356`, `358`, `359`, `361`, `363`, `365`, `367`, `369`, `373`, `376`, `378`, `379`, `380`, `382`, `384`, `386`, `389`, `390`, `391`, `394`, `396`, `398`, `399`, `401`, `404`, `406`, `409`, `412`, `414`, `418`, `421`, `423`, `424`, `426`, `428`, `429`, `430`, `434`, `436`, `438`, `440`, `441`, `443`, `446`, `447`, `448`, `451`, `453`, `456`, `457`, `458`, `460`, `462`, `463`, `465`, `468`, `470`, `472`, `474`, `480`, `482`, `483`, `485`, `486`, `490`, `493`, `494`, `497`, `499`, `500`, `501`, `503`, `506`, `509`, `511`, `512`, `514`, `516`, `518`, `522`, `523`, `526`, `530`, `532`, `534`, `537`, `539`, `540`, `541`, `543`, `545`, `546`, `548`, `550`, `551`, `552`, `554`, `556`, `557`, `558`, `561`, `563`, `565`, `567`, `570`, `571`, `573`, `574`, `575`, `576`, `578`, `579`, `581`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `590`, `592`, `595`, `600`, `603`, `604`, `606`, `608`, `611`, `612`, `614`, `615`, `616`, `618`, `619`, `620`, `621`, `622`, `623`, `624`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `632`, `633`, `634`, `635`, `636`, `638`, `640`, `644`, `646`, `647`, `648`, `650`, `652`, `654`, `657`, `659`, `660`, `661`, `662`, `663`, `664`, `665`, `666`, `668`, `672`, `674`, `675`, `677`, `678`, `679`, `680`, `681`, `682`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `691`, `692`, `693`, `694`, `695`, `696`, `697`, `698`, `699`, `700`, `701`, `702`, `704`, `705`, `706`, `707`, `708`, `709`, `710`, `711`, `712`, `713`, `714`, `715`, `716`, `717`, `718`, `719`, `720`, `721`, `722`, `723`, `724`, `725`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `734`, `735`, `736`, `737`, `738`, `739`, `740`, `741`, `743`, `744`, `747`, `748`, `749`, `750`, `751`, `752`, `753`, `754`, `755`, `756`, `758`, `760`, `762`, `763`, `766`, `767`, `768`, `770`, `772`, `773`, `774`, `775`, `776`, `777`, `778`, `779`, `781`, `783`, `784`, `786`, `787`, `789`, `790`, `791`, `794`, `795`, `796`, `797`, `798`, `799`, `800`, `801`, `802`, `803`, `807`, `809`, `812`, `813`, `815`, `817`, `819`, `821`, `825`, `828`, `829`, `832`, `833`, `834`, `837`, `838`, `839`, `841`, `842`, `844`, `846`, `849`, `851`, `853`, `854`, `855`, `858`, `861`, `862`, `866`, `868`, `869`, `871`, `872`, `874`, `876`, `879`, `880`, `882`, `885`, `887`, `891`, `893`, `895`, `898`, `899`, `902`, `903`, `905`, `906`, `908`, `910`, `911`, `912`, `914`, `917`, `920`, `923`, `925`, `927`, `929`, `932`, `933`, `934`, `936`, `938`, `939`, `943`, `944`, `945`, `946`, `947`, `950`, `952`, `954`, `956`, `958`, `959`, `961`, `963`, `965`, `967`, `969`, `971`, `973`, `976`, `978`, `979`, `980`, `981`, `984`, `986`, `987`, `990`, `993`, `994`, `996`, `998`, `999`, `1000`, `1001`, `1002`, `1004`, `1006`, `1007`, `1009`, `1010`, `1012`, `1014`, `1016`, `1018`, `1021`, `1023`, `1026`, `1027`, `1029`, `1031`, `1033`, `1034`, `1036`, `1037`, `1039`, `1041`, `1043`, `1044`, `1045`, `1046`, `1049`, `1051`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1059`, `1061`, `1063`, `1065`, `1067`, `1068`, `1070`, `1072`, `1073`, `1075`, `1077`, `1078`, `1080`, `1081`, `1082`, `1084`, `1085`, `1087`, `1088`, `1089`, `1090`, `1091`, `1092`, `1094`, `1095`, `1097`, `1098`, `1100`, `1103`, `1106`, `1108`, `1110`, `1111`, `1113`, `1116`, `1117`, `1119`, `1121`, `1124`, `1127`, `1129`, `1131`, `1132`, `1133`, `1135`, `1136`, `1138`, `1139`, `1141`, `1142`, `1145`, `1148`, `1153`, `1154`, `1156`, `1157`, `1159`, `1161` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_F` | 99.70 |
| `TOKEN_P` | 99.69 |
| `TOKEN_R` | 99.71 |
| `TOKEN_ACC` | 99.96 |
| `SENTS_F` | 94.42 |
| `SENTS_P` | 94.42 |
| `SENTS_R` | 94.42 |
| `TAG_ACC` | 98.65 |
| `POS_ACC` | 98.56 |
| `MORPH_ACC` | 97.55 |
| `DEP_UAS` | 94.68 |
| `DEP_LAS` | 92.60 |
| `LEMMA_ACC` | 97.41 | | 5e86bb5b5d0919c30332cbe71be6110e |
jonatasgrosman/exp_w2v2t_de_vp-nl_s8 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['de'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'de'] | false | true | true | 467 | false | # exp_w2v2t_de_vp-nl_s8
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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| b5865e5c6b551ac6f68364387e5121c4 |
KGUY1/AnythingPencilAnime | KGUY1 | null | 6 | 0 | null | 1 | null | false | false | false | creativeml-openrail-m | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 962 | false |
<h1>AnythingPencilAnime</h1>
This is a merged model made from anime-pencil-deffusion-v3 and Anything-V3.0-pruned-fp32, the credits go to the authors of the original models.
# Usage
Keep the words `anime pencil concept style` towards the beginning of your prompt to invoke the finetuned style from the anime-pencil-deffusion-v3 model.
### Examples generated by the v3 model
<img src="https://huggingface.co/KGUY1/AnythingPencilAnime/resolve/main/Example-3.png"/>
<img src="https://huggingface.co/KGUY1/AnythingPencilAnime/resolve/main/Example-1.png"/>
# Models Used
https://huggingface.co/Linaqruf/anything-v3.0/blob/main/Anything-V3.0-pruned-fp32.safetensors
https://huggingface.co/yehiaserag/anime-pencil-diffusion/blob/main/anime-pencil-deffusion-v3.safetensors
# Socials
- Use the #AnythingInkPunk so i can see the cool stuff you make!
---
*NOTE: usage of this model implies accpetance of stable diffusion's [CreativeML Open RAIL-M license](LICENSE)* | e1977bc048a109e4070ccec179bfbe6c |
huxxx657/roberta-base-finetuned-scrambled-squad-10 | huxxx657 | roberta | 13 | 5 | transformers | 0 | question-answering | true | false | false | mit | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,157 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-scrambled-squad-10
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e-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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7482 | 1.0 | 5532 | 1.7200 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
| 9d0b1785d5c7f2ef3070ca4d083c8322 |
rishabhjain16/whisper_large_v2_to_myst55h | rishabhjain16 | whisper | 25 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,721 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# openai/whisper-large-v2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3834
- Wer: 11.8889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5582 | 0.12 | 500 | 0.3660 | 14.8170 |
| 0.2285 | 1.02 | 1000 | 0.2919 | 12.6304 |
| 0.2038 | 1.15 | 1500 | 0.2795 | 11.3850 |
| 0.074 | 2.04 | 2000 | 0.3150 | 12.1043 |
| 0.2165 | 2.17 | 2500 | 0.2978 | 12.8510 |
| 0.0399 | 3.07 | 3000 | 0.3467 | 11.7322 |
| 0.045 | 3.19 | 3500 | 0.3501 | 11.7218 |
| 0.0187 | 4.09 | 4000 | 0.3834 | 11.8889 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.9.1.dev0
- Tokenizers 0.13.2
| 07930004d47190642c5f079664690e56 |
csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12 | csukuangfj | null | 33 | 0 | k2 | 0 | null | false | false | false | apache-2.0 | ['en'] | ['librispeech'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['icefall', 'k2', 'transducer', 'librispeech', 'ASR', 'stateless transducer', 'PyTorch', 'RNN-T', 'pruned RNN-T', 'speech recognition'] | false | true | true | 5,023 | false |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/248>.
It is trained on full LibriSpeech dataset using pruned RNN-T loss from [k2](https://github.com/k2-fsa/k2).
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
cd icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
git lfs pull
```
**Caution**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `1603744469d167d848e074f2ea98c587153205fa`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 1603744469d167d848e074f2ea98c587153205fa
```
to download `icefall`.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
. path.sh
./pruned_transducer_stateless/train.py \
--world-size 8 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless/exp \
--full-libri 1 \
--max-duration 300 \
--prune-range 5 \
--lr-factor 5 \
--lm-scale 0.25
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/WKRFY5fYSzaVBHahenpNlA/>
The command for decoding is:
```bash
epoch=42
avg=11
sym=1
# greedy search
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method greedy_search \
--beam-size 4 \
--max-sym-per-frame $sym
# modified beam search
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
# beam search
# (not recommended)
./pruned_transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.62 | 6.37 | --epoch 42, --avg 11, --max-duration 100 |
| greedy search (max sym per frame 2) | 2.62 | 6.37 | --epoch 42, --avg 11, --max-duration 100 |
| greedy search (max sym per frame 3) | 2.62 | 6.37 | --epoch 42, --avg 11, --max-duration 100 |
| modified beam search (beam size 4) | 2.56 | 6.27 | --epoch 42, --avg 11, --max-duration 100 |
| beam search (beam size 4) | 2.57 | 6.27 | --epoch 42, --avg 11, --max-duration 100 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```bash
epoch=42
avg=11
./pruned_transducer_stateless/export.py \
--exp-dir ./pruned_transducer_stateless/exp \
--bpe-model data/lang_bpe_500/bpe.model \
--epoch $epoch \
--avg $avg
```
**HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/pruned_transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `pruned_transducer_stateless/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
| fd8c87ca9022157a1bb3af04f6c6b743 |
jaeyeon/wav2vec2-child-en-tokenizer-4 | jaeyeon | wav2vec2 | 11 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,314 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-child-en-tokenizer-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4709
- Wer: 0.3769
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0334 | 1.72 | 100 | 1.4709 | 0.3769 |
| 0.0332 | 3.45 | 200 | 1.4709 | 0.3769 |
| 0.0343 | 5.17 | 300 | 1.4709 | 0.3769 |
| 0.032 | 6.9 | 400 | 1.4709 | 0.3769 |
| 0.0332 | 8.62 | 500 | 1.4709 | 0.3769 |
| 0.0327 | 10.34 | 600 | 1.4709 | 0.3769 |
| 0.0331 | 12.07 | 700 | 1.4709 | 0.3769 |
| 0.0334 | 13.79 | 800 | 1.4709 | 0.3769 |
| 0.0319 | 15.52 | 900 | 1.4709 | 0.3769 |
| 0.0338 | 17.24 | 1000 | 1.4709 | 0.3769 |
| 0.0321 | 18.97 | 1100 | 1.4709 | 0.3769 |
| 0.0367 | 20.69 | 1200 | 1.4709 | 0.3769 |
| 0.0331 | 22.41 | 1300 | 1.4709 | 0.3769 |
| 0.0332 | 24.14 | 1400 | 1.4709 | 0.3769 |
| 0.0347 | 25.86 | 1500 | 1.4709 | 0.3769 |
| 0.0319 | 27.59 | 1600 | 1.4709 | 0.3769 |
| 0.0302 | 29.31 | 1700 | 1.4709 | 0.3769 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
| 92200fbcee3c6361c841ebde9072f735 |
manoharahuggingface/bert-finetuned-ner | manoharahuggingface | bert | 12 | 7 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,518 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-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: 0.0608
- Precision: 0.9362
- Recall: 0.9507
- F1: 0.9434
- Accuracy: 0.9866
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869 | 1.0 | 1756 | 0.0684 | 0.9180 | 0.9369 | 0.9274 | 0.9823 |
| 0.033 | 2.0 | 3512 | 0.0681 | 0.9264 | 0.9487 | 0.9374 | 0.9854 |
| 0.0178 | 3.0 | 5268 | 0.0608 | 0.9362 | 0.9507 | 0.9434 | 0.9866 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
| 78ace10b2ef219ecc0de800ad8e5736d |
RayMelius/bert-finetuned-ner | RayMelius | bert | 12 | 3 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['conll2003'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 893 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| b28d965f53ae8961b6aba4b2199f2782 |
versae/wav2vec2-large-voxrex-swedish-coscan-no-region | versae | wav2vec2 | 10 | 4 | transformers | 0 | audio-classification | true | false | false | cc0-1.0 | null | ['coscan-speech2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,869 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-voxrex-swedish-coscan-no-region
This model is a fine-tuned version of [KBLab/wav2vec2-large-voxrex-swedish](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) on the coscan-speech2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0151
- Accuracy: 0.8773
- F1: 0.8773
- Precision: 0.8773
- Recall: 0.8773
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.1651 | 1.0 | 6468 | 0.5657 | 0.8650 | 0.8650 | 0.8650 | 0.8650 |
| 0.1217 | 2.0 | 12936 | 0.9411 | 0.8487 | 0.8487 | 0.8487 | 0.8487 |
| 0.0013 | 3.0 | 19404 | 0.9991 | 0.8617 | 0.8617 | 0.8617 | 0.8617 |
| 0.0652 | 4.0 | 25872 | 1.0151 | 0.8773 | 0.8773 | 0.8773 | 0.8773 |
| 0.0001 | 5.0 | 32340 | 1.1031 | 0.8700 | 0.8700 | 0.8700 | 0.8700 |
### Classification report on Coscan Speech (test set)
```
precision recall f1-score support
Bergen og Ytre Vestland 0.65 0.97 0.78 1809
Hedmark og Oppland 0.12 0.06 0.08 2302
Nordland 0.97 0.47 0.63 2195
Oslo-området 0.78 0.42 0.55 6957
Sunnmøre 0.94 0.71 0.81 2636
Sør-Vestlandet 0.96 0.46 0.62 2860
Sørlandet 0.62 0.81 0.70 2490
Troms 0.67 1.00 0.80 2867
Trøndelag 0.52 0.94 0.67 2666
Voss og omland 0.70 0.71 0.71 2641
Ytre Oslofjord 0.20 0.49 0.29 1678
accuracy 0.62 31101
macro avg 0.65 0.64 0.60 31101
weighted avg 0.68 0.62 0.61 31101
```
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 2.4.1.dev0
- Tokenizers 0.12.1 | 9759f8574c4b82d36601e0ed6fb10765 |
ghatgetanuj/roberta-large_cls_subj | ghatgetanuj | roberta | 13 | 1 | transformers | 0 | text-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,507 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large_cls_subj
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: 0.6931
- Accuracy: 0.4835
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-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: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3714 | 1.0 | 500 | 0.2392 | 0.9335 |
| 0.395 | 2.0 | 1000 | 0.7052 | 0.4855 |
| 0.5316 | 3.0 | 1500 | 0.6932 | 0.5055 |
| 0.7051 | 4.0 | 2000 | 0.6926 | 0.5165 |
| 0.6965 | 5.0 | 2500 | 0.6931 | 0.4835 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 9975161cc7685b3c946a414d898682c7 |
sd-concepts-library/david-moreno-architecture | sd-concepts-library | null | 10 | 0 | null | 1 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,103 | false | ### David Moreno Architecture on Stable Diffusion
This is the `<dm-arch>` 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`:
| d49ea4a5ec83b6de0e435dd7119a6f44 |
fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90 | fathyshalab | roberta | 14 | 2 | sentence-transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | true | true | 1,514 | false |
# fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("fathyshalab/domain_transfer_general-massive_takeaway-roberta-large-v1-5-90")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| fde8530b9a4be61ecef474baf02fc723 |
muhtasham/bert-small-finetuned-legal-definitions-longer | muhtasham | bert | 9 | 2 | transformers | 0 | fill-mask | true | false | false | apache-2.0 | null | ['finiteautomata/legal-definitions'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,690 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_uncased_L-4_H-512_A-8-finetuned-legal-definitions-longer
This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the legal-definitions dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3701
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4867 | 1.0 | 801 | 1.4951 |
| 1.429 | 2.0 | 1602 | 1.4872 |
| 1.4055 | 3.0 | 2403 | 1.4147 |
| 1.3703 | 4.0 | 3204 | 1.4231 |
| 1.3414 | 5.0 | 4005 | 1.4094 |
| 1.3254 | 6.0 | 4806 | 1.3913 |
| 1.3064 | 7.0 | 5607 | 1.3827 |
| 1.2967 | 8.0 | 6408 | 1.3905 |
| 1.2961 | 9.0 | 7209 | 1.3719 |
| 1.2824 | 10.0 | 8010 | 1.3701 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| e25b13ddc0d3731f38afc493476a5849 |
gokuls/distilbert_add_GLUE_Experiment_logit_kd_stsb_96 | gokuls | distilbert | 17 | 2 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 3,326 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_add_GLUE_Experiment_logit_kd_stsb_96
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1264
- Pearson: nan
- Spearmanr: nan
- Combined Score: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 4.3296 | 1.0 | 23 | 3.3387 | nan | nan | nan |
| 3.9535 | 2.0 | 46 | 3.1277 | nan | nan | nan |
| 3.7081 | 3.0 | 69 | 2.9189 | nan | nan | nan |
| 3.4597 | 4.0 | 92 | 2.7125 | nan | nan | nan |
| 3.2232 | 5.0 | 115 | 2.5114 | nan | nan | nan |
| 2.972 | 6.0 | 138 | 2.3156 | 0.0070 | 0.0078 | 0.0074 |
| 2.7373 | 7.0 | 161 | 2.1284 | nan | nan | nan |
| 2.527 | 8.0 | 184 | 1.9503 | nan | nan | nan |
| 2.3016 | 9.0 | 207 | 1.7828 | 0.0092 | 0.0081 | 0.0087 |
| 2.0903 | 10.0 | 230 | 1.6295 | nan | nan | nan |
| 1.8919 | 11.0 | 253 | 1.4932 | -0.0357 | -0.0358 | -0.0358 |
| 1.7184 | 12.0 | 276 | 1.3768 | nan | nan | nan |
| 1.5665 | 13.0 | 299 | 1.2813 | 0.0302 | 0.0292 | 0.0297 |
| 1.4283 | 14.0 | 322 | 1.2075 | 0.0115 | 0.0132 | 0.0123 |
| 1.3175 | 15.0 | 345 | 1.1569 | nan | nan | nan |
| 1.2276 | 16.0 | 368 | 1.1298 | nan | nan | nan |
| 1.1643 | 17.0 | 391 | 1.1264 | nan | nan | nan |
| 1.1172 | 18.0 | 414 | 1.1447 | 0.0009 | 0.0027 | 0.0018 |
| 1.1066 | 19.0 | 437 | 1.1677 | nan | nan | nan |
| 1.1002 | 20.0 | 460 | 1.1712 | 0.0024 | 0.0003 | 0.0014 |
| 1.1027 | 21.0 | 483 | 1.1767 | nan | nan | nan |
| 1.0984 | 22.0 | 506 | 1.1799 | nan | nan | nan |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
| d1a23eac9167d2cebb47f365210f6269 |
MBMMurad/wav2vec2-base-cvbn-37k | MBMMurad | wav2vec2 | 13 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['cvbn'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,206 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-cvbn-37k
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2288
- eval_wer: 0.3332
- eval_runtime: 329.8903
- eval_samples_per_second: 9.094
- eval_steps_per_second: 0.57
- epoch: 3.59
- step: 8400
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
| 01966695195a334122d229cfc369f314 |
anas-awadalla/bart-large-few-shot-k-64-finetuned-squad-infilling-seed-2 | anas-awadalla | bart | 16 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 971 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-64-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
| e2a96bfaf2ca67c5e1fe2a5d721ced83 |
tuananh7198/whisper-medium-vi | tuananh7198 | whisper | 47 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['vi'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,327 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium Vietnamese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5422
- Wer: 20.0483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0241 | 4.01 | 1000 | 0.5422 | 20.0483 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
| 3cbc489cb363bf12869f72738cbbe737 |
WillHeld/byt5-base-cstop_artificial | WillHeld | t5 | 15 | 5 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | ['en'] | ['cstop_artificial'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,221 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# byt5-base-cstop_artificial
This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on the cstop_artificial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0461
- Exact Match: 0.7996
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:------:|:----:|:---------------:|:-----------:|
| 0.2563 | 28.5 | 200 | 0.0461 | 0.0376 |
| 0.0065 | 57.13 | 400 | 0.0563 | 0.0376 |
| 0.0021 | 85.63 | 600 | 0.0592 | 0.0358 |
| 0.0013 | 114.25 | 800 | 0.0569 | 0.0376 |
| 0.0008 | 142.75 | 1000 | 0.0675 | 0.0358 |
| 0.0007 | 171.38 | 1200 | 0.0627 | 0.0394 |
| 0.0004 | 199.88 | 1400 | 0.0677 | 0.0358 |
| 0.0003 | 228.5 | 1600 | 0.0650 | 0.0376 |
| 0.0002 | 257.13 | 1800 | 0.0693 | 0.0394 |
| 0.0002 | 285.63 | 2000 | 0.0721 | 0.0394 |
| 0.0002 | 314.25 | 2200 | 0.0714 | 0.0376 |
| 0.0002 | 342.75 | 2400 | 0.0701 | 0.0394 |
| 0.0002 | 371.38 | 2600 | 0.0750 | 0.0394 |
| 0.0001 | 399.88 | 2800 | 0.0739 | 0.0394 |
| 0.0001 | 428.5 | 3000 | 0.0745 | 0.0394 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+cu117
- Datasets 2.7.0
- Tokenizers 0.13.2
| 0219ad6c2359249a0f6057ce537c7861 |
facebook/esm1b_t33_650M_UR50S | facebook | esm | 8 | 346 | transformers | 2 | fill-mask | true | true | false | mit | null | null | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | [] | false | true | true | 4,495 | false | # **ESM-1b**
ESM-1b ([paper](https://www.pnas.org/content/118/15/e2016239118#:~:text=https%3A//doi.org/10.1073/pnas.2016239118), [repository](https://github.com/facebookresearch/esm)) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref50 with an unsupervised masked language modeling (MLM) objective, meaning the model is trained to predict amino acids from the surrounding sequence context. This pretraining objective allows ESM-1b to learn generally useful features which can be transferred to downstream prediction tasks. ESM-1b has been evaluated on a variety of tasks related to protein structure and function, including remote homology detection, secondary structure prediction, contact prediction, and prediction of the effects of mutations on function, producing state-of-the-art results.
**Important note**: ESM-2 is now available in a range of checkpoint sizes. For most tasks, ESM-2 performance will be superior to ESM-1 and ESM-1b, and so we recommend using it instead unless your goal is explicitly to compare against ESM-1b. The ESM-2 checkpoint closest in size to ESM-1b is [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D).
## **Model description**
The ESM-1b model is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and training procedure, using the Uniref50 2018_03 database of protein sequences. Note that the pretraining is on the raw protein sequences only. The training is purely unsupervised -- during training no labels are given related to structure or function.
Training is with the masked language modeling objective. The masking follows the procedure of [Devlin et al. 2019](https://arxiv.org/abs/1810.04805), randomly masking 15% of the amino acids in the input, and includes the pass-through and random token noise. One architecture difference from the RoBERTa model is that ESM-1b uses [pre-activation layer normalization](https://arxiv.org/abs/1603.05027).
The learned representations can be used as features for downstream tasks. For example if you have a dataset of measurements of protein activity you can fit a regression model on the features output by ESM-1b to predict the activity of new sequences. The model can also be fine-tuned.
ESM-1b can infer information about the structure and function of proteins without further supervision, i.e. it is capable of zero-shot transfer to structure and function prediction. [Rao et al. 2020](https://openreview.net/pdf?id=fylclEqgvgd) found that the attention heads of ESM-1b directly represent contacts in the 3d structure of the protein. [Meier et al. 2021](https://openreview.net/pdf?id=uXc42E9ZPFs) found that ESM-1b can be used to score the effect of sequence variations on protein function.
## **Intended uses & limitations**
The model can be used for feature extraction, fine-tuned on downstream tasks, or used directly to make inferences about the structure and function of protein sequences, like any other masked language model. For full examples, please see [our notebook on fine-tuning protein models](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb)
## **Training data**
The ESM-1b model was pretrained on [Uniref50](https://www.uniprot.org/downloads) 2018-03, a dataset consisting of approximately 30 million protein sequences.
## **Training procedure**
### **Preprocessing**
The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The inputs of the model are then of the form:
```
<cls> Protein Sequence A
```
During training, sequences longer than 1023 tokens (without CLS) are randomly cropped to a length of 1023.
The details of the masking procedure for each sequence follow Devlin et al. 2019:
* 15% of the amino acids are masked.
* In 80% of the cases, the masked amino acids are replaced by `<mask>`.
* In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
* In the 10% remaining cases, the masked amino acids are left as is.
### **Pretraining**
The model was trained on 128 NVIDIA v100 GPUs for 500K updates, using sequence length 1024 (131,072 tokens per batch). The optimizer used is Adam (betas=[0.9, 0.999]) with a learning rate of 1e-4, a weight decay of 0, learning rate warmup for 16k steps and inverse square root decay of the learning rate after. | ab3f06e16f4aa050e2c01a0f796e43bc |
ncduy/distilbert-base-cased-distilled-squad-finetuned-squad-small | ncduy | distilbert | 12 | 7 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,031 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-cased-distilled-squad-finetuned-squad-small
This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| 1b8c82be9dd8ef342f8a8dccecbc5d0e |
jonatasgrosman/exp_w2v2t_fa_unispeech_s108 | jonatasgrosman | unispeech | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fa'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fa'] | false | true | true | 469 | false | # exp_w2v2t_fa_unispeech_s108
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (fa)](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.
| 95d30187b3aa1d6d96173990ecc89c86 |
mikr/whisper-small-cs-sk-cv11 | mikr | whisper | 23 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['sk'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,590 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Slovak test on Czech
This model is a fine-tuned version of [mikr/whisper-small-cs-cv11](https://huggingface.co/mikr/whisper-small-cs-cv11) on the mozilla-foundation/common_voice_11_0 sk dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7223
- Wer: 35.4355
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.001 | 21.0 | 1000 | 0.6507 | 37.3275 |
| 0.0003 | 42.01 | 2000 | 0.6954 | 36.1138 |
| 0.0002 | 63.01 | 3000 | 0.7223 | 35.4355 |
| 0.0001 | 85.0 | 4000 | 0.7388 | 35.5902 |
| 0.0001 | 106.0 | 5000 | 0.7465 | 35.6735 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
| b3057409511ea3dd21fd3a831eb78dc1 |
DDSC/roberta-base-danish | DDSC | roberta | 25 | 232 | transformers | 0 | fill-mask | true | false | true | cc-by-4.0 | ['da'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['danish', 'roberta'] | false | true | true | 516 | false |
# RøBÆRTa - Danish Roberta Base
## Description
RøBÆRTa is a danish pretrained Roberta base model. RøBÆRTa was pretrained on the danish mC4 dataset during the flax community week. This project was organized by Dansk Data Science Community (DDSC) 👇 <br><br>
https://www.linkedin.com/groups/9017904/
## Team RøBÆRTa:
- Dan Saattrup Nielsen (saattrupdan)
- Malte Højmark-Bertelsen (Maltehb)
- Morten Kloster Pedersen (MortenKP)
- Kasper Junge (Juunge)
- Per Egil Kummervold (pere)
- Birger Moëll (birgermoell)
---
| 6eb790f15411bf81b7b35d7431bf9c9f |
cpierse/wav2vec2-large-xlsr-53-esperanto | cpierse | wav2vec2 | 9 | 7,240 | transformers | 1 | automatic-speech-recognition | true | false | true | apache-2.0 | ['eo'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | true | true | true | 4,078 | false |
# Wav2Vec2-Large-XLSR-53-eo
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on esperanto using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "eo", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Esperanto test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import jiwer
def chunked_wer(targets, predictions, chunk_size=None):
if chunk_size is None: return jiwer.wer(targets, predictions)
start = 0
end = chunk_size
H, S, D, I = 0, 0, 0, 0
while start < len(targets):
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
H = H + chunk_metrics["hits"]
S = S + chunk_metrics["substitutions"]
D = D + chunk_metrics["deletions"]
I = I + chunk_metrics["insertions"]
start += chunk_size
end += chunk_size
return float(S + D + I) / float(H + S + D)
test_dataset = load_dataset("common_voice", "eo", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model = Wav2Vec2ForCTC.from_pretrained("cpierse/wav2vec2-large-xlsr-53-esperanto")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\„\«\(\»\)\’\']'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * chunked_wer(predictions=result["pred_strings"], targets=result["sentence"],chunk_size=2000)))
```
**Test Result**: 12.31 %
## Training
The Common Voice `train`, `validation` datasets were used for training.
| d6b4e0c36713afdbb9f89d39cd65953b |
cyycyy/xlm-roberta-base-finetuned-panx-de-fr | cyycyy | xlm-roberta | 10 | 13 | transformers | 0 | token-classification | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,315 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1624
- F1: 0.8591
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.289 | 1.0 | 715 | 0.1831 | 0.8193 |
| 0.1471 | 2.0 | 1430 | 0.1527 | 0.8507 |
| 0.0938 | 3.0 | 2145 | 0.1624 | 0.8591 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.1
- Datasets 1.16.1
- Tokenizers 0.10.3
| decb5747f04881e613e202f69b3303ff |
BasStein/doe2vec-d10-m8-ls32-VAE-kl0.001 | BasStein | null | 6 | 0 | keras | 0 | null | false | false | false | apache-2.0 | ['en'] | ['BasStein/250000-randomfunctions-10d'] | {'emissions': 0.0363, 'source': 'code carbon', 'training_type': 'pre-training', 'geographical_location': 'Leiden, The Netherlands', 'hardware_used': '1 Tesla T4'} | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['doe2vec', 'exploratory-landscape-analysis', 'autoencoders'] | false | true | true | 1,232 | false |
## Model description
DoE2Vec model that can transform any design of experiments (function landscape) to a feature vector.
For different input dimensions or sample size you require a different model.
Each model name is build up like doe2vec-d{dimension\}-m{sample size}-ls{latent size}-{AE or VAE}-kl{Kl loss weight}
Example code of loading this huggingface model using the doe2vec package.
First install the package
```zsh
pip install doe2vec
```
Then import and load the model.
```python
from doe2vec import doe_model
obj = doe_model(
10,
8,
latent_dim=32,
kl_weight=0.001,
model_type="VAE"
)
obj.load_from_huggingface()
#test the model
obj.plot_label_clusters_bbob()
```
## Intended uses & limitations
The model is intended to be used to generate feature representations for optimization function landscapes.
The representations can then be used for downstream tasks such as automatic optimization pipelines and meta-learning.
## Training procedure
The model is trained using a weighed KL loss and mean squared error reconstruction loss.
The model is trained using 250.000 randomly generated functions (see the dataset) over 100 epochs.
- **Hardware:** 1x Tesla T4 GPU
- **Optimizer:** Adam
| 26060008e15e5420fc9541cef471c45d |
sd-concepts-library/dog-chip | sd-concepts-library | null | 9 | 0 | null | 1 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,006 | false | ### Dog Chip on Stable Diffusion
This is the `<dog-chip>` 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 an `object`:
| 35026e2362876b13137cf096b4eeacb9 |
AlonCohen/social-groups-ner-first-try | AlonCohen | distilbert | 12 | 4 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 970 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# social-groups-ner-first-try
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| d7a4c134c4b911cfde2adee5a591422a |
artemnech/enrut5-base | artemnech | mt5 | 8 | 1 | transformers | 0 | text2text-generation | true | false | false | mit | ['ru', 'en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['russian'] | false | true | true | 1,683 | false | This is mt5-base model [google/mt5-base](https://huggingface.co/google/mt5-base) in which only Russian and English tokens are left
The model has been fine-tuned for several tasks:
* translation (opus100 dataset)
* dialog (daily dialog dataset)
How to use:
```
# !pip install transformers sentencepiece
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, T5Tokenizer
import torch
model_name = 'artemnech/enrut5-base'
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
def generate(text, **kwargs):
model.eval()
inputs = tokenizer(text, return_tensors='pt').to(model.device)
with torch.no_grad():
hypotheses = model.generate(**inputs, **kwargs)
return tokenizer.decode(hypotheses[0], skip_special_tokens=True)
print(generate('translate ru-en: Я боюсь, что я не завершу доклад в ближайшее время.', num_beams=4,))
# I fear I'm not going to complete the report in the near future.
print(generate("translate en-ru: I'm afraid that I won't finish the report on time.", num_beams=4, max_length = 30))
# Я боюсь, что я не завершу доклад в ближайшее время.
print(generate('dialog: user1>>: Hello', num_beams=2))
# Hi
print(generate('dialog: user1>>: Hello user2>>: Hi user1>>: Would you like to drink something?', num_beams=2))
# I would like to drink a glass of wine.
from collections import deque
context =deque([], maxlen=6)
while True:
text = input()
text = 'user1>>: ' + text
context.append(text)
answ = generate('dialog: ' + ' '.join(context), num_beams=3, do_sample = True, temperature=1.5)
context.append('user2>>: ' + answ)
print('bot: ', answ)
```
| 534f396b3d29d33baf5a54de2ba00aac |
Rongjiehuang/ProDiff | Rongjiehuang | null | 12 | 0 | null | 5 | text-to-speech | false | false | false | other | null | ['LJSpeech'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['text-to-speech', 'neural-vocoder', 'diffusion probabilistic model'] | false | true | true | 1,715 | false |
# ProDiff and FastDiff Model Card
## Key Features
- **Extremely-Fast** diffusion text-to-speech synthesis pipeline for potential **industrial deployment**.
- **Tutorial and code base** for speech diffusion models.
- More **supported diffusion mechanism** (e.g., guided diffusion) will be available.
## Model Details
- **Model type:** Diffusion-based text-to-speech generation model
- **Language(s):** English
- **Model Description:** A conditional diffusion probabilistic model capable of generating high fidelity speech efficiently.
- **Resources for more information:** [FastDiff GitHub Repository](https://github.com/Rongjiehuang/FastDiff), [FastDiff Paper](https://arxiv.org/abs/2204.09934). [ProDiff GitHub Repository](https://github.com/Rongjiehuang/ProDiff), [ProDiff Paper](https://arxiv.org/abs/2207.06389).
- **Cite as:**
@inproceedings{huang2022prodiff,
title={ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech},
author={Huang, Rongjie and Zhao, Zhou and Liu, Huadai and Liu, Jinglin and Cui, Chenye and Ren, Yi},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
year={2022}
@inproceedings{huang2022fastdiff,
title={FastDiff: A Fast Conditional Diffusion Model for High-Quality Speech Synthesis},
author={Huang, Rongjie and Lam, Max WY and Wang, Jun and Su, Dan and Yu, Dong and Ren, Yi and Zhao, Zhou},
booktitle = {Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI-22}},
year={2022}
-
*This model card was written based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* | e8b4ba2c2e8d37499dabd4ac72a6a2d1 |
BenjaminB/plain-sklearn | BenjaminB | null | 5 | 3 | sklearn | 0 | null | false | false | false | bsd-3-clause | null | ['synthetic dataset from sklearn'] | null | 3 | 0 | 0 | 1 | 0 | 0 | 0 | ['sklearn'] | false | true | true | 543 | false |
# Simple example using plain scikit-learn
## Reproducing the model
Inside a Python environment, install the dependencies listed in `requirements.txt` and then run:
``` bash
python train.py
```
The resulting model artifact should be stored in `model.pickle`.
## The model
The used model is a simple logistic regression trained through gradient descent.
## Intended use & limitations
This model is just for demonstration purposes and should thus not be used.
## Dataset
The dataset is entirely synthetic and has no real world origin.
| 54d83559eeb41b72f41c810a3735c337 |
jonatasgrosman/exp_w2v2t_ar_xlsr-53_s34 | jonatasgrosman | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['ar'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'ar'] | false | true | true | 460 | false | # exp_w2v2t_ar_xlsr-53_s34
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| 4da2078816781929f3d1ee0599b74c4b |
tomekkorbak/dazzling_swirles | tomekkorbak | null | 2 | 0 | null | 0 | null | false | false | false | mit | ['en'] | ['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'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 8,841 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dazzling_swirles
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- 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_scheduler_warmup_ratio: 0.01
- training_steps: 25000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# 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': 'dazzling_swirles',
'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}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/1957kpf2 | b5dcef59ac88a44deb1cdc97ee141415 |
laion/CLIP-ViT-g-14-laion2B-s12B-b42K | laion | clip | 12 | 14,744 | open_clip | 16 | null | true | false | false | mit | null | null | null | 1 | 0 | 1 | 0 | 1 | 1 | 0 | [] | false | true | true | 7,212 | false | # Model Card for CLIP ViT-g/14 - LAION-2B
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [Acknowledgements](#acknowledgements)
6. [Citation](#citation)
7. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
A CLIP ViT-g/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip).
Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster.
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset.
## Direct Use
Zero-shot image classification, image and text retrieval, among others.
## Downstream Use
Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/slow-g-14--VmlldzoyNTMwMjg5).
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
**TODO** - more detail
## Results
The model achieves a 76.6 zero-shot top-1 accuracy on ImageNet-1k.
An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
**TODO** - create table for just this model's metrics.
# Acknowledgements
Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model.
# Citation
**BibTeX:**
In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite:
OpenAI CLIP paper
```
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
OpenCLIP software
```
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
# How to Get Started with the Model
Use the code below to get started with the model.
** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets | 1ec30846e4c10f0a6832756752177030 |
tomekkorbak/boring_mcclintock | tomekkorbak | gpt2 | 39 | 2 | transformers | 0 | null | true | false | false | mit | ['en'] | ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tomekkorbak/pii-pile-chunk3-200000-250000', 'tomekkorbak/pii-pile-chunk3-250000-300000', 'tomekkorbak/pii-pile-chunk3-300000-350000', 'tomekkorbak/pii-pile-chunk3-350000-400000', 'tomekkorbak/pii-pile-chunk3-400000-450000', 'tomekkorbak/pii-pile-chunk3-450000-500000', 'tomekkorbak/pii-pile-chunk3-500000-550000', 'tomekkorbak/pii-pile-chunk3-550000-600000', 'tomekkorbak/pii-pile-chunk3-600000-650000', 'tomekkorbak/pii-pile-chunk3-650000-700000', 'tomekkorbak/pii-pile-chunk3-700000-750000', 'tomekkorbak/pii-pile-chunk3-750000-800000', 'tomekkorbak/pii-pile-chunk3-800000-850000', 'tomekkorbak/pii-pile-chunk3-850000-900000', 'tomekkorbak/pii-pile-chunk3-900000-950000', 'tomekkorbak/pii-pile-chunk3-950000-1000000', 'tomekkorbak/pii-pile-chunk3-1000000-1050000', 'tomekkorbak/pii-pile-chunk3-1050000-1100000', 'tomekkorbak/pii-pile-chunk3-1100000-1150000', 'tomekkorbak/pii-pile-chunk3-1150000-1200000', 'tomekkorbak/pii-pile-chunk3-1200000-1250000', 'tomekkorbak/pii-pile-chunk3-1250000-1300000', 'tomekkorbak/pii-pile-chunk3-1300000-1350000', 'tomekkorbak/pii-pile-chunk3-1350000-1400000', 'tomekkorbak/pii-pile-chunk3-1400000-1450000', 'tomekkorbak/pii-pile-chunk3-1450000-1500000', 'tomekkorbak/pii-pile-chunk3-1500000-1550000', 'tomekkorbak/pii-pile-chunk3-1550000-1600000', 'tomekkorbak/pii-pile-chunk3-1600000-1650000', 'tomekkorbak/pii-pile-chunk3-1650000-1700000', 'tomekkorbak/pii-pile-chunk3-1700000-1750000', 'tomekkorbak/pii-pile-chunk3-1750000-1800000', 'tomekkorbak/pii-pile-chunk3-1800000-1850000', 'tomekkorbak/pii-pile-chunk3-1850000-1900000', 'tomekkorbak/pii-pile-chunk3-1900000-1950000'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 8,327 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# boring_mcclintock
This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-250000-300000, the tomekkorbak/pii-pile-chunk3-300000-350000, the tomekkorbak/pii-pile-chunk3-350000-400000, the tomekkorbak/pii-pile-chunk3-400000-450000, the tomekkorbak/pii-pile-chunk3-450000-500000, the tomekkorbak/pii-pile-chunk3-500000-550000, the tomekkorbak/pii-pile-chunk3-550000-600000, the tomekkorbak/pii-pile-chunk3-600000-650000, the tomekkorbak/pii-pile-chunk3-650000-700000, the tomekkorbak/pii-pile-chunk3-700000-750000, the tomekkorbak/pii-pile-chunk3-750000-800000, the tomekkorbak/pii-pile-chunk3-800000-850000, the tomekkorbak/pii-pile-chunk3-850000-900000, the tomekkorbak/pii-pile-chunk3-900000-950000, the tomekkorbak/pii-pile-chunk3-950000-1000000, the tomekkorbak/pii-pile-chunk3-1000000-1050000, the tomekkorbak/pii-pile-chunk3-1050000-1100000, the tomekkorbak/pii-pile-chunk3-1100000-1150000, the tomekkorbak/pii-pile-chunk3-1150000-1200000, the tomekkorbak/pii-pile-chunk3-1200000-1250000, the tomekkorbak/pii-pile-chunk3-1250000-1300000, the tomekkorbak/pii-pile-chunk3-1300000-1350000, the tomekkorbak/pii-pile-chunk3-1350000-1400000, the tomekkorbak/pii-pile-chunk3-1400000-1450000, the tomekkorbak/pii-pile-chunk3-1450000-1500000, the tomekkorbak/pii-pile-chunk3-1500000-1550000, the tomekkorbak/pii-pile-chunk3-1550000-1600000, the tomekkorbak/pii-pile-chunk3-1600000-1650000, the tomekkorbak/pii-pile-chunk3-1650000-1700000, the tomekkorbak/pii-pile-chunk3-1700000-1750000, the tomekkorbak/pii-pile-chunk3-1750000-1800000, the tomekkorbak/pii-pile-chunk3-1800000-1850000, the tomekkorbak/pii-pile-chunk3-1850000-1900000 and the tomekkorbak/pii-pile-chunk3-1900000-1950000 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 8
- 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_scheduler_warmup_ratio: 0.01
- training_steps: 50354
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.24.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.11.6
# Full config
{'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>',
'drop_token_fraction': 0.01,
'misaligned_prefix': '<|misaligned|>',
'threshold': 0.0},
'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000',
'tomekkorbak/pii-pile-chunk3-50000-100000',
'tomekkorbak/pii-pile-chunk3-100000-150000',
'tomekkorbak/pii-pile-chunk3-150000-200000',
'tomekkorbak/pii-pile-chunk3-200000-250000',
'tomekkorbak/pii-pile-chunk3-250000-300000',
'tomekkorbak/pii-pile-chunk3-300000-350000',
'tomekkorbak/pii-pile-chunk3-350000-400000',
'tomekkorbak/pii-pile-chunk3-400000-450000',
'tomekkorbak/pii-pile-chunk3-450000-500000',
'tomekkorbak/pii-pile-chunk3-500000-550000',
'tomekkorbak/pii-pile-chunk3-550000-600000',
'tomekkorbak/pii-pile-chunk3-600000-650000',
'tomekkorbak/pii-pile-chunk3-650000-700000',
'tomekkorbak/pii-pile-chunk3-700000-750000',
'tomekkorbak/pii-pile-chunk3-750000-800000',
'tomekkorbak/pii-pile-chunk3-800000-850000',
'tomekkorbak/pii-pile-chunk3-850000-900000',
'tomekkorbak/pii-pile-chunk3-900000-950000',
'tomekkorbak/pii-pile-chunk3-950000-1000000',
'tomekkorbak/pii-pile-chunk3-1000000-1050000',
'tomekkorbak/pii-pile-chunk3-1050000-1100000',
'tomekkorbak/pii-pile-chunk3-1100000-1150000',
'tomekkorbak/pii-pile-chunk3-1150000-1200000',
'tomekkorbak/pii-pile-chunk3-1200000-1250000',
'tomekkorbak/pii-pile-chunk3-1250000-1300000',
'tomekkorbak/pii-pile-chunk3-1300000-1350000',
'tomekkorbak/pii-pile-chunk3-1350000-1400000',
'tomekkorbak/pii-pile-chunk3-1400000-1450000',
'tomekkorbak/pii-pile-chunk3-1450000-1500000',
'tomekkorbak/pii-pile-chunk3-1500000-1550000',
'tomekkorbak/pii-pile-chunk3-1550000-1600000',
'tomekkorbak/pii-pile-chunk3-1600000-1650000',
'tomekkorbak/pii-pile-chunk3-1650000-1700000',
'tomekkorbak/pii-pile-chunk3-1700000-1750000',
'tomekkorbak/pii-pile-chunk3-1750000-1800000',
'tomekkorbak/pii-pile-chunk3-1800000-1850000',
'tomekkorbak/pii-pile-chunk3-1850000-1900000',
'tomekkorbak/pii-pile-chunk3-1900000-1950000'],
'is_split_by_sentences': True},
'generation': {'force_call_on': [25177],
'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}],
'scenario_configs': [{'generate_kwargs': {'bad_words_ids': [[50257],
[50258]],
'do_sample': True,
'max_length': 128,
'min_length': 10,
'temperature': 0.7,
'top_k': 0,
'top_p': 0.9},
'name': 'unconditional',
'num_samples': 2048,
'prefix': '<|aligned|>'}],
'scorer_config': {}},
'kl_gpt3_callback': {'force_call_on': [25177],
'max_tokens': 64,
'num_samples': 4096,
'prefix': '<|aligned|>'},
'model': {'from_scratch': True,
'gpt2_config_kwargs': {'reorder_and_upcast_attn': True,
'scale_attn_by': True},
'num_additional_tokens': 2,
'path_or_name': 'gpt2'},
'objective': {'name': 'MLE'},
'tokenizer': {'path_or_name': 'gpt2',
'special_tokens': ['<|aligned|>', '<|misaligned|>']},
'training': {'dataloader_num_workers': 0,
'effective_batch_size': 64,
'evaluation_strategy': 'no',
'fp16': True,
'hub_model_id': 'boring_mcclintock',
'hub_strategy': 'all_checkpoints',
'learning_rate': 0.0005,
'logging_first_step': True,
'logging_steps': 1,
'num_tokens': 3300000000,
'output_dir': 'training_output2',
'per_device_train_batch_size': 16,
'push_to_hub': True,
'remove_unused_columns': False,
'save_steps': 25177,
'save_strategy': 'steps',
'seed': 42,
'warmup_ratio': 0.01,
'weight_decay': 0.1}}
# Wandb URL:
https://wandb.ai/tomekkorbak/apo/runs/c17x87uu | 996754d0ffe78db40ebb1b9f35ed7ffa |
hamidov02/wav2vec2-large-xls-r-300m-hungarian-colab | hamidov02 | wav2vec2 | 10 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,733 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6404
- Wer: 0.4662
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4833 | 4.0 | 400 | 0.6493 | 0.6491 |
| 0.2282 | 8.0 | 800 | 0.6395 | 0.5555 |
| 0.1612 | 12.0 | 1200 | 0.6841 | 0.5423 |
| 0.1207 | 16.0 | 1600 | 0.6646 | 0.5224 |
| 0.0929 | 20.0 | 2000 | 0.6355 | 0.4908 |
| 0.0713 | 24.0 | 2400 | 0.6410 | 0.4711 |
| 0.0613 | 28.0 | 2800 | 0.6404 | 0.4662 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
| fdd8974829c5587f1eadb608d2b88cba |
itaihay/wav2vec_asr_swbd | itaihay | wav2vec2 | 361 | 19 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 5,521 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec_asr_swbd
This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3052
- Wer: 0.5302
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0004
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 80
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.5445 | 0.29 | 500 | 0.9114 | 0.6197 |
| 0.9397 | 0.58 | 1000 | 0.5057 | 0.5902 |
| 0.8557 | 0.86 | 1500 | 0.4465 | 0.6264 |
| 0.7716 | 1.15 | 2000 | 0.4182 | 0.5594 |
| 0.7659 | 1.44 | 2500 | 0.4111 | 0.7048 |
| 0.7406 | 1.73 | 3000 | 0.3927 | 0.5944 |
| 0.6857 | 2.02 | 3500 | 0.3852 | 0.7118 |
| 0.7113 | 2.31 | 4000 | 0.3775 | 0.5608 |
| 0.6804 | 2.59 | 4500 | 0.3885 | 0.5759 |
| 0.6654 | 2.88 | 5000 | 0.3703 | 0.7226 |
| 0.6569 | 3.17 | 5500 | 0.3688 | 0.5972 |
| 0.6335 | 3.46 | 6000 | 0.3661 | 0.7278 |
| 0.6309 | 3.75 | 6500 | 0.3579 | 0.6324 |
| 0.6231 | 4.03 | 7000 | 0.3620 | 0.5770 |
| 0.6171 | 4.32 | 7500 | 0.3640 | 0.5772 |
| 0.6191 | 4.61 | 8000 | 0.3553 | 0.6075 |
| 0.6142 | 4.9 | 8500 | 0.3543 | 0.6126 |
| 0.5905 | 5.19 | 9000 | 0.3601 | 0.6319 |
| 0.5846 | 5.48 | 9500 | 0.3429 | 0.7343 |
| 0.5874 | 5.76 | 10000 | 0.3429 | 0.5962 |
| 0.5768 | 6.05 | 10500 | 0.3381 | 0.7410 |
| 0.5783 | 6.34 | 11000 | 0.3391 | 0.5823 |
| 0.5835 | 6.63 | 11500 | 0.3447 | 0.5821 |
| 0.5817 | 6.92 | 12000 | 0.3314 | 0.6890 |
| 0.5459 | 7.2 | 12500 | 0.3363 | 0.5727 |
| 0.5575 | 7.49 | 13000 | 0.3363 | 0.7387 |
| 0.5505 | 7.78 | 13500 | 0.3368 | 0.5685 |
| 0.55 | 8.07 | 14000 | 0.3330 | 0.5587 |
| 0.5523 | 8.36 | 14500 | 0.3338 | 0.5484 |
| 0.5116 | 8.65 | 15000 | 0.3350 | 0.4351 |
| 0.5263 | 8.93 | 15500 | 0.3254 | 0.6235 |
| 0.5265 | 9.22 | 16000 | 0.3297 | 0.6207 |
| 0.5265 | 9.51 | 16500 | 0.3279 | 0.6143 |
| 0.5172 | 9.8 | 17000 | 0.3260 | 0.5800 |
| 0.5028 | 10.09 | 17500 | 0.3259 | 0.5774 |
| 0.5062 | 10.37 | 18000 | 0.3259 | 0.5552 |
| 0.5112 | 10.66 | 18500 | 0.3201 | 0.6625 |
| 0.5149 | 10.95 | 19000 | 0.3184 | 0.6865 |
| 0.4939 | 11.24 | 19500 | 0.3152 | 0.6116 |
| 0.5065 | 11.53 | 20000 | 0.3172 | 0.5246 |
| 0.5129 | 11.82 | 20500 | 0.3129 | 0.5908 |
| 0.4909 | 12.1 | 21000 | 0.3152 | 0.6075 |
| 0.4865 | 12.39 | 21500 | 0.3160 | 0.5037 |
| 0.4805 | 12.68 | 22000 | 0.3139 | 0.5458 |
| 0.4691 | 12.97 | 22500 | 0.3225 | 0.5815 |
| 0.4534 | 13.26 | 23000 | 0.3168 | 0.5614 |
| 0.4661 | 13.54 | 23500 | 0.3135 | 0.6053 |
| 0.4636 | 13.83 | 24000 | 0.3120 | 0.5142 |
| 0.4554 | 14.12 | 24500 | 0.3127 | 0.5552 |
| 0.4602 | 14.41 | 25000 | 0.3117 | 0.5562 |
| 0.4521 | 14.7 | 25500 | 0.3106 | 0.4995 |
| 0.4369 | 14.99 | 26000 | 0.3100 | 0.5663 |
| 0.4249 | 15.27 | 26500 | 0.3110 | 0.5262 |
| 0.4321 | 15.56 | 27000 | 0.3106 | 0.5183 |
| 0.4293 | 15.85 | 27500 | 0.3091 | 0.5311 |
| 0.4537 | 16.14 | 28000 | 0.3134 | 0.4986 |
| 0.4258 | 16.43 | 28500 | 0.3138 | 0.4487 |
| 0.4347 | 16.71 | 29000 | 0.3091 | 0.5011 |
| 0.4615 | 17.0 | 29500 | 0.3068 | 0.5616 |
| 0.4163 | 17.29 | 30000 | 0.3115 | 0.5426 |
| 0.4074 | 17.58 | 30500 | 0.3079 | 0.5341 |
| 0.4121 | 17.87 | 31000 | 0.3047 | 0.5619 |
| 0.4219 | 18.16 | 31500 | 0.3085 | 0.5051 |
| 0.4049 | 18.44 | 32000 | 0.3084 | 0.5116 |
| 0.4119 | 18.73 | 32500 | 0.3071 | 0.5028 |
| 0.4129 | 19.02 | 33000 | 0.3064 | 0.5030 |
| 0.4143 | 19.31 | 33500 | 0.3040 | 0.5086 |
| 0.4013 | 19.6 | 34000 | 0.3057 | 0.5271 |
| 0.4162 | 19.88 | 34500 | 0.3052 | 0.5302 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
| 82324667c6d12be6d9f76d10601610ab |
EmileEsmaili/ddpm-sheetmusic-clean-l1loss-colabVM | EmileEsmaili | null | 38 | 0 | diffusers | 1 | null | false | false | false | apache-2.0 | ['en'] | ['EmileEsmaili/sheet_music_clean'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,256 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-sheetmusic-clean-l1loss-colabVM
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `EmileEsmaili/sheet_music_clean` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: no
### Training results
📈 [TensorBoard logs](https://huggingface.co/EmileEsmaili/ddpm-sheetmusic-clean-l1loss-colabVM/tensorboard?#scalars)
| a5bd9a722c09c9d77b2c2b31962de889 |
sd-dreambooth-library/haaaa | sd-dreambooth-library | null | 18 | 60 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 424 | false | ### haaaa Dreambooth model trained by valentinaw1sa4ajh with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| 2e53c3c4531018bc518eec89edb32b9f |
gokuls/mobilebert_sa_GLUE_Experiment_rte_256 | gokuls | mobilebert | 17 | 5 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,581 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_sa_GLUE_Experiment_rte_256
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6927
- Accuracy: 0.5271
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6937 | 1.0 | 20 | 0.6927 | 0.5271 |
| 0.6936 | 2.0 | 40 | 0.6929 | 0.5307 |
| 0.693 | 3.0 | 60 | 0.6930 | 0.5018 |
| 0.693 | 4.0 | 80 | 0.6934 | 0.4874 |
| 0.6927 | 5.0 | 100 | 0.6947 | 0.4585 |
| 0.6909 | 6.0 | 120 | 0.6942 | 0.5126 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.8.0
- Tokenizers 0.13.2
| b16d59edc9349a290b268947a29e7663 |
dallinmackay/Van-Gogh-diffusion | dallinmackay | null | 20 | 4,544 | diffusers | 179 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 6 | 3 | 3 | 0 | 1 | 1 | 0 | ['stable-diffusion', 'text-to-image'] | false | true | true | 2,968 | false | ### Van Gogh Diffusion
v2 - fixed and working
This is a fine-tuned Stable Diffusion model (based on v1.5) trained on screenshots from the film **_Loving Vincent_**. Use the token **_lvngvncnt_** at the BEGINNING of your prompts to use the style (e.g., "lvngvncnt, beautiful woman at sunset"). This model works best with the Euler sampler (NOT Euler_a).
_Download the ckpt file from "files and versions" tab into the stable diffusion models folder of your web-ui of choice._
If you get too many yellow faces or you dont like the strong blue bias, simply put them in the negative prompt (e.g., "Yellow face, blue").
--
**Characters rendered with this model:**
_prompt and settings used: **lvngvncnt, [person], highly detailed** | **Steps: 25, Sampler: Euler, CFG scale: 6**_
--
**Landscapes/miscellaneous rendered with this model:**
_prompt and settings used: **lvngvncnt, [subject/setting], highly detailed** | **Steps: 25, Sampler: Euler, CFG scale: 6**_
--
This model was trained with Dreambooth, using TheLastBen colab notebook
--
### 🧨 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]().
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "dallinmackay/Van-Gogh-diffusion"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "lvngvncnt, beautiful woman at sunset"
image = pipe(prompt).images[0]
image.save("./sunset.png")
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
--
[](https://www.patreon.com/dallinmackay) | bd9bd0d2c8facd46b464a4be5364404a |
KyleLackinger/kylack | KyleLackinger | null | 28 | 15 | diffusers | 0 | text-to-image | false | false | false | creativeml-openrail-m | null | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['text-to-image', 'stable-diffusion'] | false | true | true | 1,432 | false | ### kyLack Dreambooth model trained by KyleLackinger with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
.png)
| c92fe1f39f1391cd347908f70a437921 |
SetFit/distilbert-base-uncased__sst2__train-8-2 | SetFit | distilbert | 10 | 7 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,888 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.4931
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7081 | 1.0 | 3 | 0.7031 | 0.25 |
| 0.6853 | 2.0 | 6 | 0.7109 | 0.25 |
| 0.6696 | 3.0 | 9 | 0.7211 | 0.25 |
| 0.6174 | 4.0 | 12 | 0.7407 | 0.25 |
| 0.5717 | 5.0 | 15 | 0.7625 | 0.25 |
| 0.5096 | 6.0 | 18 | 0.7732 | 0.25 |
| 0.488 | 7.0 | 21 | 0.7798 | 0.25 |
| 0.4023 | 8.0 | 24 | 0.7981 | 0.25 |
| 0.3556 | 9.0 | 27 | 0.8110 | 0.25 |
| 0.2714 | 10.0 | 30 | 0.8269 | 0.25 |
| 0.2295 | 11.0 | 33 | 0.8276 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
| 54a5c481b1b42c6a7201866cb337ce34 |
dminiotas05/distilbert-base-uncased-finetuned-ft750_reg5 | dminiotas05 | distilbert | 12 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,535 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ft750_reg5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6298
- Mse: 0.6298
- Mae: 0.6087
- R2: 0.4072
- Accuracy: 0.4973
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:|
| 1.8617 | 1.0 | 188 | 0.7482 | 0.7482 | 0.6639 | 0.2957 | 0.4707 |
| 0.5667 | 2.0 | 376 | 0.6017 | 0.6017 | 0.5978 | 0.4336 | 0.5127 |
| 0.5038 | 3.0 | 564 | 0.6298 | 0.6298 | 0.6087 | 0.4072 | 0.4973 |
### Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| fa12c397e7bb190df5087662ca97756e |
Padomin/t5-base-TEDxJP-5front-1body-0rear | Padomin | t5 | 20 | 1 | transformers | 0 | text2text-generation | true | false | false | cc-by-sa-4.0 | null | ['te_dx_jp'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,953 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-TEDxJP-5front-1body-0rear
This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4633
- Wer: 0.1756
- Mer: 0.1693
- Wil: 0.2562
- Wip: 0.7438
- Hits: 55657
- Substitutions: 6415
- Deletions: 2515
- Insertions: 2414
- Cer: 0.1382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
| 0.6441 | 1.0 | 1457 | 0.4872 | 0.2061 | 0.1954 | 0.2850 | 0.7150 | 54813 | 6709 | 3065 | 3540 | 0.1823 |
| 0.543 | 2.0 | 2914 | 0.4422 | 0.1832 | 0.1765 | 0.2641 | 0.7359 | 55188 | 6458 | 2941 | 2432 | 0.1491 |
| 0.4896 | 3.0 | 4371 | 0.4373 | 0.1811 | 0.1739 | 0.2612 | 0.7388 | 55568 | 6464 | 2555 | 2679 | 0.1450 |
| 0.4299 | 4.0 | 5828 | 0.4326 | 0.1745 | 0.1685 | 0.2553 | 0.7447 | 55604 | 6391 | 2592 | 2288 | 0.1367 |
| 0.3853 | 5.0 | 7285 | 0.4390 | 0.1758 | 0.1693 | 0.2561 | 0.7439 | 55696 | 6406 | 2485 | 2462 | 0.1375 |
| 0.357 | 6.0 | 8742 | 0.4433 | 0.1835 | 0.1757 | 0.2619 | 0.7381 | 55609 | 6386 | 2592 | 2871 | 0.1438 |
| 0.3735 | 7.0 | 10199 | 0.4479 | 0.1799 | 0.1729 | 0.2598 | 0.7402 | 55582 | 6425 | 2580 | 2617 | 0.1411 |
| 0.302 | 8.0 | 11656 | 0.4554 | 0.1770 | 0.1702 | 0.2569 | 0.7431 | 55725 | 6408 | 2454 | 2568 | 0.1386 |
| 0.2992 | 9.0 | 13113 | 0.4614 | 0.1784 | 0.1715 | 0.2581 | 0.7419 | 55672 | 6405 | 2510 | 2606 | 0.1404 |
| 0.2972 | 10.0 | 14570 | 0.4633 | 0.1756 | 0.1693 | 0.2562 | 0.7438 | 55657 | 6415 | 2515 | 2414 | 0.1382 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
| e1c084f14d4dd4758c3bd49ca1bcc373 |
Helsinki-NLP/opus-mt-gem-en | Helsinki-NLP | marian | 11 | 11,040 | transformers | 1 | translation | true | true | false | apache-2.0 | ['da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl', 'is', 'en', 'lb', 'yi', 'gem'] | null | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 4,289 | false |
### gem-eng
* source group: Germanic languages
* target group: English
* OPUS readme: [gem-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-eng/README.md)
* model: transformer
* source language(s): afr ang_Latn dan deu enm_Latn fao frr fry gos got_Goth gsw isl ksh ltz nds nld nno nob nob_Hebr non_Latn pdc sco stq swe swg yid
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-deueng.deu.eng | 27.2 | 0.542 |
| news-test2008-deueng.deu.eng | 26.3 | 0.536 |
| newstest2009-deueng.deu.eng | 25.1 | 0.531 |
| newstest2010-deueng.deu.eng | 28.3 | 0.569 |
| newstest2011-deueng.deu.eng | 26.0 | 0.543 |
| newstest2012-deueng.deu.eng | 26.8 | 0.550 |
| newstest2013-deueng.deu.eng | 30.2 | 0.570 |
| newstest2014-deen-deueng.deu.eng | 30.7 | 0.574 |
| newstest2015-ende-deueng.deu.eng | 32.1 | 0.581 |
| newstest2016-ende-deueng.deu.eng | 36.9 | 0.624 |
| newstest2017-ende-deueng.deu.eng | 32.8 | 0.588 |
| newstest2018-ende-deueng.deu.eng | 40.2 | 0.640 |
| newstest2019-deen-deueng.deu.eng | 36.8 | 0.614 |
| Tatoeba-test.afr-eng.afr.eng | 62.8 | 0.758 |
| Tatoeba-test.ang-eng.ang.eng | 10.5 | 0.262 |
| Tatoeba-test.dan-eng.dan.eng | 61.6 | 0.754 |
| Tatoeba-test.deu-eng.deu.eng | 49.7 | 0.665 |
| Tatoeba-test.enm-eng.enm.eng | 23.9 | 0.491 |
| Tatoeba-test.fao-eng.fao.eng | 23.4 | 0.446 |
| Tatoeba-test.frr-eng.frr.eng | 10.2 | 0.184 |
| Tatoeba-test.fry-eng.fry.eng | 29.6 | 0.486 |
| Tatoeba-test.gos-eng.gos.eng | 17.8 | 0.352 |
| Tatoeba-test.got-eng.got.eng | 0.1 | 0.058 |
| Tatoeba-test.gsw-eng.gsw.eng | 15.3 | 0.333 |
| Tatoeba-test.isl-eng.isl.eng | 51.0 | 0.669 |
| Tatoeba-test.ksh-eng.ksh.eng | 6.7 | 0.266 |
| Tatoeba-test.ltz-eng.ltz.eng | 33.0 | 0.505 |
| Tatoeba-test.multi.eng | 54.0 | 0.687 |
| Tatoeba-test.nds-eng.nds.eng | 33.6 | 0.529 |
| Tatoeba-test.nld-eng.nld.eng | 58.9 | 0.733 |
| Tatoeba-test.non-eng.non.eng | 37.3 | 0.546 |
| Tatoeba-test.nor-eng.nor.eng | 54.9 | 0.696 |
| Tatoeba-test.pdc-eng.pdc.eng | 29.6 | 0.446 |
| Tatoeba-test.sco-eng.sco.eng | 40.5 | 0.581 |
| Tatoeba-test.stq-eng.stq.eng | 14.5 | 0.361 |
| Tatoeba-test.swe-eng.swe.eng | 62.0 | 0.745 |
| Tatoeba-test.swg-eng.swg.eng | 17.1 | 0.334 |
| Tatoeba-test.yid-eng.yid.eng | 19.4 | 0.400 |
### System Info:
- hf_name: gem-eng
- source_languages: gem
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl', 'is', 'en', 'lb', 'yi', 'gem']
- src_constituents: {'ksh', 'enm_Latn', 'got_Goth', 'stq', 'dan', 'swe', 'afr', 'pdc', 'gos', 'nno', 'fry', 'gsw', 'fao', 'deu', 'swg', 'sco', 'nob', 'nld', 'isl', 'eng', 'ltz', 'nob_Hebr', 'ang_Latn', 'frr', 'non_Latn', 'yid', 'nds'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.test.txt
- src_alpha3: gem
- tgt_alpha3: eng
- short_pair: gem-en
- chrF2_score: 0.687
- bleu: 54.0
- brevity_penalty: 0.993
- ref_len: 72120.0
- src_name: Germanic languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: gem
- tgt_alpha2: en
- prefer_old: False
- long_pair: gem-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | 0d35a47dff49b16ae9ea79b250b1ae87 |
sd-concepts-library/ori-toor | sd-concepts-library | null | 11 | 0 | null | 15 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,216 | false | ### Ori Toor on Stable Diffusion
This is the `<ori-toor>` 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`:
| dc61cbcdb04d5f90fc4e8ac15c089cfb |
StonyBrookNLP/teabreac-bart-large-numglue | StonyBrookNLP | bart | 9 | 3 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | true | true | 2,631 | false |
# What's this?
This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496).
This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details.
We release the following models:
- **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}`
- **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}`
- **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}`
The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`.
The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`.
The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**.
# How to use it?
Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac
model_name = "StonyBrookNLP/teabreac-bart-large-numglue"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
enable_digit_tokenization(tokenizer)
input_texts = [
"answer_me: Who scored the first touchdown of the game?" +
"context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..."
# Note: some models have slightly different qn/ctxt format. See the github repo.
]
input_ids = tokenizer(
input_texts, return_tensors="pt",
truncation=True, max_length=800,
add_special_tokens=True, padding=True,
)["input_ids"]
generated_ids = model.generate(input_ids, min_length=1, max_length=50)
generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False)
generated_predictions = [
tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions
]
# => ["Chaz Schilens"]
``` | 53624f60db77c0055cce89aebad49053 |
stanfordnlp/stanza-id | stanfordnlp | null | 15 | 232 | stanza | 0 | token-classification | false | false | false | apache-2.0 | ['id'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stanza', 'token-classification'] | false | true | true | 583 | false | # Stanza model for Indonesian (id)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2022-09-25 01:33:23.962
| 08fc4b7f220b29941601cfb3d627d8d4 |
mpoyraz/wav2vec2-xls-r-300m-cv6-turkish | mpoyraz | wav2vec2 | 13 | 34 | transformers | 5 | automatic-speech-recognition | true | false | false | apache-2.0 | ['tr'] | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'robust-speech-event', 'tr'] | true | true | true | 2,256 | false |
# wav2vec2-xls-r-300m-cv6-turkish
## Model description
This ASR model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Turkish language.
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 6.1 TR](https://huggingface.co/datasets/common_voice) All `validated` split except `test` split was used for training.
- [MediaSpeech](https://www.openslr.org/108/)
## Training procedure
To support both of the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
### Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2e-4
- num_train_epochs 10
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05
- per_device_train_batch_size 8
- per_device_eval_batch_size 8
- gradient_accumulation_steps 8
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1
- Datasets 1.18.3
- Tokenizers 0.10.3
## Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
## Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `common_voice` with split `test`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset common_voice --config tr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv6-turkish --dataset speech-recognition-community-v2/dev_data --config tr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Evaluation results:
| Dataset | WER | CER |
|---|---|---|
|Common Voice 6.1 TR test split| 8.83 | 2.37 |
|Speech Recognition Community dev data| 32.81 | 11.22 |
| ebaeb3d6dfffd297d63c9b115312a61f |
sd-concepts-library/cecilio-g | sd-concepts-library | null | 11 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,232 | false | ### Cecilio G on Stable Diffusion
This is the `<cecilio-g>` 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 an `object`:
| 352f48a479f3bdd0ba0e5376540e0905 |
brianpaiva/global_ep5_bertsqv1pptbased_ctxpar_punct_sqv2 | brianpaiva | bert | 12 | 13 | transformers | 0 | question-answering | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,546 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# global_ep5_bertsqv1pptbased_ctxpar_punct_sqv2
This model is a fine-tuned version of [pierreguillou/bert-base-cased-squad-v1.1-portuguese](https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 30
- eval_batch_size: 180
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 90
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 36 | 1.4093 |
| No log | 1.99 | 72 | 1.1903 |
| No log | 2.99 | 108 | 1.0553 |
| No log | 3.99 | 144 | 0.7175 |
| No log | 4.99 | 180 | 0.5786 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
| 5718baddbae5f6643dae1cec301c5642 |
Helsinki-NLP/opus-mt-en-rnd | Helsinki-NLP | marian | 10 | 13 | transformers | 0 | translation | true | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 776 | false |
### opus-mt-en-rnd
* source languages: en
* target languages: rnd
* OPUS readme: [en-rnd](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-rnd/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/en-rnd/opus-2020-01-08.zip)
* test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rnd/opus-2020-01-08.test.txt)
* test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-rnd/opus-2020-01-08.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.en.rnd | 34.5 | 0.571 |
| 5095a4d21dc53b00655b423d0f3dac82 |
syaimu/7th_furry | syaimu | null | 5 | 0 | null | 20 | null | false | false | false | other | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 417 | false | ## / 7th Furry /
<img src="https://i.imgur.com/3vnM7yh.png" width="1700" height="">
# (Important Notice:1.6)
It is quite peaky in use, so the prompts need to be adjusted firmly.
default CFG Scale : 8 ±5
default Sampler : DPM++ SDE Karras
default Steps : 30
The following prompts are used for comparison images.
https://majinai.art/i/AmrKBRI
<img src="https://i.imgur.com/SMmZVuQ.jpg" width="1700" height=""> | b8d56ebff4c8908a8825fe23a4696d32 |
sergiocannata/cvt-21-finetuned-brs2 | sergiocannata | cvt | 9 | 6 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['imagefolder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 9,148 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cvt-21-finetuned-brs2
This model is a fine-tuned version of [microsoft/cvt-21](https://huggingface.co/microsoft/cvt-21) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6947
- Accuracy: 0.6604
- F1: 0.6087
- Precision (ppv): 0.5385
- Recall (sensitivity): 0.7
- Specificity: 0.6364
- Npv: 0.7778
- Auc: 0.6682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
| 0.8177 | 1.89 | 100 | 0.7113 | 0.5283 | 0.5098 | 0.4194 | 0.65 | 0.4545 | 0.6818 | 0.5523 |
| 0.736 | 3.77 | 200 | 0.7178 | 0.5283 | 0.3902 | 0.3810 | 0.4 | 0.6061 | 0.625 | 0.5030 |
| 0.5978 | 5.66 | 300 | 0.6889 | 0.6038 | 0.5532 | 0.4815 | 0.65 | 0.5758 | 0.7308 | 0.6129 |
| 0.5576 | 7.55 | 400 | 0.7349 | 0.4717 | 0.5484 | 0.4048 | 0.85 | 0.2424 | 0.7273 | 0.5462 |
| 0.5219 | 9.43 | 500 | 0.6522 | 0.6038 | 0.4 | 0.4667 | 0.35 | 0.7576 | 0.6579 | 0.5538 |
| 0.5326 | 11.32 | 600 | 0.6665 | 0.6226 | 0.5238 | 0.5 | 0.55 | 0.6667 | 0.7097 | 0.6083 |
| 0.4381 | 13.21 | 700 | 0.7685 | 0.4717 | 0.5333 | 0.4 | 0.8 | 0.2727 | 0.6923 | 0.5364 |
| 0.5598 | 15.09 | 800 | 0.7212 | 0.5283 | 0.1935 | 0.2727 | 0.15 | 0.7576 | 0.5952 | 0.4538 |
| 0.6887 | 16.98 | 900 | 0.6985 | 0.6604 | 0.64 | 0.5333 | 0.8 | 0.5758 | 0.8261 | 0.6879 |
| 0.7594 | 18.87 | 1000 | 0.7040 | 0.5472 | 0.4286 | 0.4091 | 0.45 | 0.6061 | 0.6452 | 0.5280 |
| 0.2177 | 20.75 | 1100 | 0.8056 | 0.4528 | 0.5397 | 0.3953 | 0.85 | 0.2121 | 0.7 | 0.5311 |
| 0.4893 | 22.64 | 1200 | 0.8821 | 0.3396 | 0.3860 | 0.2973 | 0.55 | 0.2121 | 0.4375 | 0.3811 |
| 0.5994 | 24.53 | 1300 | 0.8059 | 0.5660 | 0.5660 | 0.4545 | 0.75 | 0.4545 | 0.75 | 0.6023 |
| 0.5179 | 26.42 | 1400 | 0.6750 | 0.6038 | 0.4615 | 0.4737 | 0.45 | 0.6970 | 0.6765 | 0.5735 |
| 0.198 | 28.3 | 1500 | 0.7448 | 0.3962 | 0.3333 | 0.2857 | 0.4 | 0.3939 | 0.52 | 0.3970 |
| 0.6536 | 30.19 | 1600 | 0.7555 | 0.5094 | 0.4583 | 0.3929 | 0.55 | 0.4848 | 0.64 | 0.5174 |
| 0.7558 | 32.08 | 1700 | 0.6664 | 0.5849 | 0.4762 | 0.4545 | 0.5 | 0.6364 | 0.6774 | 0.5682 |
| 0.4915 | 33.96 | 1800 | 0.9213 | 0.3962 | 0.5152 | 0.3696 | 0.85 | 0.1212 | 0.5714 | 0.4856 |
| 0.3661 | 35.85 | 1900 | 0.9202 | 0.4528 | 0.4912 | 0.3784 | 0.7 | 0.3030 | 0.625 | 0.5015 |
| 0.4838 | 37.74 | 2000 | 0.9297 | 0.4528 | 0.5085 | 0.3846 | 0.75 | 0.2727 | 0.6429 | 0.5114 |
| 0.8461 | 39.62 | 2100 | 0.9464 | 0.4717 | 0.5758 | 0.4130 | 0.95 | 0.1818 | 0.8571 | 0.5659 |
| 0.6937 | 41.51 | 2200 | 0.7129 | 0.5094 | 0.48 | 0.4 | 0.6 | 0.4545 | 0.6522 | 0.5273 |
| 0.6302 | 43.4 | 2300 | 0.6866 | 0.5849 | 0.6071 | 0.4722 | 0.85 | 0.4242 | 0.8235 | 0.6371 |
| 0.0793 | 45.28 | 2400 | 0.7791 | 0.5094 | 0.5517 | 0.4211 | 0.8 | 0.3333 | 0.7333 | 0.5667 |
| 0.464 | 47.17 | 2500 | 0.8116 | 0.4340 | 0.4444 | 0.3529 | 0.6 | 0.3333 | 0.5789 | 0.4667 |
| 0.6131 | 49.06 | 2600 | 0.5970 | 0.6226 | 0.5455 | 0.5 | 0.6 | 0.6364 | 0.7241 | 0.6182 |
| 0.6937 | 50.94 | 2700 | 0.8201 | 0.4340 | 0.4 | 0.3333 | 0.5 | 0.3939 | 0.5652 | 0.4470 |
| 0.6552 | 52.83 | 2800 | 0.7168 | 0.5660 | 0.5306 | 0.4483 | 0.65 | 0.5152 | 0.7083 | 0.5826 |
| 0.7749 | 54.72 | 2900 | 0.6875 | 0.5849 | 0.5217 | 0.4615 | 0.6 | 0.5758 | 0.7037 | 0.5879 |
| 0.9482 | 56.6 | 3000 | 0.6392 | 0.6226 | 0.6296 | 0.5 | 0.85 | 0.4848 | 0.8421 | 0.6674 |
| 0.2467 | 58.49 | 3100 | 0.6281 | 0.6038 | 0.5333 | 0.48 | 0.6 | 0.6061 | 0.7143 | 0.6030 |
| 0.2903 | 60.38 | 3200 | 0.7383 | 0.5472 | 0.5556 | 0.4412 | 0.75 | 0.4242 | 0.7368 | 0.5871 |
| 0.5859 | 62.26 | 3300 | 0.7191 | 0.6226 | 0.5652 | 0.5 | 0.65 | 0.6061 | 0.7407 | 0.6280 |
| 0.3815 | 64.15 | 3400 | 0.7469 | 0.5283 | 0.4444 | 0.4 | 0.5 | 0.5455 | 0.6429 | 0.5227 |
| 0.531 | 66.04 | 3500 | 0.7566 | 0.6226 | 0.5652 | 0.5 | 0.65 | 0.6061 | 0.7407 | 0.6280 |
| 0.3892 | 67.92 | 3600 | 0.8168 | 0.5660 | 0.5490 | 0.4516 | 0.7 | 0.4848 | 0.7273 | 0.5924 |
| 0.6487 | 69.81 | 3700 | 0.9077 | 0.4340 | 0.4643 | 0.3611 | 0.65 | 0.3030 | 0.5882 | 0.4765 |
| 0.5525 | 71.7 | 3800 | 0.6961 | 0.6038 | 0.5116 | 0.4783 | 0.55 | 0.6364 | 0.7 | 0.5932 |
| 0.3137 | 73.58 | 3900 | 1.0817 | 0.3774 | 0.4590 | 0.3415 | 0.7 | 0.1818 | 0.5 | 0.4409 |
| 0.3526 | 75.47 | 4000 | 0.7684 | 0.5472 | 0.5862 | 0.4474 | 0.85 | 0.3636 | 0.8 | 0.6068 |
| 0.5938 | 77.36 | 4100 | 0.8786 | 0.4340 | 0.4828 | 0.3684 | 0.7 | 0.2727 | 0.6 | 0.4864 |
| 0.2431 | 79.25 | 4200 | 0.8925 | 0.4151 | 0.4746 | 0.3590 | 0.7 | 0.2424 | 0.5714 | 0.4712 |
| 0.1021 | 81.13 | 4300 | 1.0740 | 0.4528 | 0.4727 | 0.3714 | 0.65 | 0.3333 | 0.6111 | 0.4917 |
| 0.3429 | 83.02 | 4400 | 0.7723 | 0.4906 | 0.5091 | 0.4 | 0.7 | 0.3636 | 0.6667 | 0.5318 |
| 0.3836 | 84.91 | 4500 | 0.7247 | 0.5472 | 0.5556 | 0.4412 | 0.75 | 0.4242 | 0.7368 | 0.5871 |
| 0.4099 | 86.79 | 4600 | 0.8508 | 0.4340 | 0.4828 | 0.3684 | 0.7 | 0.2727 | 0.6 | 0.4864 |
| 0.8264 | 88.68 | 4700 | 0.7682 | 0.5849 | 0.5769 | 0.4688 | 0.75 | 0.4848 | 0.7619 | 0.6174 |
| 0.1928 | 90.57 | 4800 | 0.8738 | 0.4906 | 0.5574 | 0.4146 | 0.85 | 0.2727 | 0.75 | 0.5614 |
| 0.3422 | 92.45 | 4900 | 0.8810 | 0.5660 | 0.5965 | 0.4595 | 0.85 | 0.3939 | 0.8125 | 0.6220 |
| 0.5524 | 94.34 | 5000 | 1.0801 | 0.3774 | 0.4923 | 0.3556 | 0.8 | 0.1212 | 0.5 | 0.4606 |
| 0.464 | 96.23 | 5100 | 0.9417 | 0.5283 | 0.5902 | 0.4390 | 0.9 | 0.3030 | 0.8333 | 0.6015 |
| 0.7182 | 98.11 | 5200 | 1.0335 | 0.4151 | 0.4746 | 0.3590 | 0.7 | 0.2424 | 0.5714 | 0.4712 |
| 0.604 | 100.0 | 5300 | 0.6947 | 0.6604 | 0.6087 | 0.5385 | 0.7 | 0.6364 | 0.7778 | 0.6682 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
| fa30a5424e14ea285e88d6bb96ea7fb6 |
mprzibilla/super_large_finetune_M01 | mprzibilla | wav2vec2 | 10 | 7 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,803 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# super_large_finetune_M01
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9906
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 20
- 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: 35440
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:---:|
| 10.0626 | 20.0 | 70880 | 3.0307 | 1.0 |
| 2.5319 | 40.0 | 141760 | 3.0316 | 1.0 |
| 2.4978 | 60.0 | 212640 | 3.0123 | 1.0 |
| 2.4849 | 80.0 | 283520 | 2.9923 | 1.0 |
| 2.4776 | 100.0 | 354400 | 3.0092 | 1.0 |
| 2.4733 | 120.0 | 425280 | 2.9964 | 1.0 |
| 2.4702 | 140.0 | 496160 | 2.9968 | 1.0 |
| 2.4686 | 160.0 | 567040 | 2.9937 | 1.0 |
| 2.4669 | 180.0 | 637920 | 2.9908 | 1.0 |
| 2.4661 | 200.0 | 708800 | 2.9906 | 1.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
| 802197e2f63b3fdb7676502ad3efff33 |
anas-awadalla/bart-large-few-shot-k-256-finetuned-squad-infilling-seed-2 | anas-awadalla | bart | 16 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 968 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-few-shot-k-256-finetuned-squad-infilling-seed-2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 35.0
### Training results
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.11.6
| c75780ec26bf4cc0fd7f0d207ae86b99 |
akira0402/xlm-roberta-base-finetuned-panx-de | akira0402 | xlm-roberta | 9 | 5 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,319 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1380
- F1: 0.8630
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2625 | 1.0 | 525 | 0.1667 | 0.8208 |
| 0.1281 | 2.0 | 1050 | 0.1361 | 0.8510 |
| 0.0809 | 3.0 | 1575 | 0.1380 | 0.8630 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| 183d65f4b8a77ae8fe14864c5d21945b |
AlekseyKorshuk/6.7b-ri-reproduce-4-gpu | AlekseyKorshuk | opt | 13 | 4 | transformers | 0 | text-generation | true | false | false | other | null | ['ChaiML/dalio_training_v1'] | null | 10 | 10 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,055 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 6.7b-ri-reproduce-4-gpu
This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the ChaiML/dalio_training_v1 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 10.0
### Training results
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| ff349ed939c52f6f74ade54daa0cc16d |
Lvxue/distilled-mt5-small-1-0.5 | Lvxue | mt5 | 14 | 1 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | ['en', 'ro'] | ['wmt16'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,036 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilled-mt5-small-1-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8410
- Bleu: 5.3917
- Gen Len: 40.6103
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
| 6c320831d9c9de46ea35825eb67f78db |
facebook/s2t-small-mustc-en-it-st | facebook | speech_to_text | 11 | 282 | transformers | 1 | automatic-speech-recognition | true | true | false | mit | ['en', 'it'] | ['mustc'] | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | ['audio', 'speech-translation', 'automatic-speech-recognition'] | false | true | true | 4,212 | false |
# S2T-SMALL-MUSTC-EN-IT-ST
`s2t-small-mustc-en-it-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
## Model description
S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
transcripts/translations autoregressively.
## Intended uses & limitations
This model can be used for end-to-end English speech to Italian text translation.
See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
### How to use
As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
transcripts by passing the speech features to the model.
*Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
filter bank features. Make sure to install the `torchaudio` package before running this example.*
You could either install those as extra speech dependancies with
`pip install transformers"[speech, sentencepiece]"` or install the packages seperatly
with `pip install torchaudio sentencepiece`.
```python
import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-it-st")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-it-st")
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset(
"patrickvonplaten/librispeech_asr_dummy",
"clean",
split="validation"
)
ds = ds.map(map_to_array)
inputs = processor(
ds["speech"][0],
sampling_rate=16_000,
return_tensors="pt"
)
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
```
## Training data
The s2t-small-mustc-en-it-st is trained on English-Italian subset of [MuST-C](https://ict.fbk.eu/must-c/).
MuST-C is a multilingual speech translation corpus whose size and quality facilitates the training of end-to-end systems
for speech translation from English into several languages. For each target language, MuST-C comprises several hundred
hours of audio recordings from English TED Talks, which are automatically aligned at the sentence level with their manual
transcriptions and translations.
## Training procedure
### Preprocessing
The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
is applied to each example.
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 8,000.
### Training
The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate
model training and for better performance the encoder is pre-trained for English ASR.
## Evaluation results
MuST-C test results for en-it (BLEU score): 22.7
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2020fairseqs2t,
title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
year = {2020},
}
```
| edda4585582ce468dc094593bc5fc544 |
brennan-richards/gpt2-finetuned-academic-topics | brennan-richards | gpt2 | 5 | 7 | transformers | 0 | text-generation | true | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,773 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-academic-topics
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on a dataset of sequences of science, technology, engineering and mathematics academic topics/tags which a user has used on their CiteULike or Google Scholar profiles.
Please contact brichards88@uri.edu for questions or inquiries.
It achieves the following results on the evaluation set:
- Train Loss: 3.3216
- Validation Loss: 3.2215
- Epoch: 4
## Model description
Give a sequence of topics, i.e.: "machine learning, deep learning, chemistry, evolution" the model will continue the sequence, effectively recommending/generating new topics that might be of interest.
## Intended uses & limitations
The model is not guaranteed to generate a real topic or even a real word/words as output.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 4.7873 | 4.2950 | 0 |
| 4.1032 | 3.8203 | 1 |
| 3.7363 | 3.5614 | 2 |
| 3.4999 | 3.3740 | 3 |
| 3.3216 | 3.2215 | 4 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 684cc9e665adf9d85638a7c04379cef2 |
plantdoctor/swin-tiny-patch4-window7-224-plant-doctor | plantdoctor | swin | 24 | 12 | transformers | 0 | image-classification | true | false | false | apache-2.0 | null | ['image_folder'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,490 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-plant-doctor
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 image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0043
- Accuracy: 0.9983
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0643 | 1.0 | 3954 | 0.0218 | 0.9933 |
| 0.0536 | 2.0 | 7908 | 0.0103 | 0.9966 |
| 0.018 | 3.0 | 11862 | 0.0043 | 0.9983 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu115
- Datasets 2.1.0
- Tokenizers 0.12.1
| 860734457ca4ce4cac1346a070f5a15e |
brad1141/bertBasev2 | brad1141 | bert | 10 | 1 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,934 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertBasev2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0328
- Precision: 0.9539
- Recall: 0.9707
- F1: 0.9622
- Accuracy: 0.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2004 | 1.0 | 1012 | 0.9504 | 0.2620 | 0.3519 | 0.3004 | 0.6856 |
| 1.0265 | 2.0 | 2024 | 0.6205 | 0.4356 | 0.5161 | 0.4725 | 0.7956 |
| 0.6895 | 3.0 | 3036 | 0.3269 | 0.6694 | 0.7302 | 0.6985 | 0.9044 |
| 0.44 | 4.0 | 4048 | 0.1325 | 0.8356 | 0.9091 | 0.8708 | 0.9667 |
| 0.2585 | 5.0 | 5060 | 0.0717 | 0.9259 | 0.9531 | 0.9393 | 0.9844 |
| 0.1722 | 6.0 | 6072 | 0.0382 | 0.9480 | 0.9619 | 0.9549 | 0.99 |
| 0.0919 | 7.0 | 7084 | 0.0328 | 0.9539 | 0.9707 | 0.9622 | 0.9911 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1
| 687ad5c4b200fcfa524e56fd08ffda0e |
postbot/gpt2-medium-emailgen | postbot | gpt2 | 12 | 43 | transformers | 0 | text-generation | true | false | false | ['apache-2.0'] | null | ['aeslc', 'postbot/multi-emails-100k'] | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | ['text generation', 'emailgen', 'email generation', 'email'] | false | true | true | 2,138 | false |
# gpt2-medium-emailgen
[](https://colab.research.google.com/gist/pszemraj/70058788c6d4b430398c12ee8ba10602/minimal-demo-for-postbot-gpt2-medium-emailgen.ipynb
)
Why write the entire email when you can generate (most of) it?
```python
from transformers import pipeline
model_tag = "postbot/gpt2-medium-emailgen"
generator = pipeline(
'text-generation',
model=model_tag,
)
prompt = """
Hello,
Following up on the bubblegum shipment."""
result = generator(
prompt,
max_length=64,
do_sample=False,
early_stopping=True,
) # generate
print(result[0]['generated_text'])
```
## about
This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the postbot/multi-emails-100k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5840
## Model description
More information needed
## Intended uses & limitations
- this is intended as a tool to save time writing predictable emails and not to write emails without a human-in-the-loop. validate that your email is factually correct before sending it to others.
## Training and evaluation data
- the dataset is essentially a hand-curated/augmented expansion to the classic `aeslc` dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8701 | 1.0 | 789 | 1.8378 |
| 1.5065 | 2.0 | 1578 | 1.6176 |
| 1.1873 | 3.0 | 2367 | 1.5840 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.10.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
| 10afface88c7f0c232fb57d0826d3add |
gokuls/mobilebert_add_GLUE_Experiment_logit_kd_pretrain_qqp | gokuls | mobilebert | 17 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,856 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilebert_add_GLUE_Experiment_logit_kd_pretrain_qqp
This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE QQP dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.6318
- F1: 0.0
- Combined Score: 0.3159
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---:|:--------------:|
| 0.0 | 1.0 | 2843 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 2.0 | 5686 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 3.0 | 8529 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 4.0 | 11372 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 5.0 | 14215 | nan | 0.6318 | 0.0 | 0.3159 |
| 0.0 | 6.0 | 17058 | nan | 0.6318 | 0.0 | 0.3159 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
| 77dc479086bd2292b22d7819844ecb59 |
Helsinki-NLP/opus-mt-uk-no | Helsinki-NLP | marian | 11 | 7 | transformers | 0 | translation | true | true | false | apache-2.0 | ['uk', False] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['translation'] | false | true | true | 1,996 | false |
### ukr-nor
* source group: Ukrainian
* target group: Norwegian
* OPUS readme: [ukr-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nor/README.md)
* model: transformer-align
* source language(s): ukr
* target language(s): nob
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ukr.nor | 21.3 | 0.397 |
### System Info:
- hf_name: ukr-nor
- source_languages: ukr
- target_languages: nor
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-nor/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['uk', 'no']
- src_constituents: {'ukr'}
- tgt_constituents: {'nob', 'nno'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-nor/opus-2020-06-17.test.txt
- src_alpha3: ukr
- tgt_alpha3: nor
- short_pair: uk-no
- chrF2_score: 0.397
- bleu: 21.3
- brevity_penalty: 0.966
- ref_len: 4378.0
- src_name: Ukrainian
- tgt_name: Norwegian
- train_date: 2020-06-17
- src_alpha2: uk
- tgt_alpha2: no
- prefer_old: False
- long_pair: ukr-nor
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | 88e92fb5479a7b7ed21f3a8dd9d34716 |
jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295 | jonatasgrosman | wav2vec2 | 10 | 3 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['fr'] | ['mozilla-foundation/common_voice_7_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['automatic-speech-recognition', 'fr'] | false | true | true | 476 | false | # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (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 been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
| b7c9300cf4375ecc51ed7ba7c900eeec |
jkhan447/HateXplain-2nd-anno-labeled | jkhan447 | bert | 15 | 7 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,019 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HateXplain-2nd-anno-labeled
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: 2.8699
- Accuracy: 0.5778
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
| 7c78b043162fb95ab6300d602edf7947 |
tnkmr/sfi_convtasnet_fd_mgf_musdb18hq | tnkmr | null | 4,430 | 0 | null | 0 | audio-to-audio | false | false | false | mit | ['ja'] | ['MUSDB18-HQ'] | null | 4 | 0 | 4 | 0 | 0 | 0 | 0 | ['music', 'audio', 'audio-to-audio', 'SFI'] | false | true | true | 1,254 | false |
# Sampling-frequency-independent (SFI) Conv-TasNet trained with the MUSDB18-HQ dataset for music source separation.
This model was proposed in [our IEEE/ACM Trans. ASLP paper](https://doi.org/10.1109/TASLP.2022.3203907) and works well with untrained sampling frequencies by using sampling-frequency-independent convolutional layers with the frequency domain filter design.
The latent analog filter is a modulated Gaussian filter.
It was trained by Tomohiko Nakamura using [the codebase](https://github.com/TomohikoNakamura/sfi_convtasnet)).
This model was trained with 32 kHz-sampled data but works well with untrained sampling frequencies (e.g., 8, 16 kHz).
# License
MIT
# Citation
Please cite the following paper.
```
@article{KSaito2022IEEEACMTASLP,
author={Saito, Koichi and Nakamura, Tomohiko and Yatabe, Kohei and Saruwatari, Hiroshi},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title = {Sampling-frequency-independent convolutional layer and its application to audio source separation},
year=2022,
month=sep,
volume=30,
pages={2928--2943},
doi={10.1109/TASLP.2022.3203907},
}
```
# Contents
- Four trained models (seed=40,42,44,47)
- Evaluation results (json files obtained with the museval library)
| 43e2dbc0e294151fdd0aa32a43f3c1b6 |
yhavinga/t5-v1.1-large-dutch-cnn-test | yhavinga | t5 | 13 | 138 | transformers | 1 | summarization | true | false | true | apache-2.0 | ['nl'] | ['yhavinga/mc4_nl_cleaned', 'ml6team/cnn_dailymail_nl'] | null | 3 | 0 | 2 | 1 | 0 | 0 | 0 | ['summarization', 't5', 'seq2seq'] | true | true | true | 5,857 | false |
# T5 v1.1 Large finetuned for CNN news summarization in Dutch 🇳🇱
This model is [t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) finetuned on [CNN Dailymail NL](https://huggingface.co/datasets/ml6team/cnn_dailymail_nl)
For a demo of the Dutch CNN summarization models, head over to the Hugging Face Spaces for
the **[Netherformer 📰](https://huggingface.co/spaces/flax-community/netherformer)** example application!
Rouge scores for this model are listed below.
## Tokenizer
* SentencePiece tokenizer trained from scratch for Dutch on mC4 nl cleaned with scripts from the Huggingface
Transformers [Flax examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling).
## Dataset
All models listed below are trained on of the `full` configuration (39B tokens) of
[cleaned Dutch mC4](https://huggingface.co/datasets/yhavinga/mc4_nl_cleaned),
which is the original mC4, except
* Documents that contained words from a selection of the Dutch and English [List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) are removed
* Sentences with less than 3 words are removed
* Sentences with a word of more than 1000 characters are removed
* Documents with less than 5 sentences are removed
* Documents with "javascript", "lorum ipsum", "terms of use", "privacy policy", "cookie policy", "uses cookies",
"use of cookies", "use cookies", "elementen ontbreken", "deze printversie" are removed.
## Models
TL;DR: [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) is the best model.
* `yhavinga/t5-base-dutch` is a re-training of the Dutch T5 base v1.0 model trained during the summer 2021
Flax/Jax community week. Accuracy was improved from 0.64 to 0.70.
* The two T5 v1.1 base models are an uncased and cased version of `t5-v1.1-base`, again pre-trained from scratch on Dutch,
with a tokenizer also trained from scratch. The t5 v1.1 models are slightly different from the t5 models, and the
base models are trained with a dropout of 0.0. For fine-tuning it is intended to set this back to 0.1.
* The large cased model is a pre-trained Dutch version of `t5-v1.1-large`. Training of t5-v1.1-large proved difficult.
Without dropout regularization, the training would diverge at a certain point. With dropout training went better,
be it much slower than training the t5-model. At some point convergance was too slow to warrant further training.
The latest checkpoint, training scripts and metrics are available for reference. For actual fine-tuning the cased
base model is probably the better choice.
| | model | train seq len | acc | loss | batch size | epochs | steps | dropout | optim | lr | duration |
|---------------------------------------------------------------------------------------------------|---------|---------------|----------|----------|------------|--------|---------|---------|-----------|------|----------|
| [yhavinga/t5-base-dutch](https://huggingface.co/yhavinga/t5-base-dutch) | T5 | 512 | 0,70 | 1,38 | 128 | 1 | 528481 | 0.1 | adafactor | 5e-3 | 2d 9h |
| [yhavinga/t5-v1.1-base-dutch-uncased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-uncased) | t5-v1.1 | 1024 | 0,73 | 1,20 | 64 | 2 | 1014525 | 0.0 | adafactor | 5e-3 | 5d 5h |
| [yhavinga/t5-v1.1-base-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cased) | t5-v1.1 | 1024 | **0,78** | **0,96** | 64 | 2 | 1210000 | 0.0 | adafactor | 5e-3 | 6d 6h |
| [yhavinga/t5-v1.1-large-dutch-cased](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cased) | t5-v1.1 | 512 | 0,76 | 1,07 | 64 | 1 | 1120000 | 0.1 | adafactor | 5e-3 | 86 13h |
The cased t5-v1.1 Dutch models were fine-tuned on summarizing the CNN Daily Mail dataset.
| | model | input len | target len | Rouge1 | Rouge2 | RougeL | RougeLsum | Test Gen Len | epochs | batch size | steps | duration |
|-------------------------------------------------------------------------------------------------------|---------|-----------|------------|--------|--------|--------|-----------|--------------|--------|------------|-------|----------|
| [yhavinga/t5-v1.1-base-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-base-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,8 | 13,6 | 25,2 | 32,1 | 79 | 6 | 64 | 26916 | 2h 40m |
| [yhavinga/t5-v1.1-large-dutch-cnn-test](https://huggingface.co/yhavinga/t5-v1.1-large-dutch-cnn-test) | t5-v1.1 | 1024 | 96 | 34,4 | 13,6 | 25,3 | 31,7 | 81 | 5 | 16 | 89720 | 11h |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/). The HuggingFace 🤗 ecosystem was also
instrumental in many, if not all parts of the training. The following repositories where helpful in setting up the TPU-VM,
and training the models:
* [Gsarti's Pretrain and Fine-tune a T5 model with Flax on GCP](https://github.com/gsarti/t5-flax-gcp)
* [HUggingFace Flax MLM examples](https://github.com/huggingface/transformers/tree/master/examples/flax/language-modeling)
* [Flax/Jax Community week t5-base-dutch](https://huggingface.co/flax-community/t5-base-dutch)
Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/) | ac08e82106850492ddc1a18bc5b0244f |
JosephusCheung/ACertainThing | JosephusCheung | null | 23 | 3,760 | diffusers | 130 | text-to-image | false | false | false | creativeml-openrail-m | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | true | true | 5,563 | false |
# ACertainThing
**Try full functions with Google Colab free T4** [](https://colab.research.google.com/drive/1gwJViXR0UxoXx01qiU6uTSEKGjTagOgp?usp=sharing)
Anything3.0 is an overfitted model that takes liberties when it shouldn't be generating human images and certain details. However, the community has given it a high rating, and I believe that is because many lazy people who don't know how to write a prompt can use this overfitted model to generate high-quality images even if their prompts are poorly written.
Here is a ACertain version of Anything3.0, made with Dreambooth (idea of [LoRA](https://arxiv.org/abs/2106.09685) integrated), initialized with [ACertainModel](https://huggingface.co/JosephusCheung/ACertainModel).
Although this model may produce better results for image generation, it is built on two major problems. Firstly, it does not always stay true to your prompts; it adds irrelevant details, and sometimes these details are highly homogenized. Secondly, it is an unstable, overfitted model, similar to Anything3.0, and is not suitable for any form of further training. As far as I know, Anything3.0 is obtained by merging several models in just the right way, but it is itself an overfitted model with defects in both its saturation and configuration. However, as I mentioned earlier, it can make even poorly written prompts produce good output images, which leads many lazy people who are incapable of writing good prompts to quickly surpass those who study the writing of prompts carefully. Despite these problems, I still want to release an extended version of the model that caters to the preferences of many people in the community. I hope would you like it.
**In my personal view, I oppose all forms of model merging as it has no scientific principle and is nothing but a waste of time. It is a desire to get results without putting in the effort. That is why I do not like Anything3.0, or this model that is being released. But I respect the choices and preferences of the community, and I hope that you can also respect and understand my thoughts.**
If you want your prompts to be accurately output and want to learn the correct skills for using prompts, it is recommended that you use the more balanced model [ACertainModel](https://huggingface.co/JosephusCheung/ACertainModel).
e.g. **_masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden_**
## About online preview with Hosted inference API, also generation with this model
Parameters are not allowed to be modified, as it seems that it is generated with *Clip skip: 1*, for better performance, it is strongly recommended to use *Clip skip: 2* instead.
Here is an example of inference settings, if it is applicable with you on your own server: *Steps: 28, Sampler: Euler a, CFG scale: 11, Clip skip: 2*.
## 🧨 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.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "JosephusCheung/ACertainThing"
branch_name= "main"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "pikachu"
image = pipe(prompt).images[0]
image.save("./pikachu.png")
```
## Examples
Below are some examples of images generated using this model, with better performance on framing and hand gestures, as well as moving objects, comparing to other analogues:
**Anime Girl:**
```
1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
**Anime Boy:**
```
1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Is it a NovelAI based model? What is the relationship with SD1.2 and SD1.4?
See [ASimilarityCalculatior](https://huggingface.co/JosephusCheung/ASimilarityCalculatior) | 5ee62dfd9d525b8e49fb23e97b637d29 |
gokuls/distilbert_add_GLUE_Experiment_logit_kd_rte | gokuls | distilbert | 17 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | ['en'] | ['glue'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 2,366 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_add_GLUE_Experiment_logit_kd_rte
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4229
- Accuracy: 0.4729
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4684 | 1.0 | 10 | 0.4310 | 0.4729 |
| 0.4214 | 2.0 | 20 | 0.4342 | 0.4729 |
| 0.4216 | 3.0 | 30 | 0.4264 | 0.4729 |
| 0.4197 | 4.0 | 40 | 0.4311 | 0.4729 |
| 0.425 | 5.0 | 50 | 0.4297 | 0.4729 |
| 0.4192 | 6.0 | 60 | 0.4260 | 0.4729 |
| 0.4182 | 7.0 | 70 | 0.4243 | 0.4729 |
| 0.4184 | 8.0 | 80 | 0.4246 | 0.4729 |
| 0.4201 | 9.0 | 90 | 0.4240 | 0.4729 |
| 0.417 | 10.0 | 100 | 0.4259 | 0.4729 |
| 0.419 | 11.0 | 110 | 0.4269 | 0.4729 |
| 0.4165 | 12.0 | 120 | 0.4249 | 0.4729 |
| 0.4116 | 13.0 | 130 | 0.4229 | 0.4729 |
| 0.3924 | 14.0 | 140 | 0.4916 | 0.4729 |
| 0.3783 | 15.0 | 150 | 0.4539 | 0.4874 |
| 0.3384 | 16.0 | 160 | 0.4581 | 0.4982 |
| 0.3202 | 17.0 | 170 | 0.5284 | 0.4765 |
| 0.3054 | 18.0 | 180 | 0.4884 | 0.5162 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
| 3983539c1e0d3b9239fad65842c5175a |
Seongkyu/bert-base-cased-finetuned-squad | Seongkyu | bert | 12 | 20 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,261 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0179 | 1.0 | 6194 | 0.9548 |
| 0.7277 | 2.0 | 12388 | 0.9717 |
| 0.507 | 3.0 | 18582 | 1.0458 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
| 99e6dc2a7420e3c5c2c1bf4d38d56315 |
steja/whisper-small-somali | steja | whisper | 17 | 2 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['google/fleurs'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,677 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper_small_Somali
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs so_so dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0764
- Wer: 66.5950
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0205 | 30.74 | 400 | 1.8418 | 67.2524 |
| 0.0012 | 61.52 | 800 | 2.0764 | 66.5950 |
| 0.0006 | 92.3 | 1200 | 2.1537 | 67.6452 |
| 0.0004 | 123.07 | 1600 | 2.1930 | 67.1367 |
| 0.0004 | 153.81 | 2000 | 2.2065 | 66.9299 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
| 495ca3925e878afa61c118b501343ce9 |
stevemobs/deberta-base-combined-squad1-aqa-newsqa-50 | stevemobs | deberta | 17 | 5 | transformers | 0 | question-answering | true | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,226 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-base-combined-squad1-aqa-newsqa-50
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.9401 | 1.0 | 18532 | 0.8266 |
| 0.6811 | 2.0 | 37064 | 0.7756 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| 4b4f13a77feac309a62871339f9be0d1 |
martinbiber/marian-finetuned-kde4-en-to-fr | martinbiber | marian | 9 | 1 | transformers | 0 | text2text-generation | false | true | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,381 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# martinbiber/marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0539
- Validation Loss: 0.8992
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5911, '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': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.0539 | 0.8992 | 0 |
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
| 07ff73ab34898cc354d2c3fcc4f9571c |
pranay-j/whisper-large-v2-hindi | pranay-j | whisper | 17 | 1 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['hi'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,376 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2 finetuned Hindi
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2043
- Wer: 10.7225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0153 | 3.18 | 1000 | 0.2043 | 10.7225 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
| bab8dd3122d50e84a841be02356263eb |
lmqg/mt5-small-esquad-qg-ae | lmqg | mt5 | 40 | 126 | transformers | 0 | text2text-generation | true | false | false | cc-by-4.0 | ['es'] | ['lmqg/qg_esquad'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['question generation', 'answer extraction'] | true | true | true | 7,139 | false |
# Model Card of `lmqg/mt5-small-esquad-qg-ae`
This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
- **Language:** es
- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="es", model="lmqg/mt5-small-esquad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg-ae")
# answer extraction
answer = pipe("generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
# question generation
question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audiencia. <hl> El propósito de estos eventos es recaudar dinero para nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que han sufrido como consecuencia de inundaciones y / o sequías, o para reunir fondos para la creación de nuevas mezquitas como.")
```
## Evaluation
- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 83.39 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1 | 24.5 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2 | 16.48 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3 | 11.83 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4 | 8.79 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR | 21.66 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore | 58.34 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L | 23.13 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 79.06 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedF1Score (MoverScore) | 54.49 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (BERTScore) | 76.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedPrecision (MoverScore) | 52.96 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (BERTScore) | 81.94 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| QAAlignedRecall (MoverScore) | 56.21 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_esquad.default.json)
| | Score | Type | Dataset |
|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
| AnswerExactMatch | 57.63 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| AnswerF1Score | 75.31 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| BERTScore | 89.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_1 | 35.18 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_2 | 30.48 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_3 | 26.92 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| Bleu_4 | 23.89 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| METEOR | 43.11 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| MoverScore | 80.64 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
| ROUGE_L | 48.58 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) |
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 5
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
| b5d0ca1c100215d5125c5742498f34f7 |
DOOGLAK/wikigold_trained_no_DA | DOOGLAK | bert | 13 | 23 | transformers | 0 | token-classification | true | false | false | apache-2.0 | null | ['wikigold_splits'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,509 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# temp
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the wikigold_splits dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1322
- Precision: 0.8517
- Recall: 0.875
- F1: 0.8632
- Accuracy: 0.9607
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 167 | 0.1490 | 0.7583 | 0.7760 | 0.7671 | 0.9472 |
| No log | 2.0 | 334 | 0.1337 | 0.8519 | 0.8464 | 0.8491 | 0.9572 |
| 0.1569 | 3.0 | 501 | 0.1322 | 0.8517 | 0.875 | 0.8632 | 0.9607 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 2.4.0
- Tokenizers 0.11.6
| ce24cc56c92de95645a0cfe11f09fa0b |
mike157/flan-t5-base-flant5-apple-support | mike157 | t5 | 11 | 11 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | ['stackexchange_titlebody_best_voted_answer_jsonl'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,849 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-flant5-apple-support
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the stackexchange_titlebody_best_voted_answer_jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0475
- Rouge1: 12.4139
- Rouge2: 2.0562
- Rougel: 9.4938
- Rougelsum: 11.0524
- Gen Len: 18.9589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 232 | 3.0886 | 12.844 | 2.1734 | 9.8971 | 11.3641 | 18.8876 |
| No log | 2.0 | 464 | 3.0639 | 12.2909 | 2.1209 | 9.4999 | 10.9458 | 18.9416 |
| 3.3185 | 3.0 | 696 | 3.0538 | 12.4154 | 2.0984 | 9.4989 | 11.0684 | 18.9492 |
| 3.3185 | 4.0 | 928 | 3.0489 | 12.7043 | 2.1969 | 9.7356 | 11.3629 | 18.9481 |
| 3.187 | 5.0 | 1160 | 3.0475 | 12.4139 | 2.0562 | 9.4938 | 11.0524 | 18.9589 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
| dffce1434025d7cfe0fa92b3718fbf8e |
DrishtiSharma/whisper-small-hindi-2k-steps | DrishtiSharma | whisper | 15 | 0 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | ['hi'] | ['mozilla-foundation/common_voice_11_0'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['whisper-event', 'generated_from_trainer'] | true | true | true | 1,312 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hindi - Drishti Sharma
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: 0.2751
- Wer: 17.1985
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0598 | 2.44 | 2000 | 0.2751 | 17.1985 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
| d73ba0f12adc86e1b44d3a05d8b0544b |
limsc/reqroberta-tapt-epoch50 | limsc | roberta | 9 | 2 | transformers | 0 | fill-mask | false | true | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_keras_callback'] | true | true | true | 1,325 | false |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reqroberta-tapt-epoch50
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 37100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.19.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1
| 0c35d5a5e942b79dfb7a8d73b3c55c0d |
candra/wav2vec2-large-xls-r-300m-indonesia-colab | candra | wav2vec2 | 13 | 2 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | ['common_voice'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,853 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-indonesia-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3278
- Wer: 0.2831
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.0256 | 3.23 | 400 | 0.8938 | 0.8095 |
| 0.4608 | 6.45 | 800 | 0.3986 | 0.4415 |
| 0.2037 | 9.68 | 1200 | 0.3712 | 0.3881 |
| 0.1423 | 12.9 | 1600 | 0.3362 | 0.3547 |
| 0.1125 | 16.13 | 2000 | 0.3612 | 0.3452 |
| 0.0879 | 19.35 | 2400 | 0.3589 | 0.3201 |
| 0.0706 | 22.58 | 2800 | 0.3449 | 0.2989 |
| 0.0558 | 25.81 | 3200 | 0.3371 | 0.2941 |
| 0.0459 | 29.03 | 3600 | 0.3278 | 0.2831 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
| 24e2affc44b347e0aa6b14a7184528ca |
sayakpaul/distilbert-base-uncased-finetuned-emotion-lr-1e-05-wd-0002 | sayakpaul | distilbert | 10 | 3 | transformers | 0 | text-classification | true | false | false | apache-2.0 | null | ['emotion'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,396 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion-lr-1e-05-wd-0002
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: 0.7739
- Accuracy: 0.7495
- F1: 0.6924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3404 | 1.0 | 125 | 1.0081 | 0.637 | 0.5492 |
| 0.8738 | 2.0 | 250 | 0.7739 | 0.7495 | 0.6924 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.10.0
- Datasets 2.6.1
- Tokenizers 0.13.1
| 468f13ce161d30f767dc0916555ebbcd |
yanaiela/roberta-base-epoch_34 | yanaiela | roberta | 9 | 3 | transformers | 0 | fill-mask | true | false | false | mit | ['en'] | ['wikipedia', 'bookcorpus'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['roberta-base', 'roberta-base-epoch_34'] | false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 34
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_34.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
| e7926cf64da93567d578a16ffcdc5ed5 |
xpariz10/ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50-slower-LR | xpariz10 | audio-spectrogram-transformer | 7 | 0 | transformers | 0 | audio-classification | true | false | false | bsd-3-clause | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,629 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50-slower-LR
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7837
- Accuracy: 0.8929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 9.3646 | 1.0 | 28 | 6.0136 | 0.0893 |
| 2.9631 | 2.0 | 56 | 2.0175 | 0.5357 |
| 1.2435 | 3.0 | 84 | 1.1471 | 0.7679 |
| 0.7699 | 4.0 | 112 | 0.8559 | 0.875 |
| 0.5911 | 5.0 | 140 | 0.7837 | 0.8929 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
| 140d9ffd7c60ab805aa234b0141c4a80 |
imraan/ddpm-butterflies-128 | imraan | null | 13 | 3 | diffusers | 0 | null | false | false | false | apache-2.0 | ['en'] | ['huggan/smithsonian_butterflies_subset'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,228 | false |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/imraan/ddpm-butterflies-128/tensorboard?#scalars)
| 5e46e3ef303878003c6d20c1ec778e3f |
obokkkk/wav2vec2-base-timit-demo-colab2 | obokkkk | wav2vec2 | 12 | 5 | transformers | 0 | automatic-speech-recognition | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,642 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab2
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: 0.4805
- Wer: 0.3398
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4737 | 4.0 | 500 | 1.2889 | 0.9293 |
| 0.5838 | 8.0 | 1000 | 0.4751 | 0.4353 |
| 0.2141 | 12.0 | 1500 | 0.4809 | 0.3881 |
| 0.1259 | 16.0 | 2000 | 0.4587 | 0.3683 |
| 0.084 | 20.0 | 2500 | 0.4941 | 0.3601 |
| 0.0582 | 24.0 | 3000 | 0.4811 | 0.3482 |
| 0.0439 | 28.0 | 3500 | 0.4805 | 0.3398 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.10.3
| 63bc27ffb4a0fc145348c329b77eab64 |
Marscen/distilbert-base-uncased-finetuned-squad | Marscen | distilbert | 12 | 5 | transformers | 0 | question-answering | true | false | false | apache-2.0 | null | ['squad_v2'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,286 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
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: 1.4052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2178 | 1.0 | 8235 | 1.1827 |
| 0.9355 | 2.0 | 16470 | 1.3283 |
| 0.761 | 3.0 | 24705 | 1.4052 |
### Framework versions
- Transformers 4.19.4
- Pytorch 1.8.1+cu111
- Datasets 2.2.2
- Tokenizers 0.12.1
| 89f61bfa00ed65d4b31c506f192fbac2 |
yanaiela/roberta-base-epoch_48 | yanaiela | roberta | 9 | 3 | transformers | 0 | fill-mask | true | false | false | mit | ['en'] | ['wikipedia', 'bookcorpus'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['roberta-base', 'roberta-base-epoch_48'] | false | true | true | 2,102 | false |
# RoBERTa, Intermediate Checkpoint - Epoch 48
This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692),
trained on Wikipedia and the Book Corpus only.
We train this model for almost 100K steps, corresponding to 83 epochs.
We provide the 84 checkpoints (including the randomly initialized weights before the training)
to provide the ability to study the training dynamics of such models, and other possible use-cases.
These models were trained in part of a work that studies how simple statistics from data,
such as co-occurrences affects model predictions, which are described in the paper
[Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251).
This is RoBERTa-base epoch_48.
## Model Description
This model was captured during a reproduction of
[RoBERTa-base](https://huggingface.co/roberta-base), for English: it
is a Transformers model pretrained on a large corpus of English data, using the
Masked Language Modelling (MLM).
The intended uses, limitations, training data and training procedure for the fully trained model are similar
to [RoBERTa-base](https://huggingface.co/roberta-base). Two major
differences with the original model:
* We trained our model for 100K steps, instead of 500K
* We only use Wikipedia and the Book Corpus, as corpora which are publicly available.
### How to use
Using code from
[RoBERTa-base](https://huggingface.co/roberta-base), here is an example based on
PyTorch:
```
from transformers import pipeline
model = pipeline("fill-mask", model='yanaiela/roberta-base-epoch_83', device=-1, top_k=10)
model("Hello, I'm the <mask> RoBERTa-base language model")
```
## Citation info
```bibtex
@article{2207.14251,
Author = {Yanai Elazar and Nora Kassner and Shauli Ravfogel and Amir Feder and Abhilasha Ravichander and Marius Mosbach and Yonatan Belinkov and Hinrich Schütze and Yoav Goldberg},
Title = {Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions},
Year = {2022},
Eprint = {arXiv:2207.14251},
}
```
| f491a9502e627f11c8aee16bbdce3ab0 |
BayesBayes/distilgpt2-finetuned-wikitext2 | BayesBayes | gpt2 | 7 | 0 | transformers | 0 | text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,067 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.4834
- eval_runtime: 217.639
- eval_samples_per_second: 8.872
- eval_steps_per_second: 1.112
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
| 2b9b6cd2738bc783830f733f47b2446b |
sd-concepts-library/atm-ant-2 | sd-concepts-library | null | 9 | 0 | null | 0 | null | false | false | false | mit | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 1,010 | false | ### ATM Ant 2 on Stable Diffusion
This is the `<atm-ant>` 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 an `object`:
| d438baa0aedb85d91dd67485d670283a |
haesun/xlm-roberta-base-finetuned-panx-en | haesun | xlm-roberta | 10 | 7 | transformers | 0 | token-classification | true | false | false | mit | null | ['xtreme'] | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,319 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3932
- F1: 0.7032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1504 | 1.0 | 50 | 0.5992 | 0.4786 |
| 0.5147 | 2.0 | 100 | 0.4307 | 0.6468 |
| 0.3717 | 3.0 | 150 | 0.3932 | 0.7032 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
| e35095c5e33a3b18cb035bd705c3a3c0 |
milyiyo/paraphraser-spanish-t5-base | milyiyo | t5 | 18 | 3 | transformers | 0 | text2text-generation | true | false | false | apache-2.0 | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['generated_from_trainer'] | true | true | true | 1,867 | false |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# paraphraser-spanish-t5-base
This model is a fine-tuned version of [milyiyo/paraphraser-spanish-t5-base](https://huggingface.co/milyiyo/paraphraser-spanish-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7572
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1212 | 0.07 | 2000 | 0.8120 |
| 1.2263 | 0.14 | 4000 | 0.7773 |
| 1.1976 | 0.21 | 6000 | 0.7745 |
| 1.1828 | 0.28 | 8000 | 0.7675 |
| 1.1399 | 0.35 | 10000 | 0.7668 |
| 1.1378 | 0.42 | 12000 | 0.7651 |
| 1.1035 | 0.5 | 14000 | 0.7644 |
| 1.0923 | 0.57 | 16000 | 0.7633 |
| 1.0924 | 0.64 | 18000 | 0.7594 |
| 1.0943 | 0.71 | 20000 | 0.7578 |
| 1.0872 | 0.78 | 22000 | 0.7575 |
| 1.0755 | 0.85 | 24000 | 0.7599 |
| 1.0806 | 0.92 | 26000 | 0.7558 |
| 1.079 | 0.99 | 28000 | 0.7572 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
| 96d52a74cfe9de0b3cd0f0180b67be4a |
JosephusCheung/ACertainModel | JosephusCheung | null | 24 | 1,407 | diffusers | 124 | text-to-image | false | false | false | creativeml-openrail-m | ['en'] | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | true | true | 5,332 | false |
# ACertainModel
**Try full functions with Google Colab free T4** [](https://colab.research.google.com/drive/1ldhBc70wvuvkp4Af_vNTzTfBXwpf_cH5?usp=sharing)
Check Twitter [#ACertainModel](https://twitter.com/hashtag/ACertainModel) for community artworks
Welcome to ACertainModel - a latent diffusion model for weebs. This model is intended to produce high-quality, highly detailed anime style pictures with just a few prompts. Like other anime-style Stable Diffusion models, it also supports danbooru tags, including artists, to generate images.
Since I noticed that the laion-aesthetics introduced in the Stable-Diffusion-v-1-4 checkpoint hindered finetuning anime style illustration generation model, Dreambooth was used to finetune some tags separately to make it closer to what it was in SD1.2. To avoid overfitting and possible language drift, I added a huge amount of auto-generated pictures from a single word prompt to the training set, using models that are popular in the community such as Anything-3.0, together with partially manual selected full-danbooru images within a year, for further native training. I am also aware of a method of [LoRA](https://arxiv.org/abs/2106.09685), with a similar idea, finetuning attention layer solely, to have better performance on eyes, hands, and other details.
For copyright compliance and technical experiment, it was trained from few artist images directly. It was trained on Dreambooth with pictures generated from several popular diffusion models in the community. The checkpoint was initialized with the weights of a Stable Diffusion Model and subsequently fine-tuned for 2K GPU hours on V100 32GB and 600 GPU hours on A100 40GB at 512P dynamic aspect ratio resolution with a certain ratio of unsupervised auto-generated images from several popular diffusion models in the community with some Textual Inversions and Hypernetworks. We do know some tricks on xformers and 8-bit optimization, but we didn't use any of them for better quality and stability. Up to 15 branches are trained simultaneously, cherry-picking about every 20,000 steps.
e.g. **_masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden_**
## About online preview with Hosted inference API, also generation with this model
Parameters are not allowed to be modified, as it seems that it is generated with *Clip skip: 1*, for better performance, it is strongly recommended to use *Clip skip: 2* instead.
Here is an example of inference settings, if it is applicable with you on your own server: *Steps: 28, Sampler: Euler a, CFG scale: 11, Clip skip: 2*.
## 🧨 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.
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "JosephusCheung/ACertainModel"
branch_name= "main"
pipe = StableDiffusionPipeline.from_pretrained(model_id, revision=branch_name, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "pikachu"
image = pipe(prompt).images[0]
image.save("./pikachu.png")
```
## Examples
Below are some examples of images generated using this model, with better performance on framing and hand gestures, as well as moving objects, comparing to other analogues:
**Anime Girl:**
```
1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
**Anime Boy:**
```
1boy, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden
Steps: 28, Sampler: Euler a, CFG scale: 11, Seed: 114514, Clip skip: 2
```
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
## Is it a NovelAI based model? What is the relationship with SD1.2 and SD1.4?
See [ASimilarityCalculatior](https://huggingface.co/JosephusCheung/ASimilarityCalculatior) | b1a5264ce6a07c547c1dec7135c8c6ce |
shpotes/codegen-350M-mono | shpotes | codegen | 13 | 2 | transformers | 3 | text-generation | true | false | false | bsd-3-clause | null | null | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | [] | false | true | true | 2,531 | false |
# Overview
The CodeGen model was proposed in by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. From Salesforce Research.
The abstract from the paper is the following:
Program synthesis strives to generate a computer program as a solution to a given problem specification. We propose a conversational program synthesis approach via large language models, which addresses the challenges of searching over a vast program space and user intent specification faced in prior approaches. Our new approach casts the process of writing a specification and program as a multi-turn conversation between a user and a system. It treats program synthesis as a sequence prediction problem, in which the specification is expressed in natural language and the desired program is conditionally sampled. We train a family of large language models, called CodeGen, on natural language and programming language data. With weak supervision in the data and the scaling up of data size and model size, conversational capacities emerge from the simple autoregressive language modeling. To study the model behavior on conversational program synthesis, we develop a multi-turn programming benchmark (MTPB), where solving each problem requires multi-step synthesis via multi-turn conversation between the user and the model. Our findings show the emergence of conversational capabilities and the effectiveness of the proposed conversational program synthesis paradigm. In addition, our model CodeGen (with up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the HumanEval benchmark. We plan to make the training library JaxFormer including checkpoints available as open source.
# How to use
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("shpotes/codegen-350M-mono")
model = AutoModelForCausalLM.from_pretrained("shpotes/codegen-350M-mono", trust_remote_code=True)
input_ids = tokenizer(
context,
truncation=True,
padding=True,
return_tensors='pt',
pad_token_id=pad_token_id,
).input_ids
input_ids_len = input_ids.shape[1]
with torch.no_grad():
input_ids = input_ids
tokens = model.generate(
input_ids,
do_sample=True,
num_return_sequences=num_return_sequences,
temperature=temp,
max_length=input_ids_len + max_length_sample,
top_p=top_p,
use_cache=True,
)
text = tokenizer.batch_decode(tokens[:, input_ids_len:, ...])
``` | 2fd41c74714fe1370b4999e1939d19fd |