davanstrien/model_cards_with_readmes · Datasets at Hugging Face

spacy/en_core_web_trf

spacy

null

26

699

spacy

10

token-classification

false

mit

['en']

null

0

['spacy', 'token-classification']

false

true

2,830

false

### Details: https://spacy.io/models/en#en_core_web_trf English transformer pipeline (roberta-base). Components: transformer, tagger, parser, ner, attribute_ruler, lemmatizer. | Feature | Description | | --- | --- | | **Name** | `en_core_web_trf` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `transformer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston) [ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University) [WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) [roberta-base](https://github.com/pytorch/fairseq/tree/master/examples/roberta) (Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov) | | **License** | `MIT` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (112 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` | | **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` | | **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.86 | | `TOKEN_P` | 99.57 | | `TOKEN_R` | 99.58 | | `TOKEN_F` | 99.57 | | `TAG_ACC` | 97.79 | | `SENTS_P` | 95.04 | | `SENTS_R` | 84.92 | | `SENTS_F` | 89.69 | | `DEP_UAS` | 95.27 | | `DEP_LAS` | 93.95 | | `ENTS_P` | 89.78 | | `ENTS_R` | 90.49 | | `ENTS_F` | 90.13 |

d3bb3f6ccc7732cce4ce4544f36046db

netsvetaev/netsvetaev-black

netsvetaev

null

12

0

null

0

text-to-image

false

mit

['en']

null

0

['diffusion', 'netsvetaev', 'dreambooth', 'stable-diffusion', 'text-to-image']

false

true

1,885

false

Hello! This is the model, based on my paintings on a black background and SD 1.5. This is the second onw, trained with 29 images and 2900 steps. The token is «netsvetaev black style». Best suited for: abstract seamless patterns, images similar to my original paintings with blue triangles, and large objects like «cat face» or «girl face». It works well with landscape orientation and embiggen. It has MIT license, you can use it for free. Best used with Invoke AI: https://github.com/invoke-ai/InvokeAI (The examples below contain metadata for it) ![Pattern by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000199.fb94ed7d.3205796735.png) ![Pattern by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000161.3229b2d8.404674009.png) ![Cat Face by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000162.7f0f58dd.514748655.png) ![Kitchen interior by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000166.2df18f6b.2385464611.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000165.ce1281f2.514748655.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000170.00bf11f4.2086077640.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000200.d9724450.1486284977.png) ![Abstract painting by Artur Netsvetaev Stable DIffusion model](https://huggingface.co/netsvetaev/netsvetaev-black/resolve/main/000185.74e77e78.4135017744.png) ________________________ Artur Netsvetaev, 2022 https://netsvetaev.com

095ceb5e80f95d76f1b4a8cb782848d7

flood/distilbert-base-uncased-finetuned-clinc

flood

distilbert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

['clinc_oos']

null

0

['generated_from_trainer']

true

1,481

false

# distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7793 - Accuracy: 0.9161 ## 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: 48 - eval_batch_size: 48 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2926 | 1.0 | 318 | 3.2834 | 0.7374 | | 2.6259 | 2.0 | 636 | 1.8736 | 0.8303 | | 1.5511 | 3.0 | 954 | 1.1612 | 0.8913 | | 1.0185 | 4.0 | 1272 | 0.8625 | 0.91 | | 0.8046 | 5.0 | 1590 | 0.7793 | 0.9161 | ### Framework versions - Transformers 4.19.3 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

5486ae3503f9737683e6ae1ef1399064

ychu4/distilbert-base-uncased-finetuned-cola

ychu4

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

1

0

['generated_from_trainer']

true

1,571

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7512 - Matthews Correlation: 0.5097 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5237 | 1.0 | 535 | 0.5117 | 0.4469 | | 0.3496 | 2.0 | 1070 | 0.5538 | 0.4965 | | 0.2377 | 3.0 | 1605 | 0.6350 | 0.4963 | | 0.1767 | 4.0 | 2140 | 0.7512 | 0.5097 | | 0.1383 | 5.0 | 2675 | 0.8647 | 0.5056 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.8.1+cu102 - Datasets 1.15.1 - Tokenizers 0.10.1

b9b875b63ab2df93af7c92c6910bbb29

Helsinki-NLP/opus-mt-de-hr

Helsinki-NLP

marian

10

102

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-de-hr * source languages: de * target languages: hr * OPUS readme: [de-hr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-hr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/de-hr/opus-2020-01-26.zip) * test set translations: [opus-2020-01-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-hr/opus-2020-01-26.test.txt) * test set scores: [opus-2020-01-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/de-hr/opus-2020-01-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.de.hr | 42.6 | 0.643 |

d0c270513d89d9b266baea2a48da2e0e

lizaboiarchuk/bert-tiny-oa-finetuned

lizaboiarchuk

bert

8

2

transformers

0

fill-mask

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,823

false

# lizaboiarchuk/bert-tiny-oa-finetuned This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.0626 - Validation Loss: 3.7514 - Epoch: 4 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'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': -525, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.6311 | 4.1088 | 0 | | 4.2579 | 3.7859 | 1 | | 4.0635 | 3.7253 | 2 | | 4.0658 | 3.6842 | 3 | | 4.0626 | 3.7514 | 4 | ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Tokenizers 0.12.1

31a99ed4917ff2e63d6cc3366110de3c

google/t5-efficient-large-nh12

google

t5

12

7

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,258

false

# T5-Efficient-LARGE-NH12 (Deep-Narrow version) T5-Efficient-LARGE-NH12 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-large-nh12** - is of model type **Large** with the following variations: - **nh** is **12** It has **662.23** million parameters and thus requires *ca.* **2648.91 MB** of memory in full precision (*fp32*) or **1324.45 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

4ec890fd73f0c992b43a7913e18eeb2b

sd-concepts-library/female-kpop-singer

sd-concepts-library

null

10

0

null

5

null

false

mit

null

0

[]

false

true

1,436

false

### female kpop singer on Stable Diffusion This is the `<female-kpop-star>` 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). Simple test model I made with images of Choerry, Hwasa, Nancy, and last 2 are Hyuna. Placeholder token: <female-kpop-star> Initializer token: musician Here is the new concept you will be able to use as an `object`: ![<female-kpop-star> 0](https://huggingface.co/sd-concepts-library/female-kpop-singer/resolve/main/concept_images/0.jpeg) ![<female-kpop-star> 1](https://huggingface.co/sd-concepts-library/female-kpop-singer/resolve/main/concept_images/2.jpeg) ![<female-kpop-star> 2](https://huggingface.co/sd-concepts-library/female-kpop-singer/resolve/main/concept_images/3.jpeg) ![<female-kpop-star> 3](https://huggingface.co/sd-concepts-library/female-kpop-singer/resolve/main/concept_images/4.jpeg) ![<female-kpop-star> 4](https://huggingface.co/sd-concepts-library/female-kpop-singer/resolve/main/concept_images/1.jpeg) Feel free to modify / further train this model without credit.

98fd16d131ba13128ff02d5ae5397111

cahya/whisper-large-id

cahya

whisper

27

32

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['id']

['mozilla-foundation/common_voice_11_0', 'magic_data', 'TITML']

null

0

['whisper-event', 'generated_from_trainer']

true

1,891

false

# Whisper Large Indonesian This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0, magic_data, titml id dataset. It achieves the following results on the evaluation set: - Loss: 0.2034 - Wer: 6.2483 ## 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-06 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1516 | 0.5 | 1000 | 0.1730 | 6.5664 | | 0.1081 | 1.0 | 2000 | 0.1638 | 6.3682 | | 0.0715 | 1.49 | 3000 | 0.1803 | 6.2713 | | 0.1009 | 1.99 | 4000 | 0.1796 | 6.2667 | | 0.0387 | 2.49 | 5000 | 0.2054 | 6.4927 | | 0.0494 | 2.99 | 6000 | 0.2034 | 6.2483 | | 0.0259 | 3.48 | 7000 | 0.2226 | 6.3497 | | 0.0265 | 3.98 | 8000 | 0.2274 | 6.4004 | | 0.0232 | 4.48 | 9000 | 0.2443 | 6.5618 | | 0.015 | 4.98 | 10000 | 0.2413 | 6.4927 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

74b8270fc623df3b7fea9f8ea5a476cf

sd-concepts-library/gram-tops

sd-concepts-library

null

12

0

null

0

null

false

mit

null

0

[]

false

true

1,338

false

### gram-tops on Stable Diffusion This is the `<gram-tops>` 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`: ![<gram-tops> 0](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/5.jpeg) ![<gram-tops> 1](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/2.jpeg) ![<gram-tops> 2](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/6.jpeg) ![<gram-tops> 3](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/0.jpeg) ![<gram-tops> 4](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/1.jpeg) ![<gram-tops> 5](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/4.jpeg) ![<gram-tops> 6](https://huggingface.co/sd-concepts-library/gram-tops/resolve/main/concept_images/3.jpeg)

af6f1a1fddfc382747711f1fd157184a

AndyChiang/cdgp-csg-bart-cloth

AndyChiang

bart

9

3

transformers

0

text2text-generation

true

false

mit

['en']

['cloth']

null

0

['bart', 'cloze', 'distractor', 'generation']

false

true

3,710

false

# cdgp-csg-bart-cloth ## Model description This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**. Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**facebook/bart-base**](https://huggingface.co/facebook/bart-base) model. For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP). ## How to use? 1. Download the model by hugging face transformers. ```python from transformers import BartTokenizer, BartForConditionalGeneration, pipeline tokenizer = BartTokenizer.from_pretrained("AndyChiang/cdgp-csg-bart-cloth") csg_model = BartForConditionalGeneration.from_pretrained("AndyChiang/cdgp-csg-bart-cloth") ``` 2. Create a unmasker. ```python unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10) ``` 3. Use the unmasker to generate the candidate set of distractors. ```python sent = "I feel <mask> now. </s> happy" cs = unmasker(sent) print(cs) ``` ## Dataset This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below. | Number of questions | Train | Valid | Test | | ------------------- | ----- | ----- | ----- | | **Middle school** | 22056 | 3273 | 3198 | | **High school** | 54794 | 7794 | 8318 | | **Total** | 76850 | 11067 | 11516 | You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned. ## Training We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More detail is in our paper. ### Training hyperparameters The following hyperparameters were used during training: - Pre-train language model: [facebook/bart-base](https://huggingface.co/facebook/bart-base) - Optimizer: adam - Learning rate: 0.0001 - Max length of input: 64 - Batch size: 64 - Epoch: 1 - Device: NVIDIA® Tesla T4 in Google Colab ## Testing The evaluations of this model as a Candidate Set Generator in CDGP is as follows: | P@1 | F1@3 | F1@10 | MRR | NDCG@10 | | ----- | ----- | ----- | ----- | ------- | | 14.20 | 11.07 | 11.37 | 24.29 | 31.74 | ## Other models ### Candidate Set Generator | Models | CLOTH | DGen | | ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | **BERT** | [cdgp-csg-bert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) | | **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) | | **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) | | **BART** | [*cdgp-csg-bart-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) | ### Distractor Selector **fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext) ## Citation None

538cd8db3aa5cc4b6e121358f763768b

deepiit98/Pub-clustered

deepiit98

distilbert

8

10

transformers

0

question-answering

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,855

false

# deepiit98/Pub-clustered This model is a fine-tuned version of [nandysoham16/16-clustered_aug](https://huggingface.co/nandysoham16/16-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3787 - Train End Logits Accuracy: 0.8715 - Train Start Logits Accuracy: 0.8924 - Validation Loss: 0.1505 - Validation End Logits Accuracy: 1.0 - Validation Start Logits Accuracy: 0.9231 - 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': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.3787 | 0.8715 | 0.8924 | 0.1505 | 1.0 | 0.9231 | 0 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

07b0c2b3af54d701409d6043048ff3be

rishabhjain16/whisper_medium_to_pf10h

rishabhjain16

whisper

23

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,725

false

# openai/whisper-medium This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1594 - Wer: 21.8343 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0269 | 5.0 | 500 | 0.1069 | 118.0302 | | 0.0049 | 10.01 | 1000 | 0.1263 | 135.2788 | | 0.0009 | 15.01 | 1500 | 0.1355 | 94.5731 | | 0.0001 | 20.01 | 2000 | 0.1413 | 7.5188 | | 0.0001 | 25.01 | 2500 | 0.1515 | 7.2508 | | 0.0001 | 30.02 | 3000 | 0.1568 | 24.8493 | | 0.0 | 35.02 | 3500 | 0.1588 | 22.1470 | | 0.0 | 40.02 | 4000 | 0.1594 | 21.8343 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

bd7d5d769b59f9440273c7bb88d63ecf

sd-concepts-library/kodakvision500t

sd-concepts-library

null

9

0

null

13

null

false

mit

null

0

[]

false

true

1,222

false

### KodakVision500T on Stable Diffusion This is the `<kodakvision_500T>` 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). This concept was trained on **6** photographs taken with **Kodak Vision 3 500T**, through **1800** steps. Here are some generated images from the concept that you will be able to use as a `style`: ![<kodakvision_500T> 4](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_4.png) ![<kodakvision_500T> 3](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_3.png) ![<kodakvision_500T> 2](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_2.png) ![<kodakvision_500T> 1](https://huggingface.co/sd-concepts-library/kodakvision500t/resolve/main/kodakvision_500T_1.png)

75d2bdca72eef586f7ff1c3b8150ab78

Robertooo/ELL_pretrained

Robertooo

roberta

6

7

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,243

false

# ELL_pretrained This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9006 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1542 | 1.0 | 1627 | 2.1101 | | 2.0739 | 2.0 | 3254 | 2.0006 | | 2.0241 | 3.0 | 4881 | 1.7874 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

d8f5079101ebdef7f79abbdf0b8f4f1b

shashank2123/t5-finetuned-for-GEC

shashank2123

t5

10

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

false

true

1,537

false

# t5-finetuned-for-GEC This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.3949 - Bleu: 0.3571 - Gen Len: 19.0 ## 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: 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 0.3958 | 1.0 | 4053 | 0.4236 | 0.3493 | 19.0 | | 0.3488 | 2.0 | 8106 | 0.4076 | 0.3518 | 19.0 | | 0.319 | 3.0 | 12159 | 0.3962 | 0.3523 | 19.0 | | 0.3105 | 4.0 | 16212 | 0.3951 | 0.3567 | 19.0 | | 0.3016 | 5.0 | 20265 | 0.3949 | 0.3571 | 19.0 | ### Framework versions - Transformers 4.9.1 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

e9a98bc915e3de98fef4984774dd17a9

ncats/EpiExtract4GARD-v2

ncats

bert

17

114

transformers

0

token-classification

true

false

other

['en']

['ncats/EpiSet4NER']

null

0

['token-classification', 'ncats']

false

true

9,493

false

## DOCUMENTATION UPDATES IN PROGRESS ## Model description **EpiExtract4GARD-v2** is a fine-tuned [BioBERT-base-cased](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) model that is ready to use for **Named Entity Recognition** of locations (LOC), epidemiologic types (EPI), and epidemiologic rates (STAT). This model was fine-tuned on EpiSet4NER-v2 for epidemiological information from rare disease abstracts. See dataset documentation for details on the weakly supervised teaching methods and dataset biases and limitations. See [EpiExtract4GARD on GitHub](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard) for details on the entire pipeline. #### How to use You can use this model with the Hosted inference API to the right with this [test sentence](https://pubmed.ncbi.nlm.nih.gov/21659675/): "27 patients have been diagnosed with PKU in Iceland since 1947. Incidence 1972-2008 is 1/8400 living births." See code below for use with Transformers *pipeline* for NER.: ~~~ from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("ncats/EpiExtract4GARD") tokenizer = AutoTokenizer.from_pretrained("ncats/EpiExtract4GARD") NER_pipeline = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') sample = "The live-birth prevalence of mucopolysaccharidoses in Estonia. Previous studies on the prevalence of mucopolysaccharidoses (MPS) in different populations have shown considerable variations. There are, however, few data with regard to the prevalence of MPSs in Fenno-Ugric populations or in north-eastern Europe, except for a report about Scandinavian countries. A retrospective epidemiological study of MPSs in Estonia was undertaken, and live-birth prevalence of MPS patients born between 1985 and 2006 was estimated. The live-birth prevalence for all MPS subtypes was found to be 4.05 per 100,000 live births, which is consistent with most other European studies. MPS II had the highest calculated incidence, with 2.16 per 100,000 live births (4.2 per 100,000 male live births), forming 53% of all diagnosed MPS cases, and was twice as high as in other studied European populations. The second most common subtype was MPS IIIA, with a live-birth prevalence of 1.62 in 100,000 live births. With 0.27 out of 100,000 live births, MPS VI had the third-highest live-birth prevalence. No cases of MPS I were diagnosed in Estonia, making the prevalence of MPS I in Estonia much lower than in other European populations. MPSs are the third most frequent inborn error of metabolism in Estonia after phenylketonuria and galactosemia." sample2 = "Early Diagnosis of Classic Homocystinuria in Kuwait through Newborn Screening: A 6-Year Experience. Kuwait is a small Arabian Gulf country with a high rate of consanguinity and where a national newborn screening program was expanded in October 2014 to include a wide range of endocrine and metabolic disorders. A retrospective study conducted between January 2015 and December 2020 revealed a total of 304,086 newborns have been screened in Kuwait. Six newborns were diagnosed with classic homocystinuria with an incidence of 1:50,000, which is not as high as in Qatar but higher than the global incidence. Molecular testing for five of them has revealed three previously reported pathogenic variants in the CBS gene, c.969G>A, p.(Trp323Ter); c.982G>A, p.(Asp328Asn); and the Qatari founder variant c.1006C>T, p.(Arg336Cys). This is the first study to review the screening of newborns in Kuwait for classic homocystinuria, starting with the detection of elevated blood methionine and providing a follow-up strategy for positive results, including plasma total homocysteine and amino acid analyses. Further, we have demonstrated an increase in the specificity of the current newborn screening test for classic homocystinuria by including the methionine to phenylalanine ratio along with the elevated methionine blood levels in first-tier testing. Here, we provide evidence that the newborn screening in Kuwait has led to the early detection of classic homocystinuria cases and enabled the affected individuals to lead active and productive lives." #Sample 1 is from: Krabbi K, Joost K, Zordania R, Talvik I, Rein R, Huijmans JG, Verheijen FV, Õunap K. The live-birth prevalence of mucopolysaccharidoses in Estonia. Genet Test Mol Biomarkers. 2012 Aug;16(8):846-9. doi: 10.1089/gtmb.2011.0307. Epub 2012 Apr 5. PMID: 22480138; PMCID: PMC3422553. #Sample 2 is from: Alsharhan H, Ahmed AA, Ali NM, Alahmad A, Albash B, Elshafie RM, Alkanderi S, Elkazzaz UM, Cyril PX, Abdelrahman RM, Elmonairy AA, Ibrahim SM, Elfeky YME, Sadik DI, Al-Enezi SD, Salloum AM, Girish Y, Al-Ali M, Ramadan DG, Alsafi R, Al-Rushood M, Bastaki L. Early Diagnosis of Classic Homocystinuria in Kuwait through Newborn Screening: A 6-Year Experience. Int J Neonatal Screen. 2021 Aug 17;7(3):56. doi: 10.3390/ijns7030056. PMID: 34449519; PMCID: PMC8395821. NER_pipeline(sample) NER_pipeline(sample2) ~~~ Or if you download [*classify_abs.py*](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/classify_abs.py), [*extract_abs.py*](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/extract_abs.py), and [*gard-id-name-synonyms.json*](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/gard-id-name-synonyms.json) from GitHub then you can test with this [*additional* code](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/Case%20Study.ipynb): ~~~ import pandas as pd import extract_abs import classify_abs pd.set_option('display.max_colwidth', None) NER_pipeline = extract_abs.init_NER_pipeline() GARD_dict, max_length = extract_abs.load_GARD_diseases() nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer = classify_abs.init_classify_model() def search(term,num_results = 50): return extract_abs.search_term_extraction(term, num_results, NER_pipeline, GARD_dict, max_length,nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer) a = search(7058) a b = search('Santos Mateus Leal syndrome') b c = search('Fellman syndrome') c d = search('GARD:0009941') d e = search('Homocystinuria') e ~~~ #### Limitations and bias ## Training data It was trained on [EpiSet4NER](https://huggingface.co/datasets/ncats/EpiSet4NER). See dataset documentation for details on the weakly supervised teaching methods and dataset biases and limitations. The training dataset distinguishes between the beginning and continuation of an entity so that if there are back-to-back entities of the same type, the model can output where the second entity begins. As in the dataset, each token will be classified as one of the following classes: Abbreviation|Description ---------|-------------- O |Outside of a named entity B-LOC | Beginning of a location I-LOC | Inside of a location B-EPI | Beginning of an epidemiologic type (e.g. "incidence", "prevalence", "occurrence") I-EPI | Epidemiologic type that is not the beginning token. B-STAT | Beginning of an epidemiologic rate I-STAT | Inside of an epidemiologic rate +More | Description pending ### EpiSet Statistics Beyond any limitations due to the EpiSet4NER dataset, this model is limited in numeracy due to BERT-based model's use of subword embeddings, which is crucial for epidemiologic rate identification and limits the entity-level results. Recent techniques in numeracy could be used to improve the performance of the model without improving the underlying dataset. ## Training procedure This model was trained on a [AWS EC2 p3.2xlarge](https://aws.amazon.com/ec2/instance-types/), which utilized a single Tesla V100 GPU, with these hyperparameters: 4 epochs of training (AdamW weight decay = 0.05) with a batch size of 16. Maximum sequence length = 192. Model was fed one sentence at a time.                    Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at Axle Informatics/NCATS for contributing this model.

bf4976e91400ee73838550905b8a7e0d

Khalsuu/english-filipino-wav2vec2-l-xls-r-test-08

Khalsuu

wav2vec2

13

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['filipino_voice']

null

0

['generated_from_trainer']

true

1,888

false

# english-filipino-wav2vec2-l-xls-r-test-08 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5968 - Wer: 0.4255 ## 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: 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: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3434 | 2.09 | 400 | 2.2857 | 0.9625 | | 1.6304 | 4.19 | 800 | 1.1547 | 0.7268 | | 0.9231 | 6.28 | 1200 | 1.0252 | 0.6186 | | 0.6098 | 8.38 | 1600 | 0.9371 | 0.5494 | | 0.4922 | 10.47 | 2000 | 0.7092 | 0.5478 | | 0.3652 | 12.57 | 2400 | 0.7358 | 0.5149 | | 0.2735 | 14.66 | 2800 | 0.6270 | 0.4646 | | 0.2038 | 16.75 | 3200 | 0.5717 | 0.4506 | | 0.1552 | 18.85 | 3600 | 0.5968 | 0.4255 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

5eda547dc16e02d5cbf848c5bb79cbd5

dweb/deberta-base-CoLA

dweb

deberta

13

9

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,180

false

# deberta-base-CoLA This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1655 - Accuracy: 0.8482 - F1: 0.8961 - Roc Auc: 0.8987 - Mcc: 0.6288 ## 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 - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Roc Auc | Mcc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:------:| | 0.5266 | 1.0 | 535 | 0.4138 | 0.8159 | 0.8698 | 0.8627 | 0.5576 | | 0.3523 | 2.0 | 1070 | 0.3852 | 0.8387 | 0.8880 | 0.9041 | 0.6070 | | 0.2479 | 3.0 | 1605 | 0.3981 | 0.8482 | 0.8901 | 0.9120 | 0.6447 | | 0.1712 | 4.0 | 2140 | 0.4732 | 0.8558 | 0.9008 | 0.9160 | 0.6486 | | 0.1354 | 5.0 | 2675 | 0.7181 | 0.8463 | 0.8938 | 0.9024 | 0.6250 | | 0.0876 | 6.0 | 3210 | 0.8453 | 0.8520 | 0.8992 | 0.9123 | 0.6385 | | 0.0682 | 7.0 | 3745 | 1.0282 | 0.8444 | 0.8938 | 0.9061 | 0.6189 | | 0.0431 | 8.0 | 4280 | 1.1114 | 0.8463 | 0.8960 | 0.9010 | 0.6239 | | 0.0323 | 9.0 | 4815 | 1.1663 | 0.8501 | 0.8970 | 0.8967 | 0.6340 | | 0.0163 | 10.0 | 5350 | 1.1655 | 0.8482 | 0.8961 | 0.8987 | 0.6288 | ### Framework versions - Transformers 4.11.0 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

ac8fa12d2c41b12d5aad9e72086b5748

espnet/kan-bayashi_jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave

espnet

null

20

0

espnet

0

text-to-speech

false

cc-by-4.0

['ja']

['jsut']

null

0

['espnet', 'audio', 'text-to-speech']

false

true

1,859

false

## ESPnet2 TTS pretrained model ### `kan-bayashi/jsut_tts_train_transformer_raw_phn_jaconv_pyopenjtalk_prosody_train.loss.ave` ♻️ Imported from https://zenodo.org/record/5499040/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```python # coming soon ``` ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } @inproceedings{hayashi2020espnet, title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit}, author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu}, booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={7654--7658}, year={2020}, organization={IEEE} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

0eb689d7bfc3433a0acf40fbb681bf82

Helsinki-NLP/opus-mt-en-kwn

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-en-kwn * source languages: en * target languages: kwn * OPUS readme: [en-kwn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-kwn/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-kwn/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-kwn/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/en-kwn/opus-2020-01-08.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.en.kwn | 27.6 | 0.513 |

aeee6eb87d1d6b766b4a8a2a0bd16b87

jonatasgrosman/exp_w2v2t_ar_vp-nl_s103

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ar']

false

true

469

false

# exp_w2v2t_ar_vp-nl_s103 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 (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.

90d0f3a6a25dc6430040c3df00e40823

jonatasgrosman/exp_w2v2t_de_hubert_s921

jonatasgrosman

hubert

10

3

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'de']

false

true

452

false

# exp_w2v2t_de_hubert_s921 Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition using the train split of [Common Voice 7.0 (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.

b88c8a04b69f8ebac733f1b04d5e89e9

sd-concepts-library/karl-s-lzx-1

sd-concepts-library

null

9

0

null

0

null

false

mit

null

0

[]

false

true

1,003

false

### karl's lzx 1 on Stable Diffusion This is the `<lzx>` 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`: ![<lzx> 0](https://huggingface.co/sd-concepts-library/karl-s-lzx-1/resolve/main/concept_images/0.jpeg) ![<lzx> 1](https://huggingface.co/sd-concepts-library/karl-s-lzx-1/resolve/main/concept_images/3.jpeg) ![<lzx> 2](https://huggingface.co/sd-concepts-library/karl-s-lzx-1/resolve/main/concept_images/1.jpeg) ![<lzx> 3](https://huggingface.co/sd-concepts-library/karl-s-lzx-1/resolve/main/concept_images/2.jpeg)

2d522dfb442f6f2e2524168113891e96

jonatasgrosman/exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s113

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'de']

false

true

476

false

# exp_w2v2r_de_xls-r_age_teens-0_sixties-10_s113 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

2df71a7511737286b41d7e63aa418607

maesneako/ES_corlec_DeepESP-gpt2-spanish

maesneako

gpt2

8

2

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

2,029

false

# ES_corlec_DeepESP-gpt2-spanish This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.0360 ## 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-06 - 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: 200 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.2471 | 0.4 | 2000 | 4.2111 | | 4.1503 | 0.79 | 4000 | 4.1438 | | 4.0749 | 1.19 | 6000 | 4.1077 | | 4.024 | 1.59 | 8000 | 4.0857 | | 3.9855 | 1.98 | 10000 | 4.0707 | | 3.9465 | 2.38 | 12000 | 4.0605 | | 3.9277 | 2.78 | 14000 | 4.0533 | | 3.9159 | 3.17 | 16000 | 4.0482 | | 3.8918 | 3.57 | 18000 | 4.0448 | | 3.8789 | 3.97 | 20000 | 4.0421 | | 3.8589 | 4.36 | 22000 | 4.0402 | | 3.8554 | 4.76 | 24000 | 4.0387 | | 3.8509 | 5.15 | 26000 | 4.0377 | | 3.8389 | 5.55 | 28000 | 4.0370 | | 3.8288 | 5.95 | 30000 | 4.0365 | | 3.8293 | 6.34 | 32000 | 4.0362 | | 3.8202 | 6.74 | 34000 | 4.0360 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.1+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

3777a0b48e15aa83fbfcc3a8d58367f1

kadirnar/RRDB_ESRGAN_x4

kadirnar

null

3

0

null

0

null

false

apache-2.0

null

0

['Super-Resolution', 'computer-vision', 'ESRGAN', 'gan']

false

true

1,229

false

### Model Description [ESRGAN](https://arxiv.org/abs/2107.10833): ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution [Paper Repo](https://github.com/xinntao/ESRGAN): Implementation of paper. ### Installation ``` pip install bsrgan ``` ### BSRGAN Usage ```python from bsrgan import BSRGAN model = BSRGAN(weights='kadirnar/RRDB_ESRGAN_x4', device='cuda:0', hf_model=True) model.save = True pred = model.predict(img_path='data/image/test.png') ``` ### BibTeX Entry and Citation Info ``` @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} } ``` ``` @InProceedings{wang2018esrgan, author = {Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Loy, Chen Change}, title = {ESRGAN: Enhanced super-resolution generative adversarial networks}, booktitle = {The European Conference on Computer Vision Workshops (ECCVW)}, month = {September}, year = {2018} } ```

7d1bdaa4ecfd1abdd5fd07d706828baf

gchhablani/bert-base-cased-finetuned-qnli

gchhablani

bert

52

75

transformers

1

text-classification

true

false

apache-2.0

['en']

['glue']

null

1

0

['generated_from_trainer', 'fnet-bert-base-comparison']

true

2,215

false

# bert-base-cased-finetuned-qnli This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3986 - Accuracy: 0.9099 The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased). ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used: ```bash #!/usr/bin/bash python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name qnli \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 3 \\n --output_dir bert-base-cased-finetuned-qnli \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 0.337 | 1.0 | 6547 | 0.9013 | 0.2448 | | 0.1971 | 2.0 | 13094 | 0.9143 | 0.2839 | | 0.1175 | 3.0 | 19641 | 0.9099 | 0.3986 | ### Framework versions - Transformers 4.11.0.dev0 - Pytorch 1.9.0 - Datasets 1.12.1 - Tokenizers 0.10.3

3cdfb6d834695135d2121ce917efadfe

kamilali/distilbert-base-uncased-finetuned-custom

kamilali

bert

12

5

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,338

false

# distilbert-base-uncased-finetuned-custom This model is a fine-tuned version of [bert-large-uncased-whole-word-masking-finetuned-squad](https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7808 ## 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: 1 - eval_batch_size: 1 - 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 | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 368 | 1.1128 | | 2.1622 | 2.0 | 736 | 0.8494 | | 1.2688 | 3.0 | 1104 | 0.7808 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.4 - Tokenizers 0.11.6

d2e58228e74b9ef3f6fe20b0f21751c6

vitorgrs/MyModel

vitorgrs

null

20

15

diffusers

1

text-to-image

false

openrail

null

1

0

1

0

['text-to-image', 'stable-diffusion']

false

true

1,400

false

# Welcome to MY MODEL! Hi! Dreambooth model for me. Use it as you want and respect the license model. ## How do I use it? The keyword for this model is **vitordeluccagrs**. Try to use "*portrait of vitordeluccagrs shirtless reading a book in a chair, art by artgerm*". Be creative. Specify the faces features you want, hair style, the environment, if is a art, it's in which artist style? Want it realistic? Say it! And also say which ISO, camera, etc. Want to look like 500px and unsplash pictures? Say it too! Want it to look like Blade Runner? Say it. **Be creative**. USE NEGATIVE PROMPT! Stable Diffusion works best if you use negative prompt. With negative prompt, you are saying what things you don't want in picture, like you know... extra fingers!. # My social networks [Mastodon](https://bolha.us/vitordelucca) [Instagram](https://instagram.com/vitor_dlucca) [Twitter](https://twitter.com/vitor_dlucca) [Telegram](https://vitordelucca.t.me) # Sample pictures ![enter image description here](https://huggingface.co/vitorgrs/MyModel/resolve/main/samples/one.png) ![enter image description here](https://huggingface.co/vitorgrs/MyModel/resolve/main/samples/two.png) ![enter image description here](https://huggingface.co/vitorgrs/MyModel/resolve/main/samples/three.png) ![enter image description here](https://huggingface.co/vitorgrs/MyModel/resolve/main/samples/four.png)

666eb99b6a7ebe5c40c670b750a35b96

sd-concepts-library/bob-dobbs

sd-concepts-library

null

19

0

null

2

null

false

mit

null

0

[]

false

true

2,128

false

### Bob Dobbs on Stable Diffusion This is the `<bob>` 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`: ![<bob> 0](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/dobbs-512-2.png) ![<bob> 1](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/dobbs-512-1.png) ![<bob> 2](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/bob-wall-1.jpg) ![<bob> 3](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/dobbs-512-3.png) ![<bob> 4](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/1000.jpg) ![<bob> 5](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/12421049.jpg) ![<bob> 6](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/Stencil-Large.png) ![<bob> 7](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/TheWay.jpg) ![<bob> 8](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/8c09d2cc630f5e28.jpeg) ![<bob> 9](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/TheFutureRobots.png) ![<bob> 10](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/dobbs-512-5.png) ![<bob> 11](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/2016-02-03_Dobbsassault.jpg) ![<bob> 12](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/ItsMeBobSo.png) ![<bob> 13](https://huggingface.co/sd-concepts-library/bob-dobbs/resolve/main/concept_images/bob-graffiti.jpg)

19c82a393e0dae7c5f0b7004ba42f542

figfig/local_test_model

figfig

whisper

10

1

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['figfig/restaurant_order_local_test']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,476

false

# restaurant_local_test_model This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the local_test_data dataset. It achieves the following results on the evaluation set: - Loss: 0.5435 - Wer: 78.5714 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 10.0 | 10 | 2.2425 | 7.1429 | | No log | 20.0 | 20 | 0.6651 | 0.0 | | 2.4375 | 30.0 | 30 | 0.5776 | 35.7143 | | 2.4375 | 40.0 | 40 | 0.5435 | 78.5714 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

9b7c2631cbd37143750ea1036e1c2bea

sd-concepts-library/lego-astronaut

sd-concepts-library

null

9

0

null

3

null

false

mit

null

0

[]

false

true

1,070

false

### Lego astronaut on Stable Diffusion This is the `<lego-astronaut>` 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`: ![<lego-astronaut> 0](https://huggingface.co/sd-concepts-library/lego-astronaut/resolve/main/concept_images/3.jpeg) ![<lego-astronaut> 1](https://huggingface.co/sd-concepts-library/lego-astronaut/resolve/main/concept_images/0.jpeg) ![<lego-astronaut> 2](https://huggingface.co/sd-concepts-library/lego-astronaut/resolve/main/concept_images/2.jpeg) ![<lego-astronaut> 3](https://huggingface.co/sd-concepts-library/lego-astronaut/resolve/main/concept_images/1.jpeg)

44a2655b30c3724d1ab3d54e61eb1e24

sd-concepts-library/monster-toy

sd-concepts-library

null

9

0

null

4

null

false

mit

null

0

[]

false

true

1,042

false

### <monster-toy> on Stable Diffusion This is the `<monster-toy>` 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`: ![<monster-toy> 0](https://huggingface.co/sd-concepts-library/monster-toy/resolve/main/concept_images/1.jpeg) ![<monster-toy> 1](https://huggingface.co/sd-concepts-library/monster-toy/resolve/main/concept_images/3.jpeg) ![<monster-toy> 2](https://huggingface.co/sd-concepts-library/monster-toy/resolve/main/concept_images/2.jpeg) ![<monster-toy> 3](https://huggingface.co/sd-concepts-library/monster-toy/resolve/main/concept_images/0.jpeg)

381ec11a3c238585e8dd29a5918888b6

gustavecortal/distilcamembert-cae-feeling

gustavecortal

camembert

6

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,673

false

# distilcamembert-cae-feeling This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9307 - Precision: 0.6783 - Recall: 0.6835 - F1: 0.6767 ## 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.1901 | 1.0 | 40 | 1.0935 | 0.1963 | 0.4430 | 0.2720 | | 1.0584 | 2.0 | 80 | 0.8978 | 0.6304 | 0.6076 | 0.5776 | | 0.6805 | 3.0 | 120 | 0.8577 | 0.6918 | 0.6709 | 0.6759 | | 0.3938 | 4.0 | 160 | 1.0034 | 0.6966 | 0.6582 | 0.6586 | | 0.2713 | 5.0 | 200 | 0.9307 | 0.6783 | 0.6835 | 0.6767 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

16f29d1e63eb3b0a6a8b8d93e0d6a15c

plai-edp-test/distilbert_base_uncased

plai-edp-test

distilbert

8

0

transformers

0

fill-mask

false

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert']

false

true

8,470

false

# DistilBERT base model (uncased) This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-uncased). It was introduced in [this paper](https://arxiv.org/abs/1910.01108). The code for the distillation process can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation). This model is uncased: it does not make a difference between english and English. ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. More precisely, it was pretrained with three objectives: - Distillation loss: the model was trained to return the same probabilities as the BERT base model. - Masked language modeling (MLM): this is part of the original training loss of the BERT base model. When taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Cosine embedding loss: the model was also trained to generate hidden states as close as possible as the BERT base model. This way, the model learns the same inner representation of the English language than its teacher model, while being faster for inference or downstream tasks. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=distilbert) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use You can use this model directly with a pipeline for masked language modeling: ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("Hello I'm a [MASK] model.") [{'sequence': "[CLS] hello i'm a role model. [SEP]", 'score': 0.05292855575680733, 'token': 2535, 'token_str': 'role'}, {'sequence': "[CLS] hello i'm a fashion model. [SEP]", 'score': 0.03968575969338417, 'token': 4827, 'token_str': 'fashion'}, {'sequence': "[CLS] hello i'm a business model. [SEP]", 'score': 0.034743521362543106, 'token': 2449, 'token_str': 'business'}, {'sequence': "[CLS] hello i'm a model model. [SEP]", 'score': 0.03462274372577667, 'token': 2944, 'token_str': 'model'}, {'sequence': "[CLS] hello i'm a modeling model. [SEP]", 'score': 0.018145186826586723, 'token': 11643, 'token_str': 'modeling'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import DistilBertTokenizer, DistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = DistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import DistilBertTokenizer, TFDistilBertModel tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). ```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='distilbert-base-uncased') >>> unmasker("The White man worked as a [MASK].") [{'sequence': '[CLS] the white man worked as a blacksmith. [SEP]', 'score': 0.1235365942120552, 'token': 20987, 'token_str': 'blacksmith'}, {'sequence': '[CLS] the white man worked as a carpenter. [SEP]', 'score': 0.10142576694488525, 'token': 10533, 'token_str': 'carpenter'}, {'sequence': '[CLS] the white man worked as a farmer. [SEP]', 'score': 0.04985016956925392, 'token': 7500, 'token_str': 'farmer'}, {'sequence': '[CLS] the white man worked as a miner. [SEP]', 'score': 0.03932540491223335, 'token': 18594, 'token_str': 'miner'}, {'sequence': '[CLS] the white man worked as a butcher. [SEP]', 'score': 0.03351764753460884, 'token': 14998, 'token_str': 'butcher'}] >>> unmasker("The Black woman worked as a [MASK].") [{'sequence': '[CLS] the black woman worked as a waitress. [SEP]', 'score': 0.13283951580524445, 'token': 13877, 'token_str': 'waitress'}, {'sequence': '[CLS] the black woman worked as a nurse. [SEP]', 'score': 0.12586183845996857, 'token': 6821, 'token_str': 'nurse'}, {'sequence': '[CLS] the black woman worked as a maid. [SEP]', 'score': 0.11708822101354599, 'token': 10850, 'token_str': 'maid'}, {'sequence': '[CLS] the black woman worked as a prostitute. [SEP]', 'score': 0.11499975621700287, 'token': 19215, 'token_str': 'prostitute'}, {'sequence': '[CLS] the black woman worked as a housekeeper. [SEP]', 'score': 0.04722772538661957, 'token': 22583, 'token_str': 'housekeeper'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The model was trained on 8 16 GB V100 for 90 hours. See the [training code](https://github.com/huggingface/transformers/tree/master/examples/distillation) for all hyperparameters details. ## Evaluation results When fine-tuned on downstream tasks, this model achieves the following results: Glue test results: | Task | MNLI | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:| | | 82.2 | 88.5 | 89.2 | 91.3 | 51.3 | 85.8 | 87.5 | 59.9 | ### BibTeX entry and citation info ```bibtex @article{Sanh2019DistilBERTAD, title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, journal={ArXiv}, year={2019}, volume={abs/1910.01108} } ``` <a href="https://huggingface.co/exbert/?model=distilbert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

a75c71419364c4b880f611067bf63823

brashandplucky/ddpm-butterflies-128

brashandplucky

null

13

0

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,236

false

# 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/brashandplucky/ddpm-butterflies-128/tensorboard?#scalars)

fe41e696d38935f845b976da2c142a4d

Buseak/my_awesome_wnut_model

Buseak

bert

12

1

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,546

false

# my_awesome_wnut_model This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2661 - Precision: 0.8999 - Recall: 0.8933 - F1: 0.8966 - Accuracy: 0.9264 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 488 | 0.3614 | 0.8731 | 0.8606 | 0.8668 | 0.9043 | | 0.6843 | 2.0 | 976 | 0.2872 | 0.8927 | 0.8856 | 0.8891 | 0.9209 | | 0.3517 | 3.0 | 1464 | 0.2661 | 0.8999 | 0.8933 | 0.8966 | 0.9264 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

16f8e05c8486deb6e3eabcb4d2eb7752

huggan/pix2pix-night2day

huggan

null

5

0

null

1

null

true

false

mit

null

['huggan/night2day']

null

0

['huggan', 'gan']

false

true

2,374

false

# MyModelName ## Model description [Pix2pix Model](https://arxiv.org/abs/1611.07004) is a conditional adversarial networks, a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. ## Intended uses & limitations: Used for reconstruction of images from edges #### How to use ```python from torchvision.transforms import Compose, Resize, ToTensor, Normalize from PIL import Image from torchvision.utils import save_image import cv2 from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet transform = Compose( [ Resize((256, 256), Image.BICUBIC), ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ] ) model = GeneratorUNet.from_pretrained('huggan/pix2pix-night2day') def predict_fn(img): inp = transform(img).unsqueeze(0) out = model(inp) save_image(out, 'out.png', normalize=True) return 'out.png' predict_fn(img) ``` #### Limitations and bias * Gives unrealistic colors in the image * Gives Blurry image sometimes ## Training data * [night2day](https://huggingface.co/datasets/huggan/night2day) ## Training procedure ``` # clone the repository git clone https://github.com/huggingface/community-events.git pip install . # change directory cd community-events/huggan/pytorch/pix2pix/ # define config accelerate config # launch training with required parameters accelerate launch train.py --checkpoint_interval 5 --dataset huggan/night2day --push_to_hub --model_name pix2pix-night2day --batch_size 128 --n_epochs 50 ``` ## Generated Images Here, * First Image Row: Input Image * Second Image Row: Generated Image * Third Image Row: Target Image ![image1](7000.png) ![image2](6500.png) ### BibTeX entry and citation info ```bibtex @article{pix2pix2017, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A}, journal={CVPR}, year={2017} } ```

ae24493af07785bdfe4487e58e51a850

ultra-coder54732/comment-detection-prop-16

ultra-coder54732

bert

30

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

922

false

# comment-detection-prop-16 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-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: 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: 20 ### Training results ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

74b516b7c244ef0dfaa9e26e31033edf

Prem11100/donut-base-Label-studio-200-invoices

Prem11100

vision-encoder-decoder

14

3

transformers

0

null

true

false

mit

null

['imagefolder']

null

0

['generated_from_trainer']

true

1,002

false

# donut-base-Label-studio-200-invoices This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

0c9e525fb1ef8ef88a6fe54ed9a5e93e

thammarat-th/distilbert-base-uncased-finetuned-imdb

thammarat-th

distilbert

14

3

transformers

0

fill-mask

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,319

false

# distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.2591 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.4216 | 1.0 | 782 | 2.2803 | | 2.3719 | 2.0 | 1564 | 2.2577 | | 2.3407 | 3.0 | 2346 | 2.2320 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.12.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

250d46085d1ae14b38e0bf6e82ca7aff

NeuroSenko/abyss_orange_mix_senko_by_rimukoro_hyper

NeuroSenko

null

34

0

null

0

null

false

mit

null

['NeuroSenko/senko_by_rimukoro']

null

0

['Stable Diffusion', 'Senko', 'Hypernetwork']

false

true

2,784

false

## Description This hypernetwork will help you to make your Senko-san be look like she was drawn by Rimukoro. This model was trained using [AbyssOrangeMix_base](https://huggingface.co/WarriorMama777/OrangeMixs/blob/main/Models/AbyssOrangeMix/AbyssOrangeMix_base.ckpt) model so it should work fine with that specific model or with other relative models. ## Usage For using this hypernetwork just place .pt file in your `models\hypernetworks` directory and then depends on your UI you will need to choose this hypernetwork in settings or use it directly in your positive prompt like `<hypernet:abyss_orange_mix_senko_by_rimukoro:1.0>` ## Dataset Feel free to use a dataset I used for training this model. You can find it [here](https://huggingface.co/datasets/NeuroSenko/senko_by_rimukoro). ## Examples ![example](example.png) ## PNG Info Example masterpiece, best quality, 1girl, solo, cinematic lighting, 1girl, solo, senko $sewayaki kitsune no senko-san$, senko-san, sewayaki kitsune no senko-san, animal ears, fox ears, fox girl, fox tail, hair flower, hair ornament, orange eyes, orange hair, rimukoro, short hair, tail, flat chest, looking at viewer, light smile, full body, forest, rain, darkness, moon, wet clothes, blush, jacket, T-shirt, skirt Negative prompt: ugly, old, amateur drawing, odd, fat, lowres, text, error, worst quality, low quality, jpeg artifacts, signature, watermark, username, (blurry:1.3), out of focus, cropped, out of frame, cloned face, mutilated, deformed, gross proportions, disfigured, mutated hands, poorly drawn hands, bad anatomy, (bad hands:1.4), missing fingers, extra digit, (extra fingers:1.3), fewer digits, poorly drawn face, fused fingers, long neck, extra limbs, broken limb, asymmetrical eyes cell shading, watercolor Steps: 30, Sampler: DDIM, CFG scale: 12, Seed: 1393421640, Size: 640x832, Model hash: ffa7b160, Model: AbyssOrangeMix_base, Hypernet: abyss_orange_mix_senko_by_rimukoro, Hypernet hash: f3753abd, Denoising strength: 0.7, Eta: 0.69, Clip skip: 2, Hires upscale: 2, Hires upscaler: Latent ## Chosing a hypernetwork with non-default amount of steps Hypernetwork `abyss_orange_mix_senko_by_rimukoro.pt` presents a model which was trained on 4000 amount of steps (which I personally prefer). I also published hypernetworks which were trained on different amount of steps (up to 15000). You can find these hypernetworks in [models folder](https://huggingface.co/NeuroSenko/abyss_orange_mix_senko_by_rimukoro_hyper/tree/main/models). To make it easier for you to choose a hypernetwork I publish [this grid](https://neurosenko.github.io/sd-grid-viewer/?configUrl=https://neurosenko.github.io/sd-grids/orange-senko-by-rimukoro/config.json) which you can use for comparing these hypernetworks using 5 different seeds.

e9db635631c2a79fb0c0af09d7ee1220

OpenMatch/ance-tele_nq_qry-encoder

OpenMatch

bert

7

3

transformers

0

feature-extraction

true

false

mit

null

0

[]

false

true

897

false

This model is the **query** encoder of ANCE-Tele trained on NQ, described in the EMNLP 2022 paper ["Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives"](https://arxiv.org/pdf/2210.17167.pdf). The associated GitHub repository is available at https://github.com/OpenMatch/ANCE-Tele. ANCE-Tele only trains with self-mined negatives (teleportation negatives) without using additional negatives (e.g., BM25, other DR systems) and eliminates the dependency on filtering strategies and distillation modules. |NQ (Test)|R@5|R@20|R@20| |:---|:---|:---|:---| |ANCE-Tele|77.0|84.9|89.7| ``` @inproceedings{sun2022ancetele, title={Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives}, author={Si Sun, Chenyan Xiong, Yue Yu, Arnold Overwijk, Zhiyuan Liu and Jie Bao}, booktitle={Proceedings of EMNLP 2022}, year={2022} } ```

494f8866ada1f9e3ec7361acb9adbb52

uck/distilbert-base-uncased-finetuned-emotion

uck

distilbert

14

0

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,335

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2245 - Accuracy: 0.92 - F1: 0.9203 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8171 | 1.0 | 250 | 0.3222 | 0.907 | 0.9055 | | 0.2546 | 2.0 | 500 | 0.2245 | 0.92 | 0.9203 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.8.1 - Datasets 2.4.0 - Tokenizers 0.12.1

51561382d9e9903534e8dfb1ade9ad86

cyburn/gauzy_storms

cyburn

null

8

0

null

0

null

false

unknown

null

0

[]

false

true

760

false

# Gauzy Storm finetuned style Model Produced from publicly available pictures in landscape, portrait and square format. This model is focussed on creating animal hybrid artwork. ## Model info The models included was trained on "multi-resolution" images. ## Using the model * common subject prompt tokens: `<wathever> by gauzy storms` ## Example prompts `bear deer by gauzy storms`: <img src="https://huggingface.co/cyburn/gauzy_storms/resolve/main/1.png" alt="Picture." width="500"/> `pinguin by gauzy storms`: <img src="https://huggingface.co/cyburn/gauzy_storms/resolve/main/2.png" alt="Picture." width="500"/> `unicorn zebra by gauzy storms`: <img src="https://huggingface.co/cyburn/gauzy_storms/resolve/main/3.png" alt="Picture." width="500"/>

1c039a60103a4955b61ff5ff244542c8

chenglu/caicai-dog-heywhale

chenglu

null

17

39

diffusers

2

text-to-image

true

false

creativeml-openrail-m

null

1

0

1

0

['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal']

false

true

838

false

# DreamBooth model for the caicai concept trained by chenglu. This is a Stable Diffusion model fine-tuned on the caicai concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of caicai dog** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is a Stable Diffusion model fine-tuned on `dog` images for the animal theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. Thanks to @hhhxynh in the HF China community. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('chenglu/caicai-dog-heywhale') image = pipeline().images[0] image ```

9c6c5bb4477d9f6e3c368904b83358db

ZhiyuanQiu/camembert-base-finetuned-Train_RAW10-dd

ZhiyuanQiu

camembert

12

6

transformers

0

token-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,448

false

# camembert-base-finetuned-Train_RAW10-dd This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2175 - Precision: 0.8744 - Recall: 0.9056 - F1: 0.8897 - Accuracy: 0.9357 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1873 | 1.0 | 9930 | 0.2088 | 0.8652 | 0.8927 | 0.8788 | 0.9326 | | 0.1533 | 2.0 | 19860 | 0.2175 | 0.8744 | 0.9056 | 0.8897 | 0.9357 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

05bfd4b3ecc3358ed23fac0a1a31703e

09panesara/distilbert-base-uncased-finetuned-cola

09panesara

distilbert

13

29

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

1

0

['generated_from_trainer']

true

1,572

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7580 - Matthews Correlation: 0.5406 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5307 | 1.0 | 535 | 0.5094 | 0.4152 | | 0.3545 | 2.0 | 1070 | 0.5230 | 0.4940 | | 0.2371 | 3.0 | 1605 | 0.6412 | 0.5087 | | 0.1777 | 4.0 | 2140 | 0.7580 | 0.5406 | | 0.1288 | 5.0 | 2675 | 0.8494 | 0.5396 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

8049a4a9534380cfa876b5fd9f798304

betelgeux/bert-base-uncased-issues-128

betelgeux

bert

10

6

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,930

false

# bert-base-uncased-issues-128 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: 2.0348 ## 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: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.3932 | 1.0 | 1409 | 2.0750 | | 2.1659 | 2.0 | 2818 | 1.9781 | | 2.0364 | 3.0 | 4227 | 2.1215 | | 1.9399 | 4.0 | 5636 | 2.1018 | | 1.8857 | 5.0 | 7045 | 1.9919 | | 1.813 | 6.0 | 8454 | 2.2653 | | 1.7505 | 7.0 | 9863 | 2.0857 | | 1.7196 | 8.0 | 11272 | 1.9211 | | 1.672 | 9.0 | 12681 | 1.9853 | | 1.6379 | 10.0 | 14090 | 2.0391 | | 1.6037 | 11.0 | 15499 | 1.9305 | | 1.5699 | 12.0 | 16908 | 2.0291 | | 1.5363 | 13.0 | 18317 | 2.0492 | | 1.5155 | 14.0 | 19726 | 1.8807 | | 1.4999 | 15.0 | 21135 | 1.8604 | | 1.4784 | 16.0 | 22544 | 2.0348 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

5945eeff94e28a30394bbd6accfe6da0

google/t5-efficient-tiny-ff6000

google

t5

12

9

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,257

false

# T5-Efficient-TINY-FF6000 (Deep-Narrow version) T5-Efficient-TINY-FF6000 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-tiny-ff6000** - is of model type **Tiny** with the following variations: - **ff** is **6000** It has **36.55** million parameters and thus requires *ca.* **146.21 MB** of memory in full precision (*fp32*) or **73.1 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

e4d9f3e9d4591523e8227ee21b3d44ac

Graphcore/convnext-base-ipu

Graphcore

null

3

203

null

0

null

false

apache-2.0

null

0

[]

false

true

1,486

false

# Graphcore/convnext-base-ipu Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at [hf.co/hardware/graphcore](https://huggingface.co/hardware/graphcore). Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project. ## Model description Paper link : [A ConvNet for the 2020s](https://arxiv.org/pdf/2201.03545.pdf) ## Intended uses & limitations This model contains just the `IPUConfig` files for running the [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) model on Graphcore IPUs. **This model contains no model weights, only an IPUConfig.** ## Usage ``` from optimum.graphcore import IPUConfig ipu_config = IPUConfig.from_pretrained("Graphcore/convnext-base-ipu") ```

78681aa9ef055be24159893d5f7beac6

MultiBertGunjanPatrick/multiberts-seed-2-2000k

MultiBertGunjanPatrick

bert

7

5

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-2']

false

true

6,487

false

# MultiBERTs Seed 2 Checkpoint 2000k (uncased) Seed 2 intermediate checkpoint 2000k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). ## Model description MultiBERTs models are transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MultiBERTs model as inputs. ## Intended uses & limitations You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=multiberts) to look for fine-tuned versions on a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ### How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-2000k') model = BertModel.from_pretrained("multiberts-seed-2-2000k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. For an understanding of bias of this particular checkpoint, please try out this checkpoint with the snippet present in the [Limitation and bias section](https://huggingface.co/bert-base-uncased#limitations-and-bias) of the [bert-base-uncased](https://huggingface.co/bert-base-uncased) checkpoint. ## Training data The MultiBERTs models were pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Training procedure ### Preprocessing The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are then of the form: ``` [CLS] Sentence A [SEP] Sentence B [SEP] ``` With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two "sentences" has a combined length of less than 512 tokens. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. ### Pretraining The full model was trained on 16 Cloud TPU v2 chips for two million steps with a batch size of 256. The sequence length was set to 512 throughout. The optimizer used is Adam with a learning rate of 1e-4, \$\beta_{1} = 0.9\$ and \$\beta_{2} = 0.999\$, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-16163, author = {Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, title = {The MultiBERTs: {BERT} Reproductions for Robustness Analysis}, journal = {CoRR}, volume = {abs/2106.16163}, year = {2021}, url = {https://arxiv.org/abs/2106.16163}, eprinttype = {arXiv}, eprint = {2106.16163}, timestamp = {Mon, 05 Jul 2021 15:15:50 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-16163.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` <a href="https://huggingface.co/exbert/?model=multiberts"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

321dbfeac1abc4a1112e5e39558bb185

jonatasgrosman/exp_w2v2t_ja_vp-it_s544

jonatasgrosman

wav2vec2

10

4

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ja']

false

true

469

false

# exp_w2v2t_ja_vp-it_s544 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

e80dec13aee31fa52e5416345ff47b2c

paola-md/recipe-lr0.0001-wd0.05-bs64

paola-md

roberta

6

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,470

false

# recipe-lr0.0001-wd0.05-bs64 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2792 - Rmse: 0.5284 - Mse: 0.2792 - Mae: 0.4268 ## 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: 256 - eval_batch_size: 256 - 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 | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2798 | 1.0 | 623 | 0.2789 | 0.5281 | 0.2789 | 0.4219 | | 0.2786 | 2.0 | 1246 | 0.2795 | 0.5287 | 0.2795 | 0.4287 | | 0.2785 | 3.0 | 1869 | 0.2792 | 0.5284 | 0.2792 | 0.4268 | ### Framework versions - Transformers 4.19.0.dev0 - Pytorch 1.9.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1

e76c2db5ea81b6b5e9d091b3ccf49a8b

salihkavaf/distilbert-base-uncased-finetuned-imdb

salihkavaf

distilbert

8

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,610

false

# salihkavaf/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [salihkavaf/distilbert-base-uncased-finetuned-imdb](https://huggingface.co/salihkavaf/distilbert-base-uncased-finetuned-imdb) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.6769 - Validation Loss: 2.5848 - 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': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, '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: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.6769 | 2.5848 | 0 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

be4080f3d0638bb876999aea5fea30bf

goharava/bart-large-fine-tuned-large_

goharava

bart

11

3

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,431

false

# bart-large-finetuned-large This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6397 - Rouge1: 88.2870 - Rouge2: 26.4705 - Rougel: 88.1924 - Rougelsum: 88.3415 - Gen Len: 6.0323 ## 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 - 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 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 121 | 0.8676 | 67.7680 | 19.6386 | 67.5697 | 67.5758 | 6.2774 | | No log | 2.0 | 242 | 0.6661 | 73.6309 | 21.6079 | 73.2496 | 73.5335 | 5.3957 | | No log | 3.0 | 363 | 0.6649 | 82.6362 | 21.4663 | 82.3944 | 82.6107 | 5.6624 | | No log | 4.0 | 484 | 0.6598 | 86.4811 | 25.3580 | 86.1949 | 86.3580 | 5.7914 | | 0.5135 | 5.0 | 605 | 0.8032 | 86.0334 | 25.1510 | 85.8895 | 85.9038 | 6.5634 | | 0.5135 | 6.0 | 726 | 0.6981 | 88.0139 | 25.6152 | 87.9025 | 87.9932 | 6.3591 | | 0.5135 | 7.0 | 847 | 0.6991 | 88.7421 | 25.6469 | 88.5959 | 88.7255 | 6.3376 | | 0.5135 | 8.0 | 968 | 0.5995 | 88.9180 | 26.9917 | 88.6984 | 88.8878 | 5.8538 | | 0.1613 | 9.0 | 1089 | 0.5973 | 88.5923 | 26.7081 | 88.4593 | 88.6287 | 5.8387 | | 0.1613 | 10.0 | 1210 | 0.6397 | 88.2870 | 26.4705 | 88.1924 | 88.3415 | 6.0323 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

05205cc18c7a92baccfca8c2a97e0df1

jonatasgrosman/exp_w2v2t_ar_r-wav2vec2_s779

jonatasgrosman

wav2vec2

10

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'ar']

false

true

462

false

# exp_w2v2t_ar_r-wav2vec2_s779 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (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.

1c2ed4b3d32ac43575044a51b6d3a76c

juancopi81/course-bert-finetuned-squad

juancopi81

bert

8

3

transformers

0

question-answering

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,275

false

# juancopi81/course-bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0547 - 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': 2e-05, 'decay_steps': 5546, '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 | Epoch | |:----------:|:-----:| | 1.0547 | 0 | ### Framework versions - Transformers 4.20.1 - TensorFlow 2.8.2 - Datasets 2.3.2 - Tokenizers 0.12.1

9b49412836fba3c01b35d01b615fe652

viktor-enzell/wav2vec2-large-voxrex-swedish-4gram

viktor-enzell

wav2vec2

12

29

transformers

5

automatic-speech-recognition

true

false

cc0-1.0

['sv']

['common_voice', 'NST_Swedish_ASR_Database', 'P4', 'The_Swedish_Culturomics_Gigaword_Corpus']

null

0

['audio', 'automatic-speech-recognition', 'speech', 'hf-asr-leaderboard', 'sv']

true

2,972

false

# KBLab's wav2vec 2.0 large VoxRex Swedish (C) with 4-gram model Training of the acoustic model is the work of KBLab. See [VoxRex-C](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) for more details. This repo extends the acoustic model with a social media 4-gram language model for boosted performance. ## Model description VoxRex-C is extended with a 4-gram language model estimated from a subset extracted from [The Swedish Culturomics Gigaword Corpus](https://spraakbanken.gu.se/resurser/gigaword) from Språkbanken. The subset contains 40M words from the social media genre between 2010 and 2015. ## How to use #### Simple usage example with pipeline ```python import torch from transformers import pipeline # Load the model. Using GPU if available model_name = 'viktor-enzell/wav2vec2-large-voxrex-swedish-4gram' device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') pipe = pipeline(model=model_name).to(device) # Run inference on an audio file output = pipe('path/to/audio.mp3')['text'] ``` #### More verbose usage example with audio pre-processing Example of transcribing 1% of the Common Voice test split. The model expects 16kHz audio, so audio with another sampling rate is resampled to 16kHz. ```python from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM from datasets import load_dataset import torch import torchaudio.functional as F # Import model and processor. Using GPU if available model_name = 'viktor-enzell/wav2vec2-large-voxrex-swedish-4gram' device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device); processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) # Import and process speech data common_voice = load_dataset('common_voice', 'sv-SE', split='test[:1%]') def speech_file_to_array(sample): # Convert speech file to array and downsample to 16 kHz sampling_rate = sample['audio']['sampling_rate'] sample['speech'] = F.resample(torch.tensor(sample['audio']['array']), sampling_rate, 16_000) return sample common_voice = common_voice.map(speech_file_to_array) # Run inference inputs = processor(common_voice['speech'], sampling_rate=16_000, return_tensors='pt', padding=True).to(device) with torch.no_grad(): logits = model(**inputs).logits transcripts = processor.batch_decode(logits.cpu().numpy()).text ``` ## Training procedure Text data for the n-gram model is pre-processed by removing characters not part of the wav2vec 2.0 vocabulary and uppercasing all characters. After pre-processing and storing each text sample on a new line in a text file, a [KenLM](https://github.com/kpu/kenlm) model is estimated. See [this tutorial](https://huggingface.co/blog/wav2vec2-with-ngram) for more details. ## Evaluation results The model was evaluated on the full Common Voice test set version 6.1. VoxRex-C achieved a WER of 9.03% without the language model and 6.47% with the language model.

e8c141b6e2a3f64297ccf1ac8dbacab4

irena/whisper-small-sv-SE

irena

whisper

21

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hi']

['mozilla-foundation/common_voice_11_0']

null

0

['hf-asr-leaderboard', 'generated_from_trainer']

true

1,005

false

# Whisper Small sv-SE - irena This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

a991b6665cb435767fb82a6dcef68a51

anas-awadalla/bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10

anas-awadalla

bert

16

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,001

false

# bert-base-uncased-few-shot-k-32-finetuned-squad-seed-10 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3e-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 - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3

5416cd6d9215167053ee5a78bbf5d4e7

timm/maxxvitv2_rmlp_base_rw_224.sw_in12k

timm

null

4

64

timm

0

image-classification

true

false

apache-2.0

null

['imagenet-12k']

null

0

['image-classification', 'timm']

false

true

22,237

false

# Model card for maxxvitv2_rmlp_base_rw_224.sw_in12k A timm specific MaxxViT-V2 (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman. ### Model Variants in [maxxvit.py](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/maxxvit.py) MaxxViT covers a number of related model architectures that share a common structure including: - CoAtNet - Combining MBConv (depthwise-separable) convolutional blocks in early stages with self-attention transformer blocks in later stages. - MaxViT - Uniform blocks across all stages, each containing a MBConv (depthwise-separable) convolution block followed by two self-attention blocks with different partitioning schemes (window followed by grid). - CoAtNeXt - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in CoAtNet. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT - A timm specific arch that uses ConvNeXt blocks in place of MBConv blocks in MaxViT. All normalization layers are LayerNorm (no BatchNorm). - MaxxViT-V2 - A MaxxViT variation that removes the window block attention leaving only ConvNeXt blocks and grid attention w/ more width to compensate. Aside from the major variants listed above, there are more subtle changes from model to model. Any model name with the string `rw` are `timm` specific configs w/ modelling adjustments made to favour PyTorch eager use. These were created while training initial reproductions of the models so there are variations. All models with the string `tf` are models exactly matching Tensorflow based models by the original paper authors with weights ported to PyTorch. This covers a number of MaxViT models. The official CoAtNet models were never released. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 127.2 - GMACs: 24.2 - Activations (M): 62.8 - Image size: 224 x 224 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Dataset:** ImageNet-12k ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('maxxvitv2_rmlp_base_rw_224.sw_in12k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 128, 192, 192]) # torch.Size([1, 128, 96, 96]) # torch.Size([1, 256, 48, 48]) # torch.Size([1, 512, 24, 24]) # torch.Size([1, 1024, 12, 12]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'maxxvitv2_rmlp_base_rw_224.sw_in12k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled (ie.e a (batch_size, num_features, H, W) tensor output = model.forward_head(output, pre_logits=True) # output is (batch_size, num_features) tensor ``` ## Model Comparison ### By Top-1 |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| ### By Throughput (samples / sec) |model |top1 |top5 |samples / sec |Params (M) |GMAC |Act (M)| |------------------------------------------------------------------------------------------------------------------------|----:|----:|--------------:|--------------:|-----:|------:| |[coatnext_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnext_nano_rw_224.sw_in1k) |81.95|95.92| 2525.52| 14.70| 2.47| 12.80| |[coatnet_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_nano_rw_224.sw_in1k) |81.70|95.64| 2344.52| 15.14| 2.41| 15.41| |[coatnet_rmlp_nano_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_nano_rw_224.sw_in1k) |82.05|95.87| 2109.09| 15.15| 2.62| 20.34| |[coatnet_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_0_rw_224.sw_in1k) |82.39|95.84| 1831.21| 27.44| 4.43| 18.73| |[coatnet_bn_0_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_bn_0_rw_224.sw_in1k) |82.39|96.19| 1600.14| 27.44| 4.67| 22.04| |[maxvit_rmlp_pico_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_pico_rw_256.sw_in1k) |80.53|95.21| 1594.71| 7.52| 1.85| 24.86| |[maxxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_nano_rw_256.sw_in1k) |83.03|96.34| 1341.24| 16.78| 4.37| 26.05| |[maxvit_rmlp_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_nano_rw_256.sw_in1k) |82.96|96.26| 1283.24| 15.50| 4.47| 31.92| |[maxxvitv2_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxxvitv2_nano_rw_256.sw_in1k) |83.11|96.33| 1276.88| 23.70| 6.26| 23.05| |[maxvit_nano_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_nano_rw_256.sw_in1k) |82.93|96.23| 1218.17| 15.45| 4.46| 30.28| |[maxvit_tiny_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_tiny_rw_224.sw_in1k) |83.50|96.50| 1100.53| 29.06| 5.11| 33.11| |[coatnet_rmlp_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw_224.sw_in1k) |83.36|96.45| 1093.03| 41.69| 7.85| 35.47| |[coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k) |84.90|96.96| 1025.45| 41.72| 8.11| 40.13| |[maxvit_tiny_tf_224.in1k](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) |83.41|96.59| 1004.94| 30.92| 5.60| 35.78| |[coatnet_1_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_1_rw_224.sw_in1k) |83.62|96.38| 989.59| 41.72| 8.04| 34.60| |[maxvit_rmlp_tiny_rw_256.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_tiny_rw_256.sw_in1k) |84.23|96.78| 807.21| 29.15| 6.77| 46.92| |[maxvit_rmlp_small_rw_224.sw_in1k](https://huggingface.co/timm/maxvit_rmlp_small_rw_224.sw_in1k) |84.49|96.76| 693.82| 64.90| 10.75| 49.30| |[maxvit_small_tf_224.in1k](https://huggingface.co/timm/maxvit_small_tf_224.in1k) |84.43|96.83| 647.96| 68.93| 11.66| 53.17| |[coatnet_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_2_rw_224.sw_in12k_ft_in1k) |86.57|97.89| 631.88| 73.87| 15.09| 49.22| |[coatnet_rmlp_2_rw_224.sw_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in1k) |84.61|96.74| 625.81| 73.88| 15.18| 54.78| |[coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k) |86.49|97.90| 620.58| 73.88| 15.18| 54.78| |[maxxvit_rmlp_small_rw_256.sw_in1k](https://huggingface.co/timm/maxxvit_rmlp_small_rw_256.sw_in1k) |84.63|97.06| 575.53| 66.01| 14.67| 58.38| |[maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.64|98.02| 501.03| 116.09| 24.20| 62.77| |[maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k) |86.89|98.02| 375.86| 116.14| 23.15| 92.64| |[maxvit_base_tf_224.in1k](https://huggingface.co/timm/maxvit_base_tf_224.in1k) |84.85|96.99| 358.25| 119.47| 24.04| 95.01| |[maxvit_tiny_tf_384.in1k](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) |85.11|97.38| 293.46| 30.98| 17.53| 123.42| |[maxvit_large_tf_224.in1k](https://huggingface.co/timm/maxvit_large_tf_224.in1k) |84.93|96.97| 247.71| 211.79| 43.68| 127.35| |[maxvit_small_tf_384.in1k](https://huggingface.co/timm/maxvit_small_tf_384.in1k) |85.54|97.46| 188.35| 69.02| 35.87| 183.65| |[coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k) |87.39|98.31| 160.80| 73.88| 47.69| 209.43| |[maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.47|98.37| 149.49| 116.09| 72.98| 213.74| |[maxvit_tiny_tf_512.in1k](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) |85.67|97.58| 144.25| 31.05| 33.49| 257.59| |[maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k](https://huggingface.co/timm/maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k) |87.81|98.37| 106.55| 116.14| 70.97| 318.95| |[maxvit_base_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) |87.92|98.54| 104.71| 119.65| 73.80| 332.90| |[maxvit_base_tf_384.in1k](https://huggingface.co/timm/maxvit_base_tf_384.in1k) |86.29|97.80| 101.09| 119.65| 73.80| 332.90| |[maxvit_small_tf_512.in1k](https://huggingface.co/timm/maxvit_small_tf_512.in1k) |86.10|97.76| 88.63| 69.13| 67.26| 383.77| |[maxvit_large_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) |87.98|98.56| 71.75| 212.03|132.55| 445.84| |[maxvit_large_tf_384.in1k](https://huggingface.co/timm/maxvit_large_tf_384.in1k) |86.23|97.69| 70.56| 212.03|132.55| 445.84| |[maxvit_base_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) |88.20|98.53| 50.87| 119.88|138.02| 703.99| |[maxvit_base_tf_512.in1k](https://huggingface.co/timm/maxvit_base_tf_512.in1k) |86.60|97.92| 50.75| 119.88|138.02| 703.99| |[maxvit_xlarge_tf_384.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) |88.32|98.54| 42.53| 475.32|292.78| 668.76| |[maxvit_large_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) |88.04|98.40| 36.42| 212.33|244.75| 942.15| |[maxvit_large_tf_512.in1k](https://huggingface.co/timm/maxvit_large_tf_512.in1k) |86.52|97.88| 36.04| 212.33|244.75| 942.15| |[maxvit_xlarge_tf_512.in21k_ft_in1k](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) |88.53|98.64| 21.76| 475.77|534.14|1413.22| ## Citation ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/rwightman/pytorch-image-models}} } ``` ```bibtex @article{tu2022maxvit, title={MaxViT: Multi-Axis Vision Transformer}, author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao}, journal={ECCV}, year={2022}, } ``` ```bibtex @article{dai2021coatnet, title={CoAtNet: Marrying Convolution and Attention for All Data Sizes}, author={Dai, Zihang and Liu, Hanxiao and Le, Quoc V and Tan, Mingxing}, journal={arXiv preprint arXiv:2106.04803}, year={2021} } ```

660729bb96af7dec7358daafbf51e7d2

valurank/finetuned-distilbert-news-article-categorization

valurank

distilbert

8

524

transformers

0

text-classification

true

false

other

null

0

['generated_from_trainer']

true

1,163

false

### finetuned-distilbert-news-article-catgorization This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the news_article_categorization dataset. It achieves the following results on the evaluation set: - Loss: 0.1548 - F1_score(weighted): 0.96 ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data The model was trained on some subset of the news_article_categorization dataset and it was validated on the remaining subset of the data ### Training procedure More information needed ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-5 - train_batch_size: 3 - eval_batch_size: 3 - seed: 17 - optimizer: AdamW(lr=1e-5 and epsilon=1e-08) - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0 - num_epochs: 2 ### Training results | Training Loss | Epoch | Validation Loss | f1 score | |:-------------:|:-----:|:---------------: |:------:| | 0.6359 | 1.0 | 0.1739 | 0.9619 | | 0.1548 | 2.0 | 0.1898 | 0.9648 |

4efec8ebaf02b7e6fab82722fa71cf5f

kika2000/wav2vec2-large-xls-r-300m-kika5_my-colab

kika2000

wav2vec2

13

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

2,513

false

# wav2vec2-large-xls-r-300m-kika5_my-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: 0.3860 - Wer: 0.3505 ## 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: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.0007 | 4.82 | 400 | 0.6696 | 0.8283 | | 0.2774 | 9.64 | 800 | 0.4231 | 0.5476 | | 0.1182 | 14.46 | 1200 | 0.4253 | 0.5102 | | 0.0859 | 19.28 | 1600 | 0.4600 | 0.4866 | | 0.0693 | 24.1 | 2000 | 0.4030 | 0.4533 | | 0.0611 | 28.92 | 2400 | 0.4189 | 0.4412 | | 0.0541 | 33.73 | 2800 | 0.4272 | 0.4380 | | 0.0478 | 38.55 | 3200 | 0.4537 | 0.4505 | | 0.0428 | 43.37 | 3600 | 0.4349 | 0.4181 | | 0.038 | 48.19 | 4000 | 0.4562 | 0.4199 | | 0.0345 | 53.01 | 4400 | 0.4209 | 0.4310 | | 0.0316 | 57.83 | 4800 | 0.4336 | 0.4058 | | 0.0288 | 62.65 | 5200 | 0.4004 | 0.3920 | | 0.025 | 67.47 | 5600 | 0.4115 | 0.3857 | | 0.0225 | 72.29 | 6000 | 0.4296 | 0.3948 | | 0.0182 | 77.11 | 6400 | 0.3963 | 0.3772 | | 0.0165 | 81.93 | 6800 | 0.3921 | 0.3687 | | 0.0152 | 86.75 | 7200 | 0.3969 | 0.3592 | | 0.0133 | 91.57 | 7600 | 0.3803 | 0.3527 | | 0.0118 | 96.39 | 8000 | 0.3860 | 0.3505 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

73b50fce7acb6a58a4cec4b7cd5c238f

miangoar/esm2_t12_35M_UR50D-finetuned-secondary-structure-classification

miangoar

esm

7

1

transformers

0

token-classification

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,650

false

# esm2_t12_35M_UR50D-finetuned-secondary-structure-classification This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4076 - Train Masked Accuracy: 0.8342 - Validation Loss: 0.4714 - Validation Masked Accuracy: 0.8060 - Epoch: 2 ## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0} - training_precision: float32 ### Training results | Train Loss | Train Masked Accuracy | Validation Loss | Validation Masked Accuracy | Epoch | |:----------:|:---------------------:|:---------------:|:--------------------------:|:-----:| | 0.5874 | 0.7454 | 0.4908 | 0.7962 | 0 | | 0.4503 | 0.8156 | 0.4703 | 0.8043 | 1 | | 0.4076 | 0.8342 | 0.4714 | 0.8060 | 2 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

73f1e0199fe1deb2ba621021d978f960

theblackcat102/electra-large-webgpt-rm

theblackcat102

electra

10

3

transformers

2

text-classification

true

false

apache-2.0

['en']

['openai/webgpt_comparisons']

null

0

['webgpt', 'regression', 'reward-model']

false

true

1,274

false

# Reward Model pretrained on openai/webgpt_comparison Reward model finetuned from existing pretrain model. Things that aligned with the orignal papers * Overfits easily using rank loss * Small learning rate Different from the papers * Small model performs bad due to lack of world knowledge, since the validation accuracy doesn't even reach 60%. OpenAI RM had 6B parameters. * Train using a 80-20 train-validation split on torch AMP settings Other models I had tried * bloomz-560m : embedding size doesn't worth the training, since this dataset only contain english prompt * gpt2-large : not stable * gpt2-base : not stable # Performance on validation split | model | val acc | val loss (rank loss) | |---|---|---| | [roberta-base](https://huggingface.co/theblackcat102/roberta-base-webgpt-rm) | 56.21 | 0.71 | | [roberta-large](https://huggingface.co/theblackcat102/roberta-large-webgpt-rm) | 57.89 | 0.67 | | [electra-base](https://huggingface.co/theblackcat102/electra-base-webgpt-rm) | 57.02 | 0.70 | | [electra-large](https://huggingface.co/theblackcat102/electra-large-webgpt-rm) | 58.75 | 0.69 | Tensorboard logs are located under runs/ # Note: * You will have to reweight this model output such that the mean rewards equals to 0

82de84b1327cb88c7f9e708a34673b36

google/multiberts-seed_1-step_1500k

google

bert

8

12

transformers

0

null

true

false

apache-2.0

['en']

null

0

['multiberts', 'multiberts-seed_1', 'multiberts-seed_1-step_1500k']

false

true

3,527

false

# MultiBERTs, Intermediate Checkpoint - Seed 1, Step 1500k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #1, captured at step 1500k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1500k') model = TFBertModel.from_pretrained("google/multiberts-seed_1-step_1500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_1-step_1500k') model = BertModel.from_pretrained("google/multiberts-seed_1-step_1500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

dbcce8ee071c2181030fa458d3c8e266

muhtasham/small-mlm-glue-qnli-target-glue-rte

muhtasham

bert

10

4

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,441

false

# small-mlm-glue-qnli-target-glue-rte This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3314 - Accuracy: 0.6101 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4059 | 6.41 | 500 | 1.5081 | 0.6209 | | 0.0562 | 12.82 | 1000 | 2.5424 | 0.5921 | | 0.0258 | 19.23 | 1500 | 2.7425 | 0.6209 | | 0.0161 | 25.64 | 2000 | 3.3314 | 0.6101 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

96ca1d01f1203350b40b9ecbdd2a9b46

anas-awadalla/bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42

anas-awadalla

bert

18

7

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,055

false

# bert-base-uncased-few-shot-k-16-finetuned-squad-seed-42 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results {'exact_match': 3.207190160832545, 'f1': 6.680463956037787} ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

f77de9bcdf912ca0e9cadaef1199c2c7

MayaGalvez/bert-base-multilingual-cased-finetuned-multilingual_pos

MayaGalvez

bert

10

11

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,510

false

# bert-base-multilingual-cased-finetuned-multilingual-pos This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1999 - Precision: 0.9438 - Recall: 0.9438 - F1: 0.9438 - Accuracy: 0.9541 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.0385 | 0.29 | 100 | 0.4411 | 0.8523 | 0.8473 | 0.8498 | 0.8739 | | 0.3849 | 0.57 | 200 | 0.3275 | 0.8907 | 0.8913 | 0.8910 | 0.9103 | | 0.2976 | 0.86 | 300 | 0.2879 | 0.9034 | 0.9037 | 0.9036 | 0.9203 | | 0.2487 | 1.14 | 400 | 0.2599 | 0.9132 | 0.9115 | 0.9123 | 0.9285 | | 0.2027 | 1.43 | 500 | 0.2444 | 0.9224 | 0.9198 | 0.9211 | 0.9349 | | 0.1899 | 1.71 | 600 | 0.2287 | 0.9239 | 0.9246 | 0.9243 | 0.9378 | | 0.18 | 2.0 | 700 | 0.2184 | 0.9282 | 0.9297 | 0.9289 | 0.9418 | | 0.1351 | 2.29 | 800 | 0.2214 | 0.9297 | 0.9291 | 0.9294 | 0.9424 | | 0.134 | 2.57 | 900 | 0.2123 | 0.9337 | 0.9333 | 0.9335 | 0.9458 | | 0.1294 | 2.86 | 1000 | 0.1993 | 0.9359 | 0.9344 | 0.9352 | 0.9476 | | 0.1156 | 3.14 | 1100 | 0.2018 | 0.9377 | 0.9377 | 0.9377 | 0.9494 | | 0.1007 | 3.43 | 1200 | 0.2027 | 0.9375 | 0.9384 | 0.9380 | 0.9495 | | 0.0959 | 3.71 | 1300 | 0.1971 | 0.9387 | 0.9394 | 0.9390 | 0.9505 | | 0.0982 | 4.0 | 1400 | 0.1953 | 0.9408 | 0.9414 | 0.9411 | 0.9522 | | 0.0761 | 4.29 | 1500 | 0.1987 | 0.9404 | 0.9412 | 0.9408 | 0.9517 | | 0.0788 | 4.57 | 1600 | 0.1994 | 0.9405 | 0.9411 | 0.9408 | 0.9518 | | 0.0755 | 4.86 | 1700 | 0.2009 | 0.9413 | 0.9420 | 0.9417 | 0.9525 | | 0.0671 | 5.14 | 1800 | 0.2011 | 0.9421 | 0.9423 | 0.9422 | 0.9527 | | 0.0636 | 5.43 | 1900 | 0.2002 | 0.9428 | 0.9431 | 0.9430 | 0.9532 | | 0.0628 | 5.71 | 2000 | 0.1993 | 0.9422 | 0.9433 | 0.9428 | 0.9532 | | 0.0645 | 6.0 | 2100 | 0.1979 | 0.9434 | 0.9430 | 0.9432 | 0.9536 | | 0.0543 | 6.29 | 2200 | 0.2017 | 0.9427 | 0.9434 | 0.9430 | 0.9532 | | 0.0558 | 6.57 | 2300 | 0.1992 | 0.9427 | 0.9432 | 0.9430 | 0.9534 | | 0.0529 | 6.86 | 2400 | 0.1999 | 0.9438 | 0.9438 | 0.9438 | 0.9541 | ### Framework versions - Transformers 4.21.0 - Pytorch 1.12.0+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

4826183159e09ea8157460f6a38bfa91

tillschwoerer/distilbert-base-uncased-finetuned-tagesschau-subcategories

tillschwoerer

distilbert

21

5

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,743

false

# distilbert-base-uncased-finetuned-tagesschau-subcategories 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: 0.7723 - Accuracy: 0.7267 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.4 | 30 | 1.3433 | 0.5667 | | No log | 0.8 | 60 | 1.0861 | 0.6933 | | No log | 1.2 | 90 | 0.9395 | 0.7067 | | No log | 1.6 | 120 | 0.8647 | 0.68 | | No log | 2.0 | 150 | 0.8018 | 0.72 | | No log | 2.4 | 180 | 0.7723 | 0.7267 | | No log | 2.8 | 210 | 0.7616 | 0.72 | | No log | 3.2 | 240 | 0.7348 | 0.7067 | | No log | 3.6 | 270 | 0.7747 | 0.72 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

7b6203bb795313d67299a451a9d0d574

Gladiator/distilbert-base-uncased_swag_mqa

Gladiator

distilbert

12

0

transformers

0

multiple-choice

true

false

apache-2.0

null

['swag']

null

0

['generated_from_trainer']

true

1,254

false

# distilbert-base-uncased_swag_mqa This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the swag dataset. It achieves the following results on the evaluation set: - Loss: 0.8556 - Accuracy: 0.6494 ## 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 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9234 | 1.0 | 2000 | 0.8556 | 0.6494 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

059819cb0140c0c3a911c03d957148f5

fpuentes/gpt2-galician

fpuentes

gpt2

9

3

transformers

0

text-generation

true

false

apache-2.0

['gl']

null

0

[]

false

true

888

false

Modelo de 125M de parámetros, adestrado e afinado desde un modelo preentrenado (GPT2-Spanish), usando un dataset en galego de 387MB obtido da wikipedia en galego. No contexto da **[Resolución do 22 de decembro de 2021 da Secretaría Xeral de Educación e Formación Profesional pola que se convocan premios para o desenvolvemento de proxectos de innovación tecnolóxica ou científica e proxectos de innovación didáctica no ámbito da formación profesional en centros públicos dependentes da Consellería de Cultura, Educación e Universidade](http://www.edu.xunta.gal/fp/sites/fp/files/pi2022__resolucion_de_convocatoria.pdf)**, baixo o nome de "*Creación dun modelo de linguaxe adestrado previamente mediante técnicas de autoatención para explorar arquitecturas que permitan o seu uso en solucións de procesamento da linguaxe natural en galego tanto na docencia como na contorna empresarial*"

6ebaddbee7b681dff2c5e995f2b31745

Tirendaz/sentiment-model-on-imdb-dataset

Tirendaz

distilbert

19

6

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,043

false

# sentiment-model-on-imdb-dataset This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3694 - Accuracy: 0.85 - F1: 0.8544 ## 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: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

8d25b34e1843191cbf333e326e58dcd2

Haakf/allsides_right_text_headline_padded_overfit

Haakf

distilbert

8

4

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

2,338

false

# Haakf/allsides_right_text_headline_padded_overfit This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.8995 - Validation Loss: 1.7970 - Epoch: 19 ## 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': -797, '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: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.9722 | 1.8914 | 0 | | 1.9552 | 1.8628 | 1 | | 1.9303 | 1.8589 | 2 | | 1.9311 | 1.8490 | 3 | | 1.9168 | 1.8710 | 4 | | 1.8825 | 1.8630 | 5 | | 1.8841 | 1.8935 | 6 | | 1.8924 | 1.8301 | 7 | | 1.8940 | 1.8391 | 8 | | 1.9021 | 1.8450 | 9 | | 1.8821 | 1.8698 | 10 | | 1.8958 | 1.8886 | 11 | | 1.8891 | 1.8550 | 12 | | 1.8849 | 1.8777 | 13 | | 1.8809 | 1.8690 | 14 | | 1.8859 | 1.8723 | 15 | | 1.8932 | 1.8602 | 16 | | 1.9025 | 1.8583 | 17 | | 1.8853 | 1.7923 | 18 | | 1.8995 | 1.7970 | 19 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

1c4245bb79bca8e84f0eda93c96885e3

milyiyo/paraphraser-german-mt5-small

milyiyo

mt5

14

58

transformers

0

text2text-generation

true

false

apache-2.0

['de']

['paws-x', 'tapaco']

null

0

['generated_from_trainer']

true

2,079

false

# paraphraser-german-mt5-small This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the paws-x (de) and tapaco (de) dataset. It achieves the following results on the evaluation set: - Loss: 1.7678 - Perplexity: 5.86 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 3.7064 | 0.05 | 2000 | 2.0731 | | 2.8673 | 0.11 | 4000 | 2.0420 | | 2.6133 | 0.16 | 6000 | 2.0080 | | 2.4563 | 0.21 | 8000 | 1.9556 | | 2.385 | 0.27 | 10000 | 1.9090 | | 2.3122 | 0.32 | 12000 | 1.9127 | | 2.2775 | 0.38 | 14000 | 1.8658 | | 2.2323 | 0.43 | 16000 | 1.8407 | | 2.17 | 0.48 | 18000 | 1.8342 | | 2.1672 | 0.54 | 20000 | 1.8328 | | 2.1488 | 0.59 | 22000 | 1.8071 | | 2.1026 | 0.64 | 24000 | 1.8328 | | 2.1036 | 0.7 | 26000 | 1.7979 | | 2.0854 | 0.75 | 28000 | 1.7895 | | 2.0594 | 0.81 | 30000 | 1.7944 | | 2.0793 | 0.86 | 32000 | 1.7726 | | 2.0661 | 0.91 | 34000 | 1.7762 | | 2.0722 | 0.97 | 36000 | 1.7714 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

2068239718ac0014097bc521a74a4e47

pooyaphoenix/distilbert-base-uncased-finetuned-cola

pooyaphoenix

distilbert

13

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

1

0

['generated_from_trainer']

true

1,571

false

# distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7904 - Matthews Correlation: 0.5227 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.528 | 1.0 | 535 | 0.5180 | 0.4003 | | 0.3508 | 2.0 | 1070 | 0.5120 | 0.5019 | | 0.2409 | 3.0 | 1605 | 0.6374 | 0.5128 | | 0.1806 | 4.0 | 2140 | 0.7904 | 0.5227 | | 0.1311 | 5.0 | 2675 | 0.8824 | 0.5227 | ### Framework versions - Transformers 4.12.2 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

46e7217a07a70ba879055cedd2927df8

ali2066/finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32

ali2066

distilbert

13

6

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,615

false

# finetuned_sentence_itr3_2e-05_all_27_02_2022-17_44_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Accuracy: 0.8263 - F1: 0.8865 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 | | No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 | | 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 | | 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 | | 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

325725c826e0273c119e51fc8dcfb139

Seznam/small-e-czech

Seznam

electra

9

4,903

transformers

6

null

true

false

cc-by-4.0

['cs']

null

0

[]

false

true

2,117

false

# Small-E-Czech Small-E-Czech is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a Czech web corpus created at [Seznam.cz](https://www.seznam.cz/) and introduced in an [IAAI 2022 paper](https://arxiv.org/abs/2112.01810). Like other pretrained models, it should be finetuned on a downstream task of interest before use. At Seznam.cz, it has helped improve [web search ranking](https://blog.seznam.cz/2021/02/vyhledavani-pomoci-vyznamovych-vektoru/), query typo correction or clickbait titles detection. We release it under [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/) (i.e. allowing commercial use). To raise an issue, please visit our [github](https://github.com/seznam/small-e-czech). ### How to use the discriminator in transformers ```python from transformers import ElectraForPreTraining, ElectraTokenizerFast import torch discriminator = ElectraForPreTraining.from_pretrained("Seznam/small-e-czech") tokenizer = ElectraTokenizerFast.from_pretrained("Seznam/small-e-czech") sentence = "Za hory, za doly, mé zlaté parohy" fake_sentence = "Za hory, za doly, kočka zlaté parohy" fake_sentence_tokens = ["[CLS]"] + tokenizer.tokenize(fake_sentence) + ["[SEP]"] fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt") outputs = discriminator(fake_inputs) predictions = torch.nn.Sigmoid()(outputs[0]).cpu().detach().numpy() for token in fake_sentence_tokens: print("{:>7s}".format(token), end="") print() for prediction in predictions.squeeze(): print("{:7.1f}".format(prediction), end="") print() ``` In the output we can see the probabilities of particular tokens not belonging in the sentence (i.e. having been faked by the generator) according to the discriminator: ``` [CLS] za hory , za dol ##y , kočka zlaté paro ##hy [SEP] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.3 0.2 0.1 0.0 ``` ### Finetuning For instructions on how to finetune the model on a new task, see the official HuggingFace transformers [tutorial](https://huggingface.co/transformers/training.html).

8145a4b7721103ce9c96a7f22399b5b0

abkbvknv/bert-finetuned-ner

abkbvknv

bert

12

11

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

0

['generated_from_trainer']

true

893

false

# bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. ## 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 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

2133aa02c3f70e9cd147a315438e7d1e

fathyshalab/all-roberta-large-v1-banking-5-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,512

false

# all-roberta-large-v1-banking-5-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 ## 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: 48 - eval_batch_size: 48 - 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7211 | 1.0 | 1 | 2.5748 | 0.2301 | | 2.2722 | 2.0 | 2 | 2.4566 | 0.3009 | | 1.9185 | 3.0 | 3 | 2.3596 | 0.3805 | | 1.667 | 4.0 | 4 | 2.2920 | 0.3982 | | 1.4704 | 5.0 | 5 | 2.2565 | 0.3982 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

f3aa4580bcdfccdf51924d6ed1203e95

aioxlabs/dvoice-swahili

aioxlabs

wav2vec2

8

16

speechbrain

0

automatic-speech-recognition

true

false

apache-2.0

['sw']

['commonvoice']

null

0

['CTC', 'pytorch', 'speechbrain', 'Transformer']

false

true

5,832

false

5c8951ad65175ee81b9dee63de4071fd

malmarz/whisper_small_s5k_b64_nofreeze_mgb2cv11

malmarz

whisper

61

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,528

false

# openai/whisper-small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4429 - Wer: 52.7568 ## 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: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3629 | 1.03 | 1000 | 0.4917 | 53.1291 | | 0.289 | 2.06 | 2000 | 0.4747 | 61.3855 | | 0.2996 | 3.08 | 3000 | 0.4542 | 55.4692 | | 0.2331 | 4.11 | 4000 | 0.4353 | 51.4917 | | 0.1566 | 5.14 | 5000 | 0.4429 | 52.7568 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

e0de70fbbad9a68b91936cb3a610eab8

luigisaetta/whisper-small-it

luigisaetta

whisper

22

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['it']

['mozilla-foundation/common_voice_11_0']

null

0

['generated_from_trainer', 'whisper-event']

true

1,714

false

# whisper-small-it This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1919 - Wer: 11.72 ## Model description More information needed ## Intended uses & limitations I have left this model here. BUt the "small3-it", produced later, has better performance. ## 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: 32 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - 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: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1441 | 1.68 | 1000 | 0.1912 | 0.1256 | | 0.0653 | 3.36 | 2000 | 0.1845 | 0.1182 | | 0.0374 | 5.03 | 3000 | 0.1919 | 0.1172 | | 0.0238 | 6.71 | 4000 | 0.2069 | 0.1202 | | 0.0162 | 8.39 | 5000 | 0.2184 | 0.1223 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

d89545f5d2aa1688abcc63e688b74685

google/t5-efficient-small-dl12

google

t5

12

9

transformers

0

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,254

false

# T5-Efficient-SMALL-DL12 (Deep-Narrow version) T5-Efficient-SMALL-DL12 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and was released with the paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** by *Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler*. In a nutshell, the paper indicates that a **Deep-Narrow** model architecture is favorable for **downstream** performance compared to other model architectures of similar parameter count. To quote the paper: > We generally recommend a DeepNarrow strategy where the model’s depth is preferentially increased > before considering any other forms of uniform scaling across other dimensions. This is largely due to > how much depth influences the Pareto-frontier as shown in earlier sections of the paper. Specifically, a > tall small (deep and narrow) model is generally more efficient compared to the base model. Likewise, > a tall base model might also generally more efficient compared to a large model. We generally find > that, regardless of size, even if absolute performance might increase as we continue to stack layers, > the relative gain of Pareto-efficiency diminishes as we increase the layers, converging at 32 to 36 > layers. Finally, we note that our notion of efficiency here relates to any one compute dimension, i.e., > params, FLOPs or throughput (speed). We report all three key efficiency metrics (number of params, > FLOPS and speed) and leave this decision to the practitioner to decide which compute dimension to > consider. To be more precise, *model depth* is defined as the number of transformer blocks that are stacked sequentially. A sequence of word embeddings is therefore processed sequentially by each transformer block. ## Details model architecture This model checkpoint - **t5-efficient-small-dl12** - is of model type **Small** with the following variations: - **dl** is **12** It has **85.7** million parameters and thus requires *ca.* **342.82 MB** of memory in full precision (*fp32*) or **171.41 MB** of memory in half precision (*fp16* or *bf16*). A summary of the *original* T5 model architectures can be seen here: | Model | nl (el/dl) | ff | dm | kv | nh | #Params| | ----| ---- | ---- | ---- | ---- | ---- | ----| | Tiny | 4/4 | 1024 | 256 | 32 | 4 | 16M| | Mini | 4/4 | 1536 | 384 | 32 | 8 | 31M| | Small | 6/6 | 2048 | 512 | 32 | 8 | 60M| | Base | 12/12 | 3072 | 768 | 64 | 12 | 220M| | Large | 24/24 | 4096 | 1024 | 64 | 16 | 738M| | Xl | 24/24 | 16384 | 1024 | 128 | 32 | 3B| | XXl | 24/24 | 65536 | 1024 | 128 | 128 | 11B| whereas the following abbreviations are used: | Abbreviation | Definition | | ----| ---- | | nl | Number of transformer blocks (depth) | | dm | Dimension of embedding vector (output vector of transformers block) | | kv | Dimension of key/value projection matrix | | nh | Number of attention heads | | ff | Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) | | el | Number of transformer blocks in the encoder (encoder depth) | | dl | Number of transformer blocks in the decoder (decoder depth) | | sh | Signifies that attention heads are shared | | skv | Signifies that key-values projection matrices are tied | If a model checkpoint has no specific, *el* or *dl* than both the number of encoder- and decoder layers correspond to *nl*. ## Pre-Training The checkpoint was pretrained on the [Colossal, Cleaned version of Common Crawl (C4)](https://huggingface.co/datasets/c4) for 524288 steps using the span-based masked language modeling (MLM) objective. ## Fine-Tuning **Note**: This model is a **pretrained** checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks. You can follow on of the following examples on how to fine-tune the model: *PyTorch*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) - [Question Answering](https://github.com/huggingface/transformers/blob/master/examples/pytorch/question-answering/run_seq2seq_qa.py) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *Tensorflow*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. *JAX/Flax*: - [Summarization](https://github.com/huggingface/transformers/tree/master/examples/flax/summarization) - [Text Classification](https://github.com/huggingface/transformers/tree/master/examples/flax/text-classification) - *Note*: You will have to slightly adapt the training example here to make it work with an encoder-decoder model. ## Downstream Performance TODO: Add table if available ## Computational Complexity TODO: Add table if available ## More information We strongly recommend the reader to go carefully through the original paper **[Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers](https://arxiv.org/abs/2109.10686)** to get a more nuanced understanding of this model checkpoint. As explained in the following [issue](https://github.com/google-research/google-research/issues/986#issuecomment-1035051145), checkpoints including the *sh* or *skv* model architecture variations have *not* been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept [here](https://huggingface.co/NewT5SharedHeadsSharedKeyValues) as they might be ported potentially in the future.

b5c80e88ee2399fb6fe00346070fad06

alxdfy/noggles-v21-5900

alxdfy

null

16

3

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

['text-to-image', 'stable-diffusion']

false

true

767

false

### noggles_v21_5900 Dreambooth model trained by alxdfy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/alxdfy/noggles-v21-5900/resolve/main/sample_images/00267-3860859300-portrait_of_vitalik_buterin_wearing_noggles.png)

a3737015e8d0100fdeca8744955a0794

nc33/finetune_deberta_small_model

nc33

deberta-v2

15

3

transformers

0

text-classification

true

false

mit

null

['super_glue']

null

0

['generated_from_trainer']

true

1,367

false

# finetune_deberta_small_model This model is a fine-tuned version of [nc33/finetune_deberta_small_model](https://huggingface.co/nc33/finetune_deberta_small_model) on the super_glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6788 - Accuracy: 0.8021 ## 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 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3666 | 1.0 | 590 | 0.5625 | 0.8003 | | 0.2501 | 2.0 | 1180 | 0.6762 | 0.7976 | | 0.2343 | 3.0 | 1770 | 0.6788 | 0.8021 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

99717ae010bc9e03cb4a459ca8ec44a6

marcop/musika_techno

marcop

null

8

0

keras

1

null

false

mit

null

0

['audio', 'music', 'generation', 'tensorflow']

false

true

1,089

false

# Musika Techno Model Pretrained Techno GAN model for the [Musika system](https://github.com/marcoppasini/musika) for fast infinite waveform music generation. Introduced in [this paper](https://arxiv.org/abs/2208.08706). ## Model description This pretrained GAN system consists of a ResNet-style generator and discriminator. During training, stability is controlled by adapting the strength of gradient penalty regularization on-the-fly. The gradient penalty weighting term is contained in *switch.npy*. The generator is conditioned on a latent coordinate system to produce samples of arbitrary length. The latent representations produced by the generator are then passed to a decoder which converts them into waveform audio. The generator has a context window of about 12 seconds of audio. ### How to use This pretrained Techno GAN system is automatically downloaded at the first execution of the system. Try Musika [here](https://github.com/marcoppasini/musika)! ## Training data The Techno GAN system was trained on 1000 hours of music with the *techno* tag from *jamendo.com*.

e1d88b71620ac2b7f027e86ed4350195

pinot/wav2vec2-large-xls-r-300m-ja-colab

pinot

wav2vec2

23

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_10_0']

null

0

['generated_from_trainer']

true

1,910

false

# wav2vec2-large-xls-r-300m-ja-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_10_0 dataset. It achieves the following results on the evaluation set: - Loss: 1.1407 - Wer: 0.2456 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 637 | 5.3238 | 0.9663 | | No log | 2.0 | 1274 | 4.1785 | 0.7662 | | No log | 3.0 | 1911 | 2.3701 | 0.4983 | | No log | 4.0 | 2548 | 1.8443 | 0.4090 | | 6.5781 | 5.0 | 3185 | 1.4892 | 0.3363 | | 6.5781 | 6.0 | 3822 | 1.3229 | 0.2995 | | 6.5781 | 7.0 | 4459 | 1.2418 | 0.2814 | | 6.5781 | 8.0 | 5096 | 1.1928 | 0.2647 | | 1.0184 | 9.0 | 5733 | 1.1584 | 0.2520 | | 1.0184 | 10.0 | 6370 | 1.1407 | 0.2456 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.10.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

53ad0726b0eba90d7c59b488149907a8

ScandinavianMrT/gpt2_prefinetune_SARC_1epoch_withcontext

ScandinavianMrT

gpt2

11

5

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,140

false

# gpt2_prefinetune_SARC_1epoch_withcontext This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7899 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.8788 | 1.0 | 14028 | 3.7899 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

273bdcfc2b5c263a6a489844d0b38e78

sd-dreambooth-library/spacecat

sd-dreambooth-library

null

28

9

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,860

false

### spacecat on Stable Diffusion via Dreambooth #### model by Unev3n This your the Stable Diffusion model fine-tuned the spacecat concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks spacecat** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/7.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/2.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/6.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/8.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/0.jpeg) ![image 5](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/1.jpeg) ![image 6](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/3.jpeg) ![image 7](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/4.jpeg) ![image 8](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/9.jpeg) ![image 9](https://huggingface.co/sd-dreambooth-library/spacecat/resolve/main/concept_images/5.jpeg)

c55858028a3a27802d653695ae3bd60d

NYTK/translation-bart-en-hu

NYTK

bart

9

36

transformers

0

translation

true

false

apache-2.0

['en', 'hu']

null

0

['translation']

false

true

1,160

false

# BART Translation model For further models, scripts and details, see [our repository](https://github.com/nytud/machine-translation) or [our demo site](https://juniper.nytud.hu/demo/nlp). - Source language: English - Target language: Hungarian - Pretrained on English WikiText-103 and Hungarian Wikipedia - Finetuned on subcorpora from OPUS - Segments: 56.837.602 ## Limitations - tokenized input text (tokenizer: [HuSpaCy](https://huggingface.co/huspacy)) ## Results | Model | BLEU | chrF-3 | | ------------- | ------------- | ------------- | | Google en-hu | 25.30 | 54.08 | | **BART-base-enhu** | **34.38** | **58.88** | | Google hu-en| 34.48 | 59.59 | | **BART-base-huen** | **38.03** | **61,37** | ## Citation If you use this model, please cite the following paper: ``` @inproceedings {yang-bart, title = {{BARTerezzünk! Messze, messze, messze a világtól, - BART kísérleti modellek magyar nyelvre}}, booktitle = {XVIII. Magyar Számítógépes Nyelvészeti Konferencia}, year = {2022}, publisher = {Szegedi Tudományegyetem, Informatikai Intézet}, address = {Szeged, Magyarország}, author = {Yang, Zijian Győző}, pages = {15--29} } ```

459e23f448b49b2f3204512d67860df9

Helsinki-NLP/opus-mt-tr-es

Helsinki-NLP

marian

10

111

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-tr-es * source languages: tr * target languages: es * OPUS readme: [tr-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tr-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/tr-es/opus-2020-01-26.zip) * test set translations: [opus-2020-01-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tr-es/opus-2020-01-26.test.txt) * test set scores: [opus-2020-01-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tr-es/opus-2020-01-26.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.tr.es | 56.3 | 0.722 |

0819fbc5fa91665d251d099b3543ca22

alikanakar/wav2vec2-large-xls-r-300m-turkish-colab-2

alikanakar

wav2vec2

13

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,553

false

# wav2vec2-large-xls-r-300m-turkish-colab-2 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: 0.4152 - Wer: 0.3686 ## 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: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9382 | 7.4 | 400 | 0.6296 | 0.7016 | | 0.2837 | 14.81 | 800 | 0.4440 | 0.5161 | | 0.1185 | 22.22 | 1200 | 0.4217 | 0.4007 | | 0.0701 | 29.62 | 1600 | 0.4152 | 0.3686 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

943c38409683f3169c7eb19e69fcd017

nateraw/huggingpics-package-demo-2

nateraw

vit

12

13

transformers

0

image-classification

true

false

apache-2.0

null

0

['image-classification', 'huggingpics', 'generated_from_trainer']

false

true

1,429

false

# huggingpics-package-demo-2 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3761 - Acc: 0.9403 ## 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: 4.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Acc | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0328 | 1.0 | 24 | 0.9442 | 0.7463 | | 0.8742 | 2.0 | 48 | 0.7099 | 0.9403 | | 0.6451 | 3.0 | 72 | 0.5050 | 0.9403 | | 0.508 | 4.0 | 96 | 0.3761 | 0.9403 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Tokenizers 0.10.3

df2c043ccfdc7e27258341761cab6e19

HPL/roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-210000sample

HPL

roberta

11

314

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,101

false

# roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-210000sample This model is a fine-tuned version of [HPL/roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-150000sample](https://huggingface.co/HPL/roberta-large-unlabeled-labeled-gab-reddit-task-semeval2023-t10-150000sample) on the None 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.13.0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.10.3

db307b5656793a6b1dfe70e516e1332c

HIZ/aichan_pick

HIZ

null

5

0

null

6

null

false

creativeml-openrail-m

null

0

[]

false

true

845

false

Stable diffusion models removed EMA version from the ai image channel and elsewhere. Feel free to download. **Treebark** This model was made by 나무껍질맛 in arcalive AI channel. 1. Anything V.3, add-difference 0.2 (animefull prevgood - animesfw prevgood) 2. Add-difference 1 (Gape60 - Animefull) 3. U-net merge with BasilMix (1,0.9,0.7,0.5,0.3,0.1,1,1,1,1,1,1,0,0,0,0,0,0,0,0.1,0.3,0.5,0.7,0.9,1_base alpha 0) 4. Add-difference 0.2 (SXD v1.0 - SD1.5) Treebark is more **gape** style than other 2.5D style models. **AniDosmix** This model was made by DiaryOfSta in arcalive AI channel. And the original model is uploaded on https://civitai.com/models/6437/anidosmix I permitted uploading this pruned fp16 version. AniDosmix is a 2.5D style model and balanced for making people and **background**.

e40f4d0dd3a4aa8490ddc07b7ed9979f

DOOGLAK/Article_500v1_NER_Model_3Epochs_UNAUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['article500v1_wikigold_split']

null

0

['generated_from_trainer']

true

1,561

false

# Article_500v1_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2058 - Precision: 0.6615 - Recall: 0.6746 - F1: 0.6680 - Accuracy: 0.9326 ## 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 | 58 | 0.3029 | 0.3539 | 0.3790 | 0.3660 | 0.8967 | | No log | 2.0 | 116 | 0.2191 | 0.6223 | 0.6488 | 0.6353 | 0.9262 | | No log | 3.0 | 174 | 0.2058 | 0.6615 | 0.6746 | 0.6680 | 0.9326 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

216d020f08d3f36e8ec06d4c994ddade

ali2066/finetuned_token_2e-05_all_16_02_2022-15_50_54

ali2066

distilbert

13

10

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,791

false

# finetuned_token_2e-05_all_16_02_2022-15_50_54 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1750 - Precision: 0.3286 - Recall: 0.3334 - F1: 0.3310 - Accuracy: 0.9447 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 38 | 0.3355 | 0.0975 | 0.2358 | 0.1380 | 0.8361 | | No log | 2.0 | 76 | 0.3177 | 0.1359 | 0.2709 | 0.1810 | 0.8398 | | No log | 3.0 | 114 | 0.3000 | 0.1542 | 0.3043 | 0.2047 | 0.8471 | | No log | 4.0 | 152 | 0.3033 | 0.1589 | 0.3060 | 0.2091 | 0.8434 | | No log | 5.0 | 190 | 0.3029 | 0.1629 | 0.3110 | 0.2138 | 0.8447 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

0cb4c3d371ea45d673cf514a6639c1dc