davanstrien/model_cards_with_readmes · Datasets at Hugging Face

sd-concepts-library/senneca

sd-concepts-library

null

10

0

null

0

null

false

mit

null

0

[]

false

true

1,102

false

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

1a0e4d718f7e1f20291679c29f629aeb

DFrostKilla/teamcomo-kj

DFrostKilla

null

18

2

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

617

false

### teamcomo-kj Dreambooth model trained by DFrostKilla 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:

f741ac3f6342231dd3b629998e1799f3

krinal214/bert-all-squad_que_translated

krinal214

bert

18

5

transformers

0

question-answering

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,172

false

# bert-all-squad_que_translated 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: 0.5174 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0746 | 1.0 | 18011 | 0.5174 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1 - Datasets 2.1.0 - Tokenizers 0.11.6

beda8abebb504b751e6d312094eee283

SzegedAI/hubertusz-small-wiki-seq128

SzegedAI

bert

9

2

transformers

0

null

true

false

apache-2.0

['hu']

['wikipedia']

null

0

['generated_from_keras_callback', 'hubert']

true

690

false

# hubert-small-wiki-seq128 Fully trained model with the second phase of training is available here: [SzegedAI/hubert-small-wiki](https://huggingface.co/SzegedAI/hubert-small-wiki) This model was trained from scratch on the Wikipedia subset of Hungarian Webcorpus 2.0 with MLM and SOP tasks. ### Pre-Training Parameters: - Training steps: 500.000 - Sequence length: 128 (the model is capable for 512) - Batch size: 1024 ### Framework versions - Transformers 4.21.3 - TensorFlow 2.10.0 - Datasets 2.4.0 - Tokenizers 0.12.1 # Acknowledgement [![Artificial Intelligence - National Laboratory - Hungary](https://milab.tk.hu/uploads/images/milab_logo_en.png)](https://mi.nemzetilabor.hu/)

22d9f9657209e681c5aa2f45400af376

jungjongho/wav2vec2-xlsr-korean-speech-emotion-recognition3

jungjongho

wav2vec2

13

1

transformers

0

null

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,170

false

# wav2vec2-xlsr-korean-speech-emotion-recognition3 This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15](https://huggingface.co/jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0600 - Accuracy: 0.9876 ## 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: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6472 | 0.08 | 1500 | 0.3769 | 0.8705 | | 0.3873 | 0.15 | 3000 | 0.3814 | 0.9127 | | 0.3002 | 0.23 | 4500 | 0.2617 | 0.9429 | | 0.2399 | 0.3 | 6000 | 0.1336 | 0.9693 | | 0.2181 | 0.38 | 7500 | 0.1360 | 0.9728 | | 0.1992 | 0.46 | 9000 | 0.1239 | 0.9717 | | 0.1556 | 0.53 | 10500 | 0.1053 | 0.9781 | | 0.1412 | 0.61 | 12000 | 0.0915 | 0.9810 | | 0.1396 | 0.69 | 13500 | 0.0777 | 0.9826 | | 0.1159 | 0.76 | 15000 | 0.0801 | 0.9831 | | 0.1156 | 0.84 | 16500 | 0.0667 | 0.9867 | | 0.1149 | 0.91 | 18000 | 0.0670 | 0.9860 | | 0.0929 | 0.99 | 19500 | 0.0600 | 0.9876 | ### Framework versions - Transformers 4.22.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.4.1.dev0 - Tokenizers 0.12.1

f1eb804cf36e7bba82f129cd3f64eaba

Suniljl/xridl

Suniljl

null

18

20

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

414

false

### xridl Dreambooth model trained by Suniljl with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

918d4d9f35cb90fc3876ebff8b8ccc2f

RaphaelKalandadze/wav2vec2-large-xls-r-300m-georgian-large

RaphaelKalandadze

wav2vec2

19

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice_10_0']

null

0

['generated_from_trainer']

true

1,496

false

# wav2vec2-large-xls-r-300m-georgian-large 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: 0.4291 - Wer: 0.6392 ## 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: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0867 | 4.21 | 400 | 3.1211 | 1.0 | | 2.8871 | 8.42 | 800 | 2.2250 | 1.0 | | 0.3667 | 12.63 | 1200 | 0.4291 | 0.6392 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 2.4.0 - Tokenizers 0.10.3

eb0b88d9e2c547f65778bde32b716646

MultiBertGunjanPatrick/multiberts-seed-4-160k

MultiBertGunjanPatrick

bert

7

4

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-4']

false

true

6,483

false

# MultiBERTs Seed 4 Checkpoint 160k (uncased) Seed 4 intermediate checkpoint 160k 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-4](https://hf.co/multberts-seed-4). 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-4-160k') model = BertModel.from_pretrained("multiberts-seed-4-160k") 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>

b33273440229a39b583b7de525bf8ce9

JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k

JorisCos

null

3

asteroid

0

audio-to-audio

true

false

cc-by-sa-4.0

null

['Libri3Mix', 'sep_noisy']

null

0

['asteroid', 'audio', 'ConvTasNet', 'audio-to-audio']

false

true

1,646

false

## Asteroid model `JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `sep_noisy` task of the Libri3Mix dataset. Training config: ```yml data: n_src: 3 sample_rate: 8000 segment: 3 task: sep_noisy train_dir: data/wav8k/min/train-360 valid_dir: data/wav8k/min/dev filterbank: kernel_size: 16 n_filters: 512 stride: 8 masknet: bn_chan: 128 hid_chan: 512 mask_act: relu n_blocks: 8 n_repeats: 3 n_src: 3 skip_chan: 128 optim: lr: 0.001 optimizer: adam weight_decay: 0.0 training: batch_size: 24 early_stop: true epochs: 200 half_lr: true num_workers: 4 ``` Results: On Libri3Mix min test set : ```yml si_sdr: 5.978836560066222 si_sdr_imp: 10.388889689413096 sdr: 6.8651365291740225 sdr_imp: 10.928018056925016 sir: 14.997089638783114 sir_imp: 18.08248357801549 sar: 8.127504792061933 sar_imp: -0.7869320540959925 stoi: 0.7669414686111115 stoi_imp: 0.20416563213078837 ``` License notice: This work "ConvTasNet_Libri3Mix_sepnoisy_8k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov, used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research only). "ConvTasNet_Libri3Mix_sepnoisy_8k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino

9fb95c3cd935b9ef24ad35dc3972d6a6

oo/distilbert-base-uncased-finetuned-squad

oo

distilbert

14

5

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

929

false

# distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## 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 ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

422c0c4de9ef24f431d218e3afb46669

HooshvareLab/bert-fa-base-uncased-ner-arman

HooshvareLab

bert

12

15

transformers

0

token-classification

true

apache-2.0

['fa']

null

0

[]

false

true

3,073

false

# ParsBERT (v2.0) A Transformer-based Model for Persian Language Understanding We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) repo for the latest information about previous and current models. ## Persian NER [ARMAN, PEYMA] This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. ### ARMAN ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes. 1. Organization 2. Location 3. Facility 4. Event 5. Product 6. Person | Label | # | |:------------:|:-----:| | Organization | 30108 | | Location | 12924 | | Facility | 4458 | | Event | 7557 | | Product | 4389 | | Person | 15645 | **Download** You can download the dataset from [here](https://github.com/HaniehP/PersianNER) ## Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. | Dataset | ParsBERT v2 | ParsBERT v1 | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |---------|-------------|-------------|-------|------------|--------------|----------|----------------|------------| | ARMAN | 99.84* | 98.79 | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 | ## How to use :hugs: | Notebook | Description | | |:----------|:-------------|------:| | [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) | ### BibTeX entry and citation info Please cite in publications as the following: ```bibtex @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` ## Questions? Post a Github issue on the [ParsBERT Issues](https://github.com/hooshvare/parsbert/issues) repo.

4ead11db0a2ab329f29212082e85c69b

shaoyu17/my_awesome_model

shaoyu17

distilbert

56

49

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,527

false

# my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8597 - F1: 0.5171 - Precision: 0.5205 - Recall: 0.52 - Accuracy: 0.52 ## 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 | F1 | Precision | Recall | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:| | 0.6451 | 1.0 | 752 | 0.7708 | 0.4699 | 0.5047 | 0.5035 | 0.5035 | | 0.5828 | 2.0 | 1504 | 0.7702 | 0.5101 | 0.5106 | 0.5106 | 0.5106 | | 0.5139 | 3.0 | 2256 | 0.8597 | 0.5171 | 0.5205 | 0.52 | 0.52 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

a8022a31598bf9b101740c28bdd385a8

plasmo/macro_bug-shiv

plasmo

null

12

0

null

6

null

false

mit

null

0

[]

false

true

985

false

### macro_bug on Stable Diffusion via Dreambooth trained on the ShivamShrirao notebook #### Model by plasmo This your the Stable Diffusion model fine-tuned the macro_bug concept taught to Stable Diffusion with Dreambooth. Macro Bug - A focused stacked macro insect model (ShivamShrirao Version, trained on 3000 steps) Keyword: "macro_bug" but sometimes not even needed as this model seems heavily weighted. I made another version (theLastBen) of this model, but this model seems to create more detailed and creative images. Sample pictures of this concept: ![robobeetle.png 0](https://huggingface.co/plasmo/macro-bug/resolve/main/concept_images/robobeetle.png) ![turtlefly2.png 1](https://huggingface.co/plasmo/macro-bug/resolve/main/concept_images/antattack7.png) ![cyberfly.png 2](https://huggingface.co/plasmo/macro-bug/resolve/main/concept_images/cyberfly.png) ![cyberfly.png 2](https://huggingface.co/plasmo/macro-bug/resolve/main/concept_images/cisty2.png)

66b0dc0f340bd3b67d88056eaf9fada7

MultiBertGunjanPatrick/multiberts-seed-0-1900k

MultiBertGunjanPatrick

bert

7

2

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

['exbert', 'multiberts', 'multiberts-seed-0']

false

true

6,487

false

# MultiBERTs Seed 0 Checkpoint 1900k (uncased) Seed 0 intermediate checkpoint 1900k 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-0](https://hf.co/multberts-seed-0). 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-0-1900k') model = BertModel.from_pretrained("multiberts-seed-0-1900k") 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>

1ad8564f801f09977e3708477607aa8f

WALIDALI/cronaldolibya

WALIDALI

null

18

3

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

423

false

### CRonaldolibya Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

37f7b0b93d498111bcd3a2a8261409ad

hululuzhu/solidity-t5

hululuzhu

t5

9

8

transformers

0

text2text-generation

true

false

apache-2.0

['en']

null

0

['solidity', 'web3', 'code generation', 'smart contract']

false

true

3,561

false

# A code generation T5 model for solidity (web3 smart contract) - See https://github.com/hululuzhu/solidity-t5 for more context ## How to use this trained model - A hello world example to use this model, notice the input `text` includes - Header solidity version like `pragma solidity ^0.5.7` - Ancestor class/library info, e.g. public functions and constants from `ParentA` - Contract/Library/Interface declaration header, e.g. `HelloWorld` ended with `{` - Or simply use the test widget on the right side of the window and test, however the quality is known to be worse without decoding params ```python # !pip install transformers -q from transformers import AutoTokenizer, T5ForConditionalGeneration DEVICE = 'cuda' # fallback to cpu if you do not have cuda tokenizer = AutoTokenizer.from_pretrained("hululuzhu/solidity-t5") model = T5ForConditionalGeneration.from_pretrained("hululuzhu/solidity-t5").to(DEVICE) text = """pragma solidity ^0.5.7; // Context: ParentA | Functions: helloA helloB | Constants: constantA contract HelloWorld is ParentA {""" input_ids = tokenizer(text, return_tensors="pt", truncation=True).input_ids.to(DEVICE) # Need to tune beam/topk/topp params to get good outcome generated_ids = model.generate(input_ids, max_length=256, num_beams=5, top_p=0.95, top_k=50) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) # Expect outcome """ string public constant name = "Hello World"; ... uint256 public constant override returns (uint256) { return initialSupply; } function initialSupply() public view returns (uint256) { ... """ ``` ## Background - Base T5 code model: https://huggingface.co/Salesforce/codet5-large - Source data: https://huggingface.co/datasets/mwritescode/slither-audited-smart-contracts - Processing steps: Clean, contract-level segmentation sepration, split in and out - After processing input sample ``` pragma solidity 0.5.7; // Context: PauserRole | Functions: isPauser addPauser renouncePauser | Constants: contract Pausable is PauserRole { ``` - After processing output sample (**notice indentation is bad, this is intentional to reduce token size**) ``` event Paused(address account); event Unpaused(address account); bool private _pausableActive; bool private _paused; constructor () internal { _paused = false; } function paused() public view returns (bool) { return _paused; } modifier whenNotPaused() { require(!_paused); _; } modifier whenPaused() { require(_paused); _; } function pause() public onlyPauser whenNotPaused whenPausableActive { _paused = true; emit Paused(msg.sender); } function unpause() public onlyPauser whenPaused whenPausableActive { _paused = false; emit Unpaused(msg.sender); } function _setPausableActive(bool _active) internal { _pausableActive = _active; } modifier whenPausableActive() { require(_pausableActive); _; } } ``` - Source training code: See the [end to end notebook](https://github.com/hululuzhu/solidity-t5/blob/main/code/Solidity_T5_Data_Processing_and_Training.ipynb) at code dir here ## Future TODO - The model is significantly under-trained because of lack of GPU budget, need 10x colab resources (~$100 for full train) - This is quite limited on how the model is used, potentially we could switch to GPT2 decoder-only to compare, but CodeT5 has its strong code optimization - Need more classifiers (T5 or BERT alike) to detect potential defects.

dcc26f35c4989fcb0803a027ae1d9586

plncmm/mdeberta-wl-base-es

plncmm

deberta-v2

13

4

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

951

false

# mdeberta-wl-base-es This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1

28d6305823ad7e4a07d15802fbc44645

theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3

theojolliffe

bart

13

1

transformers

0

text2text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,739

false

# bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv-v3-e3 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed-arxiv-arxiv) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8311 - Rouge1: 53.458 - Rouge2: 34.076 - Rougel: 37.3287 - Rougelsum: 50.7849 - Gen Len: 142.0 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.8697 | 52.6579 | 33.307 | 35.8099 | 49.9687 | 142.0 | | 0.8264 | 2.0 | 796 | 0.8293 | 52.6738 | 33.7202 | 36.1502 | 50.0501 | 141.9815 | | 0.5471 | 3.0 | 1194 | 0.8311 | 53.458 | 34.076 | 37.3287 | 50.7849 | 142.0 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

615379b8a28b73e74934be671f77d6bb

uxstudent/the-pm-generator

uxstudent

null

18

101

diffusers

1

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

702

false

### the_pm_generator Dreambooth model trained by uxstudent Use the prompt field in the right to generate avatars. Need ideas for prompts? Try: - `picture of Pablo by Leonardo Da Vinci` - `picture of Pablo wearing aviator jacket by greg rutkowsi` - `portrait photo of pablo warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes, 50mm portrait photography, hard rim lighting photography–beta –ar 2:3 –beta –upbeta –upbeta` more examples: - https://mpost.io/best-100-stable-diffusion-prompts-the-most-beautiful-ai-text-to-image-prompts/ # Example of generated images: (I HAVE DELETED THEM TO DISABLE GOOGLE IMAGE INDEXING. NOT A PROBLEM PER SE BUT UNNECESSARY)

e895c2cc18513345502bede1c0a4ffcb

Helsinki-NLP/opus-mt-sv-zne

Helsinki-NLP

marian

10

7

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-sv-zne * source languages: sv * target languages: zne * OPUS readme: [sv-zne](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-zne/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-zne/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-zne/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-zne/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.sv.zne | 23.8 | 0.474 |

0b8a7f6074330c9ed35eadb7db00bc1d

icy17/What-deepset-bert-uncased-finetune

icy17

bert

10

1

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,005

false

# What-deepset-bert-uncased-finetune This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) 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: 2e-05 - train_batch_size: 12 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.7.1+cu110 - Datasets 2.4.0 - Tokenizers 0.12.1

cbbad849776fe88b9c9957ad77ba06e7

UmberH/distilbert-base-uncased-finetuned-cola

UmberH

distilbert

13

2

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

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: 0.8381 - Matthews Correlation: 0.5456 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5245 | 1.0 | 535 | 0.5432 | 0.4249 | | 0.3514 | 2.0 | 1070 | 0.5075 | 0.4874 | | 0.2368 | 3.0 | 1605 | 0.5554 | 0.5403 | | 0.1712 | 4.0 | 2140 | 0.7780 | 0.5246 | | 0.1254 | 5.0 | 2675 | 0.8381 | 0.5456 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

feaa4ad63604159857977e73f1f250bd

kpriyanshu256/whisper-large-v2-cy-500-32-1e-05

kpriyanshu256

whisper

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['cy']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,576

false

# openai/whisper-large-v2-welsh This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2947 - Wer: 18.0609 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4438 | 0.2 | 100 | 0.4208 | 27.3594 | | 0.3255 | 0.4 | 200 | 0.3633 | 23.6118 | | 0.2856 | 0.6 | 300 | 0.3248 | 20.7023 | | 0.1811 | 1.14 | 400 | 0.3011 | 18.5534 | | 0.1404 | 1.34 | 500 | 0.2947 | 18.0609 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

5651eef0038eccd0f709f4a2a25ddd32

mrm8488/longformer-base-4096-spanish

mrm8488

roberta

13

317

transformers

11

fill-mask

true

false

mit

['es']

['spanish_large_corpus']

null

0

['Long documents', 'longformer', 'bertin', 'spanish']

false

true

1,060

false

# longformer-base-4096-spanish ## [Longformer](https://arxiv.org/abs/2004.05150) is a Transformer model for long documents. `longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint (**BERTIN** in this case) and pre-trained for *MLM* on long documents (from BETO's `all_wikis`). It supports sequences of length up to 4,096! **Longformer** uses a combination of a sliding window (*local*) attention and *global* attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations. This model was made following the research done by [Iz Beltagy and Matthew E. Peters and Arman Cohan](https://arxiv.org/abs/2004.05150). ## Citation If you want to cite this model you can use this: ```bibtex @misc{mromero2022longformer-base-4096-spanish, title={Spanish LongFormer by Manuel Romero}, author={Romero, Manuel}, publisher={Hugging Face}, journal={Hugging Face Hub}, howpublished={\url{https://huggingface.co/mrm8488/longformer-base-4096-spanish}}, year={2022} } ```

ea54e60c11245f7a6139859bda4a502c

anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european

anton-l

wav2vec2

16

8

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['all']

['google/xtreme_s']

null

0

['fleurs-asr', 'google/xtreme_s', 'generated_from_trainer']

true

5,853

false

# xtreme_s_xlsr_300m_fleurs_asr_western_european This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset. It achieves the following results on the evaluation set: - Cer: 0.2484 - Cer Ast Es: 0.1598 - Cer Bs Ba: 0.1749 - Cer Ca Es: 0.1655 - Cer Cy Gb: 0.2280 - Cer Da Dk: 0.3616 - Cer De De: 0.1287 - Cer El Gr: 0.6020 - Cer En Us: 0.1938 - Cer Es 419: 0.1288 - Cer Fi Fi: 0.2050 - Cer Fr Fr: 0.1811 - Cer Ga Ie: 0.4474 - Cer Gl Es: 0.1324 - Cer Hr Hr: 0.1555 - Cer Hu Hu: 0.3911 - Cer Is Is: 0.4646 - Cer It It: 0.1283 - Cer Kea Cv: 0.1818 - Cer Lb Lu: 0.2594 - Cer Mt Mt: 0.3628 - Cer Nb No: 0.2254 - Cer Nl Nl: 0.1790 - Cer Oci Fr: 0.2159 - Cer Pt Br: 0.2275 - Cer Sv Se: 0.3092 - Loss: 1.3089 - Loss Ast Es: 0.7715 - Loss Bs Ba: 0.7378 - Loss Ca Es: 0.7868 - Loss Cy Gb: 1.1441 - Loss Da Dk: 1.9130 - Loss De De: 0.5391 - Loss El Gr: 3.4904 - Loss En Us: 0.9632 - Loss Es 419: 0.6186 - Loss Fi Fi: 0.8953 - Loss Fr Fr: 0.9076 - Loss Ga Ie: 3.0217 - Loss Gl Es: 0.5788 - Loss Hr Hr: 0.6462 - Loss Hu Hu: 1.9029 - Loss Is Is: 2.6551 - Loss It It: 0.6052 - Loss Kea Cv: 0.9107 - Loss Lb Lu: 1.3705 - Loss Mt Mt: 2.3651 - Loss Nb No: 1.1518 - Loss Nl Nl: 0.8490 - Loss Oci Fr: 1.1421 - Loss Pt Br: 1.1641 - Loss Sv Se: 1.5910 - Wer: 0.6451 - Wer Ast Es: 0.4654 - Wer Bs Ba: 0.5443 - Wer Ca Es: 0.4979 - Wer Cy Gb: 0.5962 - Wer Da Dk: 0.8455 - Wer De De: 0.4221 - Wer El Gr: 0.9805 - Wer En Us: 0.4556 - Wer Es 419: 0.3928 - Wer Fi Fi: 0.8116 - Wer Fr Fr: 0.4690 - Wer Ga Ie: 0.8519 - Wer Gl Es: 0.4245 - Wer Hr Hr: 0.4895 - Wer Hu Hu: 0.9099 - Wer Is Is: 0.9960 - Wer It It: 0.4415 - Wer Kea Cv: 0.5202 - Wer Lb Lu: 0.7225 - Wer Mt Mt: 1.0096 - Wer Nb No: 0.6541 - Wer Nl Nl: 0.5257 - Wer Oci Fr: 0.5770 - Wer Pt Br: 0.6685 - Wer Sv Se: 0.8546 - Predict Samples: 20043 ## 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: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 3.1411 | 0.49 | 500 | 3.1673 | 1.0 | 1.0 | | 0.6397 | 0.97 | 1000 | 0.9039 | 0.7171 | 0.2862 | | 0.4033 | 1.46 | 1500 | 0.8914 | 0.6862 | 0.2763 | | 0.3473 | 1.94 | 2000 | 0.8017 | 0.6505 | 0.2536 | | 0.3143 | 2.43 | 2500 | 0.8568 | 0.6566 | 0.2627 | | 0.3004 | 2.91 | 3000 | 0.8898 | 0.6640 | 0.2686 | | 0.282 | 3.4 | 3500 | 0.8489 | 0.6637 | 0.2571 | | 0.2489 | 3.88 | 4000 | 0.8955 | 0.6744 | 0.2691 | | 0.1706 | 4.37 | 4500 | 0.9190 | 0.6788 | 0.2688 | | 0.3336 | 4.85 | 5000 | 0.8915 | 0.6594 | 0.2572 | | 0.1426 | 5.34 | 5500 | 0.9501 | 0.6784 | 0.2686 | | 0.2301 | 5.83 | 6000 | 1.0217 | 0.6719 | 0.2735 | | 0.1325 | 6.31 | 6500 | 0.9578 | 0.6691 | 0.2655 | | 0.1145 | 6.8 | 7000 | 0.9129 | 0.6680 | 0.2593 | | 0.1202 | 7.28 | 7500 | 0.9646 | 0.6749 | 0.2619 | | 0.143 | 7.77 | 8000 | 0.9200 | 0.6554 | 0.2554 | | 0.1012 | 8.25 | 8500 | 0.9553 | 0.6787 | 0.2628 | | 0.1018 | 8.74 | 9000 | 0.9455 | 0.6445 | 0.2511 | | 0.1148 | 9.22 | 9500 | 1.0206 | 0.6725 | 0.2629 | | 0.0794 | 9.71 | 10000 | 0.9305 | 0.6547 | 0.2526 | | 0.2891 | 10.19 | 10500 | 1.0424 | 0.6709 | 0.2570 | | 0.1665 | 10.68 | 11000 | 0.9760 | 0.6596 | 0.2507 | | 0.1956 | 11.17 | 11500 | 0.9549 | 0.6340 | 0.2440 | | 0.0828 | 11.65 | 12000 | 0.9598 | 0.6403 | 0.2460 | | 0.059 | 12.14 | 12500 | 0.9972 | 0.6574 | 0.2531 | | 0.0505 | 12.62 | 13000 | 0.9836 | 0.6534 | 0.2525 | | 0.0336 | 13.11 | 13500 | 1.0619 | 0.6564 | 0.2519 | | 0.0435 | 13.59 | 14000 | 1.0844 | 0.6480 | 0.2543 | | 0.0216 | 14.08 | 14500 | 1.1084 | 0.6512 | 0.2521 | | 0.0265 | 14.56 | 15000 | 1.1152 | 0.6607 | 0.2563 | | 0.0975 | 15.05 | 15500 | 1.1060 | 0.6456 | 0.2471 | | 0.1396 | 15.53 | 16000 | 1.1100 | 0.6337 | 0.2418 | | 0.0701 | 16.02 | 16500 | 1.1731 | 0.6309 | 0.2415 | | 0.1171 | 16.5 | 17000 | 1.1302 | 0.6315 | 0.2396 | | 0.0778 | 16.99 | 17500 | 1.1485 | 0.6379 | 0.2447 | | 0.0642 | 17.48 | 18000 | 1.2009 | 0.6400 | 0.2464 | | 0.0322 | 17.96 | 18500 | 1.2028 | 0.6357 | 0.2425 | | 0.031 | 18.45 | 19000 | 1.2381 | 0.6285 | 0.2416 | | 0.0579 | 18.93 | 19500 | 1.2299 | 0.6265 | 0.2409 | | 0.0628 | 19.42 | 20000 | 1.2582 | 0.6277 | 0.2395 | | 0.074 | 19.9 | 20500 | 1.2572 | 0.6278 | 0.2394 | ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.1+cu111 - Datasets 1.18.4.dev0 - Tokenizers 0.11.6

a426b88fd76cc24d82b7ae97ebdb2e21

DOOGLAK/Article_500v4_NER_Model_3Epochs_UNAUGMENTED

DOOGLAK

bert

13

5

transformers

0

token-classification

true

false

apache-2.0

null

['article500v4_wikigold_split']

null

0

['generated_from_trainer']

true

1,561

false

# Article_500v4_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article500v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2062 - Precision: 0.6464 - Recall: 0.6730 - F1: 0.6594 - Accuracy: 0.9315 ## 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 | 0.3048 | 0.3090 | 0.2978 | 0.3033 | 0.8852 | | No log | 2.0 | 116 | 0.2127 | 0.6096 | 0.6567 | 0.6323 | 0.9271 | | No log | 3.0 | 174 | 0.2062 | 0.6464 | 0.6730 | 0.6594 | 0.9315 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.4.0 - Tokenizers 0.11.6

6c2101dddd71025aa69408a50242f21f

RUCAIBox/mvp-task-dialog

RUCAIBox

mvp

9

3

transformers

1

text2text-generation

true

false

apache-2.0

['en']

null

0

['text-generation', 'text2text-generation']

false

true

3,685

false

# MVP-task-dialog The MVP-task-dialog model was proposed in [**MVP: Multi-task Supervised Pre-training for Natural Language Generation**](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen. The detailed information and instructions can be found [https://github.com/RUCAIBox/MVP](https://github.com/RUCAIBox/MVP). ## Model Description MVP-task-dialog is a prompt-based model that MVP is further equipped with prompts pre-trained using labeled task-oriented system datasets. It is a variant (MVP+S) of our main [MVP](https://huggingface.co/RUCAIBox/mvp) model. It follows a Transformer encoder-decoder architecture with layer-wise prompts. MVP-task-dialog is specially designed for task-oriented tasks, such as MultiWOZ. ## Example ```python >>> from transformers import MvpTokenizer, MvpForConditionalGeneration >>> tokenizer = MvpTokenizer.from_pretrained("RUCAIBox/mvp") >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp-task-dialog") >>> inputs = tokenizer( ... "Given the task dialog: System response [X_SEP] I'm looking for a affordable BBQ restaurant in Dallas for a large group of guest.", ... return_tensors="pt", ... ) >>> generated_ids = model.generate(**inputs) >>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True) ['What date and time would you like to go?'] ``` ## Related Models **MVP**: [https://huggingface.co/RUCAIBox/mvp](https://huggingface.co/RUCAIBox/mvp). **Prompt-based models**: - MVP-multi-task: [https://huggingface.co/RUCAIBox/mvp-multi-task](https://huggingface.co/RUCAIBox/mvp-multi-task). - MVP-summarization: [https://huggingface.co/RUCAIBox/mvp-summarization](https://huggingface.co/RUCAIBox/mvp-summarization). - MVP-open-dialog: [https://huggingface.co/RUCAIBox/mvp-open-dialog](https://huggingface.co/RUCAIBox/mvp-open-dialog). - MVP-data-to-text: [https://huggingface.co/RUCAIBox/mvp-data-to-text](https://huggingface.co/RUCAIBox/mvp-data-to-text). - MVP-story: [https://huggingface.co/RUCAIBox/mvp-story](https://huggingface.co/RUCAIBox/mvp-story). - MVP-question-answering: [https://huggingface.co/RUCAIBox/mvp-question-answering](https://huggingface.co/RUCAIBox/mvp-question-answering). - MVP-question-generation: [https://huggingface.co/RUCAIBox/mvp-question-generation](https://huggingface.co/RUCAIBox/mvp-question-generation). - MVP-task-dialog: [https://huggingface.co/RUCAIBox/mvp-task-dialog](https://huggingface.co/RUCAIBox/mvp-task-dialog). **Multi-task models**: - MTL-summarization: [https://huggingface.co/RUCAIBox/mtl-summarization](https://huggingface.co/RUCAIBox/mtl-summarization). - MTL-open-dialog: [https://huggingface.co/RUCAIBox/mtl-open-dialog](https://huggingface.co/RUCAIBox/mtl-open-dialog). - MTL-data-to-text: [https://huggingface.co/RUCAIBox/mtl-data-to-text](https://huggingface.co/RUCAIBox/mtl-data-to-text). - MTL-story: [https://huggingface.co/RUCAIBox/mtl-story](https://huggingface.co/RUCAIBox/mtl-story). - MTL-question-answering: [https://huggingface.co/RUCAIBox/mtl-question-answering](https://huggingface.co/RUCAIBox/mtl-question-answering). - MTL-question-generation: [https://huggingface.co/RUCAIBox/mtl-question-generation](https://huggingface.co/RUCAIBox/mtl-question-generation). - MTL-task-dialog: [https://huggingface.co/RUCAIBox/mtl-task-dialog](https://huggingface.co/RUCAIBox/mtl-task-dialog). ## Citation ```bibtex @article{tang2022mvp, title={MVP: Multi-task Supervised Pre-training for Natural Language Generation}, author={Tang, Tianyi and Li, Junyi and Zhao, Wayne Xin and Wen, Ji-Rong}, journal={arXiv preprint arXiv:2206.12131}, year={2022}, url={https://arxiv.org/abs/2206.12131}, } ```

bcdd393d0669f236e6a1d61aa324e058

gngpostalsrvc/BERiT_2000

gngpostalsrvc

roberta

11

7

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,839

false

# BERiT_2000 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.7293 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.9294 | 0.19 | 500 | 6.8136 | | 6.7692 | 0.39 | 1000 | 6.8006 | | 6.7567 | 0.58 | 1500 | 6.7770 | | 6.746 | 0.77 | 2000 | 6.7414 | | 6.7577 | 0.97 | 2500 | 6.7333 | | 6.7295 | 1.16 | 3000 | 6.7405 | | 6.7635 | 1.36 | 3500 | 6.7272 | | 6.7715 | 1.55 | 4000 | 6.7114 | | 6.7348 | 1.74 | 4500 | 6.7275 | | 6.719 | 1.94 | 5000 | 6.7322 | | 6.7427 | 2.13 | 5500 | 6.7242 | | 6.7136 | 2.32 | 6000 | 6.6852 | | 6.719 | 2.52 | 6500 | 6.7430 | | 6.7229 | 2.71 | 7000 | 6.7331 | | 6.7166 | 2.9 | 7500 | 6.7293 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

7e12d9a24f472209b9ab3e97c8319eb8

Dimitre/sd-pokemon-model

Dimitre

null

18

11

diffusers

1

null

false

apache-2.0

['en']

['lambdalabs/pokemon-blip-captions']

null

2

0

[]

false

true

1,217

false

# sd-pokemon-model ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `lambdalabs/pokemon-blip-captions` 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: 1e-05 - train_batch_size: 10 - eval_batch_size: 16 - gradient_accumulation_steps: 4 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: constant - lr_warmup_steps: 0 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Dimitre/sd-pokemon-model/tensorboard?#scalars)

2d08629fe009a8e456cea604bb288000

Neprox/STT-swedish-lr-decay-model

Neprox

whisper

27

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['sv']

null

0

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

true

1,655

false

# Whisper Small - Swedish This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4915 - Wer: 25.5384 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2107 | 1.3 | 1000 | 0.4673 | 34.0432 | | 0.0821 | 2.59 | 2000 | 0.4284 | 27.4152 | | 0.0378 | 3.89 | 3000 | 0.4210 | 25.3637 | | 0.0042 | 5.18 | 4000 | 0.4247 | 23.5541 | | 0.001 | 6.48 | 5000 | 0.4286 | 22.7770 | | 0.0106 | 7.77 | 6000 | 0.4915 | 25.5384 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1 - Datasets 2.7.1 - Tokenizers 0.13.2

3f35b00ea85b34a0b9bc566dda97e05f

anshengmay/xlm-roberta-base-finetuned-marc

anshengmay

xlm-roberta

12

2

transformers

0

text-classification

true

false

mit

null

['amazon_reviews_multi']

null

0

['generated_from_trainer']

true

1,271

false

# xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9904 - Mae: 0.4867 ## 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 | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.2067 | 1.0 | 308 | 1.0806 | 0.5575 | | 1.0182 | 2.0 | 616 | 0.9904 | 0.4867 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

12cba0080e396199735ed8df4fd6f2ee

wavymulder/lomo-diffusion

wavymulder

null

22

161

diffusers

11

text-to-image

false

creativeml-openrail-m

['en']

null

4

0

2

0

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

false

true

1,438

false

**Lomo Diffusion** ![Header](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/images/page1.jpg) [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/lomo-1.0.ckpt) - - - [*SAFETENSORS DOWNLOAD LINK*](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/lomo-1.0.safetensors) This is a dreambooth model trained on a diverse set of stylized photographs. Use the activation token **lomo style** in your prompt (I recommend at the start) This model is inspired by the Lomography movement, which embraces the imperfections and style of old LOMO cameras. The model excels at producing bright saturated colors as well as a variety of film artifacts that add to the illusion of a real photograph. When using most models, I typically use **blur haze** in my negative prompt. I encourage you to experiment and see what works well for you. Trained from 1.5 with VAE. Please see [this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images.](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/paramets_for_samples.txt) You can [see here a non-cherrypicked batch of 49 images here.](https://i.imgur.com/cfIj3iq.jpg) And you can [see here a direct comparison between Analog Style and Lomo Style.](https://i.imgur.com/ugdFzPI.jpg) ![Environments Example](https://huggingface.co/wavymulder/lomo-diffusion/resolve/main/images/page2.jpg)

04cb1215bb37e1581bc6ab5fd660f30b

bofenghuang/asr-wav2vec2-ctc-french

bofenghuang

wav2vec2

59

2,058

transformers

1

automatic-speech-recognition

true

false

apache-2.0

['fr']

['common_voice', 'mozilla-foundation/common_voice_11_0', 'facebook/multilingual_librispeech', 'facebook/voxpopuli', 'gigant/african_accented_french']

null

0

['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event', 'CTC', 'Wav2vec2']

true

3,984

false

# Fine-tuned wav2vec2-FR-7K-large model for ASR in French <style> img { display: inline; } </style> ![Model architecture](https://img.shields.io/badge/Model_Architecture-Wav2Vec2--CTC-lightgrey) ![Model size](https://img.shields.io/badge/Params-315M-lightgrey) ![Language](https://img.shields.io/badge/Language-French-lightgrey) This model is a fine-tuned version of [LeBenchmark/wav2vec2-FR-7K-large](https://huggingface.co/LeBenchmark/wav2vec2-FR-7K-large), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and validation splits of [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0), [Multilingual LibriSpeech](https://huggingface.co/datasets/facebook/multilingual_librispeech), [Voxpopuli](https://github.com/facebookresearch/voxpopuli), [Multilingual TEDx](http://www.openslr.org/100), [MediaSpeech](https://www.openslr.org/108), and [African Accented French](https://huggingface.co/datasets/gigant/african_accented_french). When using the model make sure that your speech input is also sampled at 16Khz. ## Usage 1. To use on a local audio file with the language model ```python import torch import torchaudio from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device) processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/asr-wav2vec2-french") model_sample_rate = processor_with_lm.feature_extractor.sampling_rate wav_path = "example.wav" # path to your audio file waveform, sample_rate = torchaudio.load(wav_path) waveform = waveform.squeeze(axis=0) # mono # resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # normalize input_dict = processor_with_lm(waveform, sampling_rate=model_sample_rate, return_tensors="pt") with torch.inference_mode(): logits = model(input_dict.input_values.to(device)).logits predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0] ``` 2. To use on a local audio file without the language model ```python import torch import torchaudio from transformers import AutoModelForCTC, Wav2Vec2Processor device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model = AutoModelForCTC.from_pretrained("bhuang/asr-wav2vec2-french").to(device) processor = Wav2Vec2Processor.from_pretrained("bhuang/asr-wav2vec2-french") model_sample_rate = processor.feature_extractor.sampling_rate wav_path = "example.wav" # path to your audio file waveform, sample_rate = torchaudio.load(wav_path) waveform = waveform.squeeze(axis=0) # mono # resample if sample_rate != model_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, model_sample_rate) waveform = resampler(waveform) # normalize input_dict = processor(waveform, sampling_rate=model_sample_rate, return_tensors="pt") with torch.inference_mode(): logits = model(input_dict.input_values.to(device)).logits # decode predicted_ids = torch.argmax(logits, dim=-1) predicted_sentence = processor.batch_decode(predicted_ids)[0] ``` ## Evaluation 1. To evaluate on `mozilla-foundation/common_voice_11_0` ```bash python eval.py \ --model_id "bhuang/asr-wav2vec2-french" \ --dataset "mozilla-foundation/common_voice_11_0" \ --config "fr" \ --split "test" \ --log_outputs \ --outdir "outputs/results_mozilla-foundatio_common_voice_11_0_with_lm" ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py \ --model_id "bhuang/asr-wav2vec2-french" \ --dataset "speech-recognition-community-v2/dev_data" \ --config "fr" \ --split "validation" \ --chunk_length_s 30.0 \ --stride_length_s 5.0 \ --log_outputs \ --outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm" ```

581553fbba1b84d00e013af9d45b668c

infoxixxx/cat-toy

infoxixxx

null

22

4

diffusers

0

null

false

mit

null

2

0

[]

false

true

1,198

false

### Cat toy on Stable Diffusion via Dreambooth #### model by infoxixxx This your the Stable Diffusion model fine-tuned the Cat toy concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks toy** 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/infoxixxx/cat-toy/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/infoxixxx/cat-toy/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/infoxixxx/cat-toy/resolve/main/concept_images/2.jpeg) ![image 3](https://huggingface.co/infoxixxx/cat-toy/resolve/main/concept_images/0.jpeg)

401ea0a510609a163bad8eb5c88a5f9d

Chandanab/deit-tiny-patch16-224-finetuned-eurosat

Chandanab

vit

10

9

transformers

0

image-classification

true

false

apache-2.0

null

['image_folder']

null

0

['generated_from_trainer']

true

1,468

false

# deit-tiny-patch16-224-finetuned-eurosat This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the image_folder dataset. It achieves the following results on the evaluation set: - Loss: 0.1779 - Accuracy: 0.9192 ## 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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.3528 | 0.8283 | | 0.5571 | 2.0 | 14 | 0.2141 | 0.8788 | | 0.197 | 3.0 | 21 | 0.1779 | 0.9192 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.2.0 - Tokenizers 0.12.1

8de7d5b52b062c4d393f3bf3369c1cb9

explosion/af_udv25_afrikaansafribooms_trf

explosion

null

28

1

spacy

0

token-classification

false

cc-by-sa-4.0

['af']

null

0

['spacy', 'token-classification']

false

true

6,840

false

UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms | Feature | Description | | --- | --- | | **Name** | `af_udv25_afrikaansafribooms_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (455 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `AOA`, `AOP`, `ASA`, `ASP`, `AVA`, `AVP`, `BO`, `BS`, `BV`, `KN`, `KO`, `LB`, `LO`, `NA`, `NEE`, `NM`, `NME`, `NSE`, `NSED`, `NSM`, `PA`, `PB`, `PDHEB`, `PDHEDP`, `PDHENP`, `PDHEW`, `PDMB`, `PDMP`, `PDMW`, `PDOENP`, `PDOEW`, `PDVEB`, `PDVEDP`, `PDVENP`, `PDVEW`, `PEEB`, `PEEDP`, `PEENP`, `PEMB`, `PEMP`, `PEMW`, `PO`, `PTEB`, `PTEDP`, `PTENP`, `PTEW`, `PTMP`, `PV`, `PW`, `RA`, `RK`, `RL`, `RO`, `RS`, `RSF`, `RV`, `RWD`, `SVS`, `THAB`, `THAO`, `THBB`, `THBO`, `THNB`, `THPB`, `THPO`, `TRAB`, `TRAO`, `TRBB`, `UPB`, `UPD`, `UPI`, `UPO`, `UPS`, `UPV`, `UPW`, `UXD`, `VTHOG`, `VTHOK`, `VTHOO`, `VTHOV`, `VTHSG`, `VTHSO`, `VTUOA`, `VTUOM`, `VTUOP`, `VUOT`, `VVHOG`, `VVHOK`, `VVHOO`, `VVUOM`, `VVUOP`, `ZE`, `ZM`, `ZPL`, `ZPR` | | **`morphologizer`** | `Definite=Def\|POS=DET\|PronType=Art`, `Number=Sing\|POS=NOUN`, `AdpType=Prep\|POS=ADP`, `AdjType=Attr\|Case=Nom\|Degree=Pos\|POS=ADJ`, `Number=Plur\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Cop`, `Definite=Ind\|POS=DET\|PronType=Art`, `POS=NUM`, `POS=PART\|PartType=Inf`, `POS=VERB\|Subcat=Tran\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=PRON\|PronType=Rel`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Pas`, `POS=PUNCT`, `POS=CCONJ`, `POS=SCONJ`, `POS=VERB\|Subcat=Intr\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=VERB\|Subcat=Intr\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Pas`, `Degree=Pos\|POS=ADV`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Mod`, `POS=DET\|PronType=Ind`, `POS=X`, `Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Ind`, `POS=PART\|PartType=Neg`, `POS=VERB\|Subcat=Tran\|Tense=Past\|VerbForm=Part`, `AdjType=Pred\|Case=Nom\|Degree=Pos\|POS=ADJ`, `POS=DET\|PronType=Dem`, `Degree=Cmp\|POS=ADV`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SYM`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `POS=PART\|PartType=Gen`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|POS=ADV`, `Degree=Dim\|Number=Sing\|POS=NOUN`, `Number=Sing\|POS=PRON\|Person=2\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Int`, `Number=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `AdjType=Attr\|Case=Nom\|Degree=Sup\|POS=ADJ`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `POS=VERB\|Subcat=Prep\|Tense=Pres\|VerbForm=Fin,Inf`, `POS=AUX\|Tense=Pres\|VerbForm=Fin,Inf\|VerbType=Aux`, `Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=PRON\|PronType=Rcp`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Mod`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Fin\|VerbType=Cop`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Acc,Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `AdjType=Attr\|Case=Nom\|Degree=Cmp\|POS=ADJ`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `AdjType=Pred\|Case=Nom\|Degree=Sup\|POS=ADJ` | | **`parser`** | `ROOT`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `1`, `2`, `4`, `7`, `8`, `10`, `12`, `14`, `16`, `18`, `21`, `24`, `26`, `28`, `31`, `32`, `34`, `37`, `39`, `40`, `42`, `44`, `46`, `47`, `49`, `51`, `53`, `54`, `56`, `57`, `58`, `59`, `61`, `64`, `66`, `68`, `69`, `72`, `74`, `75`, `77`, `78`, `81`, `83`, `84`, `85`, `86`, `87`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `105`, `108`, `110`, `113`, `116`, `117`, `118`, `121`, `123`, `124`, `125`, `127`, `128`, `129`, `133`, `136`, `138`, `141`, `143`, `145`, `147`, `151`, `153`, `154`, `156`, `158`, `159`, `160`, `162`, `164`, `165`, `167`, `168`, `170`, `172`, `174`, `176`, `178`, `179`, `180`, `181`, `183`, `185`, `189`, `190`, `191`, `192`, `194`, `195`, `197`, `198`, `201`, `202`, `203`, `204`, `206`, `207`, `209`, `213`, `214`, `216`, `217`, `218`, `220`, `221`, `222`, `223`, `225`, `226`, `228`, `229`, `231`, `233`, `234`, `236`, `238`, `240`, `241`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `255`, `256`, `257`, `258`, `261`, `262`, `263`, `265`, `267`, `269`, `270`, `271`, `273`, `275`, `276`, `278`, `279`, `281`, `283`, `285`, `287`, `289`, `291`, `294`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `305`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `326`, `327`, `328`, `329`, `330`, `332`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `344`, `347`, `348`, `349`, `351`, `353`, `355`, `357`, `359`, `360`, `361`, `362`, `365`, `366`, `367`, `369`, `371`, `373`, `374`, `375`, `377`, `379`, `381`, `383`, `386`, `388`, `390`, `392`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `403`, `405`, `406`, `408`, `409`, `411`, `412`, `414`, `417`, `215`, `418`, `419`, `420`, `421`, `422`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `434`, `436`, `438`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `450`, `452` | </details> ### Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.92 | | `TOKEN_P` | 99.89 | | `TOKEN_R` | 99.94 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 100.00 | | `SENTS_P` | 100.00 | | `SENTS_R` | 100.00 | | `TAG_ACC` | 96.01 | | `POS_ACC` | 98.52 | | `MORPH_ACC` | 97.52 | | `DEP_UAS` | 90.78 | | `DEP_LAS` | 87.50 | | `LEMMA_ACC` | 97.87 |

f5aaf5dd579a62055a76b7da53915b56

pyf98/chime4_conformer_e12_linear2048

pyf98

null

33

3

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['en']

['chime4']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

8,232

false

## ESPnet2 ASR model ### `pyf98/chime4_conformer_e12_linear2048` This model was trained by Yifan Peng using chime4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout ad91279f0108d54bd22abe29671b376f048822c5 pip install -e . cd egs2/chime4/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/chime4_conformer_e12_linear2048 ```  # RESULTS ## Environments - date: `Wed Dec 28 20:41:40 EST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202211` - pytorch version: `pytorch 1.12.1` - Git hash: `ad91279f0108d54bd22abe29671b376f048822c5` - Commit date: `Wed Dec 28 20:15:42 2022 -0500` ## asr_train_asr_conformer_e12_linear2048_raw_en_char_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|27119|93.3|5.4|1.3|0.5|7.3|55.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|27120|91.7|6.7|1.6|0.9|9.1|62.0| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|21409|89.2|8.9|1.9|1.1|12.0|64.5| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|21416|87.8|9.6|2.6|1.4|13.6|68.1| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_real_beamformit_5mics|1640|160390|97.2|1.5|1.3|0.7|3.5|55.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/dt05_simu_beamformit_5mics|1640|160400|96.3|2.0|1.7|1.0|4.7|62.0| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_real_beamformit_5mics|1320|126796|95.1|2.8|2.1|1.2|6.1|64.6| |decode_asr_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/et05_simu_beamformit_5mics|1320|126812|94.0|3.1|3.0|1.6|7.7|68.1| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer_e12_linear2048.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_e12_linear2048_raw_en_char_sp ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 2 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 45069 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 15000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_char_sp/train/speech_shape - exp/asr_stats_raw_en_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_en_char_sp/valid/speech_shape - exp/asr_stats_raw_en_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_multi_noisy_si284_sp/wav.scp - speech - kaldi_ark - - dump/raw/tr05_multi_noisy_si284_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dt05_multi_isolated_1ch_track/wav.scp - speech - kaldi_ark - - dump/raw/dt05_multi_isolated_1ch_track/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - <space> - E - T - A - N - I - O - S - R - H - L - D - C - U - M - P - F - G - Y - W - B - V - K - . - X - '''' - J - Q - Z - ',' - '-' - '"' - <NOISE> - '*' - ':' - ( - ) - '?' - '&' - ; - '!' - / - '{' - '}' - '1' - '2' - '0' - $ - '8' - '9' - '6' - '3' - '5' - '7' - '4' - '~' - '`' - _ - <*IN*> - <*MR.*> - \ - ^ - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: data/nlsyms.txt cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202211' distributed: true ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```

b5216f105031fa99d8066750606de763

studio-ousia/luke-japanese-large

studio-ousia

luke

10

2,488

transformers

3

fill-mask

true

false

apache-2.0

['ja']

null

0

['luke', 'named entity recognition', 'entity typing', 'relation classification', 'question answering']

false

true

2,470

false

## luke-japanese-large **luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Please refer to our [GitHub repository](https://github.com/studio-ousia/luke) for more details and updates. This model contains Wikipedia entity embeddings which are not used in general NLP tasks. Please use the [lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/) for tasks that do not use Wikipedia entities as inputs. **luke-japanese**は、単語とエンティティの知識拡張型訓練済み Transformer モデル**LUKE**の日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。詳細については、[GitHub リポジトリ](https://github.com/studio-ousia/luke)を参照してください。このモデルは、通常の NLP タスクでは使われない Wikipedia エンティティのエンベディングを含んでいます。単語の入力のみを使うタスクには、[lite version](https://huggingface.co/studio-ousia/luke-japanese-large-lite/)を使用してください。 ### Experimental results on JGLUE The experimental results evaluated on the dev set of [JGLUE](https://github.com/yahoojapan/JGLUE) is shown as follows: | Model | MARC-ja | JSTS | JNLI | JCommonsenseQA | | ----------------------------- | --------- | ------------------- | --------- | -------------- | | | acc | Pearson/Spearman | acc | acc | | **LUKE Japanese large** | **0.965** | **0.932**/**0.902** | **0.927** | 0.893 | | _Baselines:_ | | | Tohoku BERT large | 0.955 | 0.913/0.872 | 0.900 | 0.816 | | Waseda RoBERTa large (seq128) | 0.954 | 0.930/0.896 | 0.924 | **0.907** | | Waseda RoBERTa large (seq512) | 0.961 | 0.926/0.892 | 0.926 | 0.891 | | XLM RoBERTa large | 0.964 | 0.918/0.884 | 0.919 | 0.840 | The baseline scores are obtained from [here](https://github.com/yahoojapan/JGLUE/blob/a6832af23895d6faec8ecf39ec925f1a91601d62/README.md). ### Citation ```latex @inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} } ```

4f9af261b4dd9345bb338d0b998bfe77

bochaowei/t5-small-finetuned-xsum-wei0

bochaowei

t5

14

2

transformers

0

text2text-generation

true

false

apache-2.0

null

['xsum']

null

0

['generated_from_trainer']

true

1,420

false

# t5-small-finetuned-xsum-wei0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.6289 - Rouge1: 25.7398 - Rouge2: 6.1361 - Rougel: 19.8262 - Rougelsum: 19.8284 - Gen Len: 18.7984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.858 | 1.0 | 1701 | 2.6289 | 25.7398 | 6.1361 | 19.8262 | 19.8284 | 18.7984 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

facae8b92758d33f841a0bfcd97adc39

google/t5-efficient-tiny

google

t5

12

10,405

transformers

3

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,203

false

# T5-Efficient-TINY (Deep-Narrow version) T5-Efficient-TINY 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** - is of model type **Tiny** with no variations. It has **15.58** million parameters and thus requires *ca.* **62.32 MB** of memory in full precision (*fp32*) or **31.16 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.

9ff7fe5cdfb2d0cb44c6eafb1ee28b59

HMHMlee/BioLinkBERT-base-finetuned-ner

HMHMlee

bert

10

11

transformers

1

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,430

false

# BioLinkBERT-base-finetuned-ner This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1226 - Precision: 0.8760 - Recall: 0.9185 - F1: 0.8968 - Accuracy: 0.9647 ## Model description This model is designed to perform NER function for specific text using BioLink BERT ## Intended uses & limitations The goal was to have a drug tag printed immediately for a particular sentence, but it has the disadvantage of being marked as LABEL LABEL0 : irrelevant text LABEL1,2 : Drug LABEL3,4 : condition ## Training and evaluation data More information needed ## Training procedure Reference Code: SciBERT Fine-Tuning on Drug/ADE Corpus (https://github.com/jsylee/personal-projects/blob/master/Hugging%20Face%20ADR%20Fine-Tuning/SciBERT%20ADR%20Fine-Tuning.ipynb) ## How to use from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("HMHMlee/BioLinkBERT-base-finetuned-ner") ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1099 | 1.0 | 201 | 0.1489 | 0.8415 | 0.9032 | 0.8713 | 0.9566 | | 0.1716 | 2.0 | 402 | 0.1318 | 0.8456 | 0.9135 | 0.8782 | 0.9597 | | 0.1068 | 3.0 | 603 | 0.1197 | 0.8682 | 0.9110 | 0.8891 | 0.9641 | | 0.0161 | 4.0 | 804 | 0.1219 | 0.8694 | 0.9157 | 0.8919 | 0.9639 | | 0.1499 | 5.0 | 1005 | 0.1226 | 0.8760 | 0.9185 | 0.8968 | 0.9647 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

7838c8e56a540705dd20836cd3920647

ITESM/st_demo_5

ITESM

bert

12

4

sentence-transformers

0

sentence-similarity

true

false

apache-2.0

['en']

null

0

['sentence-transformers', 'feature-extraction', 'sentence-similarity']

false

true

10,029

false

# all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch import torch.nn.functional as F #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,170,060,424** |

1481c1c741770ac0b1e9316314319a97

ShannonDXQ/distilbert-base-uncased-finetuned-cola

ShannonDXQ

distilbert

33

2

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

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: 0.8107 - Matthews Correlation: 0.5422 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.522 | 1.0 | 535 | 0.5193 | 0.4152 | | 0.3451 | 2.0 | 1070 | 0.4942 | 0.5166 | | 0.2335 | 3.0 | 1605 | 0.5490 | 0.5291 | | 0.179 | 4.0 | 2140 | 0.7727 | 0.5150 | | 0.1314 | 5.0 | 2675 | 0.8107 | 0.5422 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

666d01b62f0f71703fcdf231fc0c2b0f

ArafatBHossain/distiled_flip_model_emotion_alpha_0.8_epoch7_v1

ArafatBHossain

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

0

['generated_from_trainer']

true

1,713

false

# distiled_flip_model_emotion_alpha_0.8_epoch7_v1 This model is a fine-tuned version of [ArafatBHossain/distill_bert_fine_tuned_emotion_dataset](https://huggingface.co/ArafatBHossain/distill_bert_fine_tuned_emotion_dataset) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1583 - Accuracy: 0.9435 ## 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: 7 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2013 | 1.0 | 2000 | 0.2653 | 0.9355 | | 0.1625 | 2.0 | 4000 | 0.2537 | 0.9365 | | 0.1262 | 3.0 | 6000 | 0.1934 | 0.935 | | 0.1048 | 4.0 | 8000 | 0.1813 | 0.9435 | | 0.0777 | 5.0 | 10000 | 0.1500 | 0.941 | | 0.0614 | 6.0 | 12000 | 0.1591 | 0.944 | | 0.0465 | 7.0 | 14000 | 0.1583 | 0.9435 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.11.0 - Datasets 2.6.1 - Tokenizers 0.12.1

ce0a0c076b91b38762511dc5c505dac2

commanderstrife/bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner

commanderstrife

bert

16

8

transformers

0

token-classification

true

false

mit

null

['bc4chemd_ner']

null

0

['generated_from_trainer']

true

2,237

false

# bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc4chemd_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0641 - Precision: 0.8944 - Recall: 0.8777 - F1: 0.8860 - Accuracy: 0.9908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.006 | 1.0 | 1918 | 0.0310 | 0.8697 | 0.8510 | 0.8602 | 0.9894 | | 0.0097 | 2.0 | 3836 | 0.0345 | 0.8855 | 0.8637 | 0.8745 | 0.9898 | | 0.0058 | 3.0 | 5754 | 0.0359 | 0.8733 | 0.8836 | 0.8784 | 0.9902 | | 0.0014 | 4.0 | 7672 | 0.0440 | 0.8723 | 0.8842 | 0.8782 | 0.9903 | | 0.0005 | 5.0 | 9590 | 0.0539 | 0.8862 | 0.8673 | 0.8766 | 0.9903 | | 0.0001 | 6.0 | 11508 | 0.0558 | 0.8939 | 0.8628 | 0.8781 | 0.9904 | | 0.0001 | 7.0 | 13426 | 0.0558 | 0.8846 | 0.8729 | 0.8787 | 0.9903 | | 0.0012 | 8.0 | 15344 | 0.0635 | 0.8935 | 0.8696 | 0.8814 | 0.9905 | | 0.0 | 9.0 | 17262 | 0.0624 | 0.8897 | 0.8831 | 0.8864 | 0.9908 | | 0.0002 | 10.0 | 19180 | 0.0641 | 0.8944 | 0.8777 | 0.8860 | 0.9908 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

bafa08f1c57c8292aecf0c376daa530f

HarBat/distilled_bert_finetuning

HarBat

distilbert

43

3

transformers

0

text-classification

true

false

apache-2.0

null

['sst2']

null

0

['generated_from_trainer']

true

937

false

# distilled_bert_finetuning This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the sst2 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.21.2 - Pytorch 1.11.0+cu113 - Datasets 2.5.2 - Tokenizers 0.12.1

f9f13f73636af757140404dec451582e

nouman10/robertabase-finetuned-claim-ltp-full-prompt

nouman10

roberta

9

2

transformers

0

fill-mask

false

true

false

mit

null

0

['generated_from_keras_callback']

true

1,703

false

# nouman10/robertabase-finetuned-claim-ltp-full-prompt This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0233 - Validation Loss: 0.0231 - 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: {'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': -425, '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 | |:----------:|:---------------:|:-----:| | 0.1965 | 0.0452 | 0 | | 0.0321 | 0.0231 | 1 | | 0.0232 | 0.0231 | 2 | | 0.0232 | 0.0231 | 3 | | 0.0233 | 0.0231 | 4 | ### Framework versions - Transformers 4.19.1 - TensorFlow 2.8.0 - Datasets 2.2.1 - Tokenizers 0.12.1

009e060ab5d92faaaf97df30a7a4fbcc

ibm/ColD-Fusion-bert-base-uncased-itr0-seed0

ibm

bert

8

6

transformers

0

text-classification

true

false

mit

['en']

null

0

['exbert']

false

true

3,174

false

# ColD Fusion BERT uncased model Finetuned model that aims to be a great base model. It improves over BERT base model (uncased), trained on 35 datasets. Full details at [this paper](https://arxiv.org/abs/2212.01378). ## Paper Abstract: Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture. ### How to use Best way to use is to finetune on your own task, but you can also extract features directly. To get the features of a given text in PyTorch: ```python from transformers import RobertaTokenizer, RobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = RobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` and in TensorFlow: ```python from transformers import RobertaTokenizer, TFRobertaModel tokenizer = RobertaTokenizer.from_pretrained('ibm/ColD-Fusion') model = TFRobertaModel.from_pretrained('ibm/ColD-Fusion') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` ## Evaluation results See full evaluation results of this model and many more [here](https://ibm.github.io/model-recycling/roberta-base_table.html) When fine-tuned on downstream tasks, this model achieves the following results: ### BibTeX entry and citation info ```bibtex @article{ColDFusion, author = {Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem ChoshenYinhan Liu and}, title = {ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning}, journal = {CoRR}, volume = {abs/2212.01378}, year = {2022}, url = {https://arxiv.org/abs/2212.01378}, archivePrefix = {arXiv}, eprint = {2212.01378}, } ``` <a href="https://huggingface.co/exbert/?model=ibm/ColD-Fusion"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

8b6c0752942f9187e0f4b636564d38a7

microsoft/tapex-large-finetuned-tabfact

microsoft

bart

8

81

transformers

1

text-classification

true

false

mit

['en']

['tab_fact']

null

0

['tapex', 'table-question-answering']

false

true

2,480

false

# TAPEX (large-sized model) TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). ## Model description TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. This model is the `tapex-base` model fine-tuned on the [Tabfact](https://huggingface.co/datasets/tab_fact) dataset. ## Intended Uses You can use the model for table fact verficiation. ### How to Use Here is how to use this model in transformers: ```python from transformers import TapexTokenizer, BartForSequenceClassification import pandas as pd tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact") model = BartForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact") data = { "year": [1896, 1900, 1904, 2004, 2008, 2012], "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"] } table = pd.DataFrame.from_dict(data) # tapex accepts uncased input since it is pre-trained on the uncased corpus query = "beijing hosts the olympic games in 2012" encoding = tokenizer(table=table, query=query, return_tensors="pt") outputs = model(**encoding) output_id = int(outputs.logits[0].argmax(dim=0)) print(model.config.id2label[output_id]) # Refused ``` ### How to Eval Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex). ### BibTeX entry and citation info ```bibtex @inproceedings{ liu2022tapex, title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor}, author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=O50443AsCP} } ```

07af20f0ecf53a78a5638b7a90afeae2

microsoft/swinv2-base-patch4-window12-192-22k

microsoft

swinv2

5

842

transformers

0

image-classification

true

false

apache-2.0

null

['imagenet-1k']

null

0

['vision', 'image-classification']

false

true

3,779

false

# Swin Transformer v2 (tiny-sized model) Swin Transformer v2 model pre-trained on ImageNet-21k at resolution 192x192. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer). Disclaimer: The team releasing Swin Transformer v2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally. Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png) [Source](https://paperswithcode.com/method/swin-transformer) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 21k ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k") model = AutoModelForImageClassification.from_pretrained("microsoft/swinv2-base-patch4-window12-192-22k") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 21k ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2111-09883, author = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo}, title = {Swin Transformer {V2:} Scaling Up Capacity and Resolution}, journal = {CoRR}, volume = {abs/2111.09883}, year = {2021}, url = {https://arxiv.org/abs/2111.09883}, eprinttype = {arXiv}, eprint = {2111.09883}, timestamp = {Thu, 02 Dec 2021 15:54:22 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-09883.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

6388e1cb4b7bb4b7d945ee4a7729a20e

mprzibilla/small_finetune_M01

mprzibilla

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,386

false

# small_finetune_M01 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.2363 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 20 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:---:| | 121.7217 | 200.0 | 800 | 3.1742 | 1.0 | | 2.066 | 400.0 | 1600 | 2.8390 | 1.0 | | 1.7019 | 600.0 | 2400 | 2.8359 | 1.0 | | 1.5282 | 800.0 | 3200 | 2.8655 | 1.0 | | 1.4089 | 1000.0 | 4000 | 2.8933 | 1.0 | | 1.3123 | 1200.0 | 4800 | 2.9047 | 1.0 | | 1.2361 | 1400.0 | 5600 | 2.9677 | 1.0 | | 1.1758 | 1600.0 | 6400 | 3.0008 | 1.0 | | 1.1241 | 1800.0 | 7200 | 3.0795 | 1.0 | | 1.0816 | 2000.0 | 8000 | 3.1214 | 1.0 | | 1.0497 | 2200.0 | 8800 | 3.1518 | 1.0 | | 1.0349 | 2400.0 | 9600 | 3.1584 | 1.0 | | 1.0058 | 2600.0 | 10400 | 3.1876 | 1.0 | | 0.9983 | 2800.0 | 11200 | 3.1843 | 1.0 | | 0.9863 | 3000.0 | 12000 | 3.1914 | 1.0 | | 0.9776 | 3200.0 | 12800 | 3.2005 | 1.0 | | 0.9647 | 3400.0 | 13600 | 3.2245 | 1.0 | | 0.9586 | 3600.0 | 14400 | 3.2352 | 1.0 | | 0.9521 | 3800.0 | 15200 | 3.2398 | 1.0 | | 0.9537 | 4000.0 | 16000 | 3.2363 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

558326284d70d087135ee67419a0c341

jkang/espnet2_librispeech_100_conformer_char

jkang

null

21

8

espnet

0

automatic-speech-recognition

false

cc-by-4.0

['noinfo']

['librispeech_100']

null

0

['espnet', 'audio', 'automatic-speech-recognition']

false

true

7,171

false

## ESPnet2 ASR model ### `jkang/espnet2_librispeech_100_conformer_char` This model was trained by jaekookang using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout 82a0a0fa97b8a4a578f0a2c031ec49b3afec1504 pip install -e . cd egs2/librispeech_100/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_librispeech_100_conformer_char ```  # RESULTS ## Environments - date: `Thu Feb 24 17:47:04 KST 2022` - python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.10.1` - Git hash: `82a0a0fa97b8a4a578f0a2c031ec49b3afec1504` - Commit date: `Wed Feb 23 08:06:47 2022 +0900` ## asr_conformer_lr2e-3_warmup15k_amp_nondeterministic_char ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|93.9|5.6|0.5|0.7|6.8|57.1| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|82.5|15.7|1.8|1.9|19.3|82.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|93.8|5.7|0.6|0.7|6.9|58.4| |decode_asr_asr_model_valid.acc.ave/test_other|2939|52343|82.2|15.9|2.0|1.7|19.5|83.6| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.3|1.0|0.7|0.7|2.4|57.1| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.3|4.1|2.6|1.9|8.7|82.6| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98.3|1.0|0.7|0.6|2.3|58.4| |decode_asr_asr_model_valid.acc.ave/test_other|2939|272758|93.2|4.1|2.7|1.8|8.6|83.6| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr_char.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_conformer_lr2e-3_warmup15k_amp_nondeterministic_char ngpu: 1 seed: 2022 num_workers: 4 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false collect_stats: false write_collected_feats: false max_epoch: 70 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: true log_interval: 400 use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 1600000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_char_sp/train/speech_shape - exp/asr_stats_raw_en_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_en_char_sp/valid/speech_shape - exp/asr_stats_raw_en_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_clean_100_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_clean_100_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - <blank> - <unk> - <space> - E - T - A - O - N - I - H - S - R - D - L - U - M - C - W - F - G - Y - P - B - V - K - '''' - X - J - Q - Z - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: null model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 required: - output_dir - token_list version: 0.10.7a1 distributed: false ``` </details> ### 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 Yalta 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} } ``` 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 Yalta 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} } ```

347b403f78976954b51d74de03624d15

jonatasgrosman/exp_w2v2t_ru_vp-100k_s334

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ru']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

475

false

# exp_w2v2t_ru_vp-100k_s334 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](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.

0d3c0935b21cf203c8d5db47e991986c

ckauth/food-ner

ckauth

bert

8

6

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,407

false

# food-ner 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: 0.0092 - Validation Loss: 0.0323 - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1035, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0808 | 0.0284 | 0 | | 0.0193 | 0.0286 | 1 | | 0.0092 | 0.0323 | 2 | ### Framework versions - Transformers 4.25.1 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

1e136b5bcaa3b0ed8c44e511fe1713a9

tiedeman/opus-mt-en-he

tiedeman

marian

11

21

transformers

0

translation

true

false

apache-2.0

['en', 'he']

null

1

0

['translation']

false

true

2,011

false

### en-he * source group: English * target group: Hebrew * OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md) * model: transformer * source language(s): eng * target language(s): heb * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-10-04.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip) * test set translations: [opus-2020-10-04.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt) * test set scores: [opus-2020-10-04.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.eng.heb | 37.9 | 0.602 | ### System Info: - hf_name: en-he - source_languages: eng - target_languages: heb - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'he'] - src_constituents: ('English', {'eng'}) - tgt_constituents: ('Hebrew', {'heb'}) - src_multilingual: False - tgt_multilingual: False - long_pair: eng-heb - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-heb/opus-2020-10-04.test.txt - src_alpha3: eng - tgt_alpha3: heb - chrF2_score: 0.602 - bleu: 37.9 - brevity_penalty: 1.0 - ref_len: 60359.0 - src_name: English - tgt_name: Hebrew - train_date: 2020-10-04 00:00:00 - src_alpha2: en - tgt_alpha2: he - prefer_old: False - short_pair: en-he - helsinki_git_sha: 61fd6908b37d9a7b21cc3e27c1ae1fccedc97561 - transformers_git_sha: d99ed7ad618037ae878f0758157ed0764bd7f935 - port_machine: LM0-400-22516.local - port_time: 2020-10-15-16:31

894bd241162046a5df9625efef2d3e4d

jason1234/wav2vec2-large-xlsr-law

jason1234

wav2vec2

55

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,081

false

# wav2vec2-large-xlsr-law This model is a fine-tuned version of [ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt](https://huggingface.co/ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt) 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: 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 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0 - Datasets 1.13.3 - Tokenizers 0.10.3

c6c702a9e4d004190aadba4a9b580403

Helsinki-NLP/opus-mt-fr-mos

Helsinki-NLP

marian

10

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-fr-mos * source languages: fr * target languages: mos * OPUS readme: [fr-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-mos/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-mos/opus-2020-01-20.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.fr.mos | 21.1 | 0.353 |

c73d3e14dbf0f825454e332a483cf5f7

harmonai/honk-140k

harmonai

null

6

291

diffusers

1

null

false

mit

null

0

['audio-generation']

false

true

1,305

false

[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers. ## FP32 ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write model_id = "harmonai/honk-140k" pipe = DiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") audios = pipe(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ``` ## FP16 Faster at a small loss of quality ```python # !pip install diffusers[torch] accelerate scipy from diffusers import DiffusionPipeline from scipy.io.wavfile import write import torch model_id = "harmonai/honk-140k" pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") audios = pipeline(audio_length_in_s=4.0).audios # To save locally for i, audio in enumerate(audios): write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose()) # To dislay in google colab import IPython.display as ipd for audio in audios: display(ipd.Audio(audio, rate=pipe.unet.sample_rate)) ```

fa086d87ecb2eba39792ac91a8193966

dougtrajano/toxic-comment-classification

dougtrajano

bert

10

5

transformers

0

text-classification

true

false

apache-2.0

['pt']

null

0

['toxicity', 'portuguese', 'hate speech', 'offensive language', 'generated_from_trainer']

true

2,254

false

# dougtrajano/toxic-comment-classification Toxic Comment Classification is a model that detects if the text is toxic or not. This BERT model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the [OLID-BR dataset](https://huggingface.co/datasets/dougtrajano/olid-br). ## Overview **Input:** Text in Brazilian Portuguese **Output:** Binary classification (toxic or not toxic) ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dougtrajano/toxic-comment-classification") model = AutoModelForSequenceClassification.from_pretrained("dougtrajano/toxic-comment-classification") ``` ## Limitations and bias The following factors may degrade the model’s performance. **Text Language**: The model was trained on Brazilian Portuguese texts, so it may not work well with Portuguese dialects. **Text Origin**: The model was trained on texts from social media and a few texts from other sources, so it may not work well on other types of texts. ## Trade-offs Sometimes models exhibit performance issues under particular circumstances. In this section, we'll discuss situations in which you might discover that the model performs less than optimally, and should plan accordingly. **Text Length**: The model was fine-tuned on texts with a word count between 1 and 178 words (average of 18 words). It may give poor results on texts with a word count outside this range. ## Performance The model was evaluated on the test set of the [OLID-BR](https://dougtrajano.github.io/olid-br/) dataset. **Accuracy:** 0.8578 **Precision:** 0.8594 **Recall:** 0.8578 **F1-Score:** 0.8580 | Class | Precision | Recall | F1-Score | Support | | :---: | :-------: | :----: | :------: | :-----: | | `NOT-OFFENSIVE` | 0.8886 | 0.8490 | 0.8683 | 1,775 | | `OFFENSIVE` | 0.8233 | 0.8686 | 0.8453 | 1,438 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.255788747459486e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1993 - optimizer: Adam with betas=(0.8445637934160373,0.8338816842140165) and epsilon=2.527092625455385e-08 - lr_scheduler_type: linear - num_epochs: 30 - label_smoothing_factor: 0.07158711257743958 ### Framework versions - Transformers 4.26.0 - Pytorch 1.10.2+cu113 - Datasets 2.9.0 - Tokenizers 0.13.2 ## Provide Feedback If you have any feedback on this model, please [open an issue](https://github.com/DougTrajano/ToChiquinho/issues/new) on GitHub.

582baa744532773d1e8d6c408a7d2f63

ThePioneer/UnknownMix

ThePioneer

null

13

0

null

0

null

false

other

null

0

[]

false

true

5,297

false

3f38dfd395e85b57be38dbbf6a399fa9

luke-thorburn/suggest-objections-bias-only

luke-thorburn

gpt_neo

4

transformers

0

text-generation

true

false

apache-2.0

['en']

null

0

['argumentation']

false

true

1,716

false

# Generate objections to a claim This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating the objections to a claim, optionally given some example objections to that claim. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks. Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review. # Prompt Template ``` List objections to the claim that: [original claim] Objections: * [objection 1] * [objection 2] ... * [objection n] * [generated objection] ``` # Dataset The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform [Kialo](https://kialo.com/). # Limitations and Biases The model is a finetuned version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon. # Acknowledgements This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.

01a978089503b36959761ee51b3a0f81

PlanTL-GOB-ES/bsc-bio-ehr-es

PlanTL-GOB-ES

roberta

11

888

transformers

4

fill-mask

true

false

apache-2.0

['es']

null

0

['biomedical', 'clinical', 'ehr', 'spanish']

false

true

14,406

false

# Biomedical-clinical language model for Spanish ## Table of contents <details> <summary>Click to expand</summary> - [Model description](#model-description) - [Intended uses and limitations](#intended-use) - [How to use](#how-to-use) - [Limitations and bias](#limitations-and-bias) - [Training](#training) - [Evaluation](#evaluation) - [Additional information](#additional-information) - [Author](#author) - [Contact information](#contact-information) - [Copyright](#copyright) - [Licensing information](#licensing-information) - [Funding](#funding) - [Citing information](#citing-information) - [Disclaimer](#disclaimer) </details> ## Model description Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). ## Intended uses and limitations The model is ready-to-use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine-tuned on downstream tasks such as Named Entity Recognition or Text Classification. ## How to use ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Tokenization and model pretraining This model is a [RoBERTa-based](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model trained on a **biomedical-clinical** corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of [Byte-Pair Encoding (BPE)](https://github.com/openai/gpt-2) used in the original [RoBERTA](https://github.com/pytorch/fairseq/tree/master/examples/roberta) model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences. ### Training corpora and preprocessing The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers, and a real-world clinical corpus collected from more than 278K clinical documents and notes. To obtain a high-quality training corpus while retaining the idiosyncrasies of the clinical language, a cleaning pipeline has been applied only to the biomedical corpora, keeping the clinical corpus uncleaned. Essentially, the cleaning operations used are: - data parsing in different formats - sentence splitting - language detection - filtering of ill-formed sentences - deduplication of repetitive contents - keep the original document boundaries Then, the biomedical corpora are concatenated and further global deduplication among the biomedical corpora has been applied. Eventually, the clinical corpus is concatenated to the cleaned biomedical corpus resulting in a medium-size biomedical-clinical corpus for Spanish composed of more than 1B tokens. The table below shows some basic statistics of the individual cleaned corpora: | Name | No. tokens | Description | |-----------------------------------------------------------------------------------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Medical crawler](https://zenodo.org/record/4561970) | 903,558,13 | Crawler of more than 3,000 URLs belonging to Spanish biomedical and health domains. | | Clinical cases misc. | 102,855,267 | A miscellany of medical content, essentially clinical cases. Note that a clinical case report is a scientific publication where medical practitioners share patient cases and it is different from a clinical note or document. | | EHR documents | 95,267,20 | Collection of more than 278K clinical documents, including discharge reports, clinical course notes and X-ray reports, for a total of 91M tokens. | | [Scielo](https://zenodo.org/record/2541681#.YlP1DshBwio) | 60,007,289 | Publications written in Spanish crawled from the Spanish SciELO server in 2017. | | [BARR2_background](https://temu.bsc.es/BARR2/downloads/background_set.raw_text.tar.bz2) | 24,516,442 | Biomedical Abbreviation Recognition and Resolution (BARR2) containing Spanish clinical case study sections from a variety of clinical disciplines. | | Wikipedia_life_sciences | 13,890,501 | Wikipedia articles crawled 04/01/2021 with the [Wikipedia API python library](https://pypi.org/project/Wikipedia-API/) starting from the "Ciencias\_de\_la\_vida" category up to a maximum of 5 subcategories. Multiple links to the same articles are then discarded to avoid repeating content. | | Patents | 13,463,387 | Google Patent in Medical Domain for Spain (Spanish). The accepted codes (Medical Domain) for Json files of patents are: "A61B", "A61C","A61F", "A61H", "A61K", "A61L","A61M", "A61B", "A61P". | | [EMEA](http://opus.nlpl.eu/download.php?f=EMEA/v3/moses/en-es.txt.zip) | 5,377,448 | Spanish-side documents extracted from parallel corpora made out of PDF documents from the European Medicines Agency. | | [mespen_Medline](https://zenodo.org/record/3562536#.YTt1fH2xXbR) | 4,166,077 | Spanish-side articles extracted from a collection of Spanish-English parallel corpus consisting of biomedical scientific literature. The collection of parallel resources is aggregated from the MedlinePlus source. | | PubMed | 1,858,966 | Open-access articles from the PubMed repository crawled in 2017. | ## Evaluation The model has been fine-tuned on three Named Entity Recognition (NER) tasks using three clinical NER datasets: - [PharmaCoNER](https://zenodo.org/record/4270158): is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/). - [CANTEMIST](https://zenodo.org/record/3978041#.YTt5qH2xXbQ): is a shared task specifically focusing on named entity recognition of tumor morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ). - ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables. We addressed the NER task as a token classification problem using a standard linear layer along with the BIO tagging schema. We compared our models with the general-domain Spanish [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne), the general-domain multilingual model that supports Spanish [mBERT](https://huggingface.co/bert-base-multilingual-cased), the domain-specific English model [BioBERT](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2), and three domain-specific models based on continual pre-training, [mBERT-Galén](https://ieeexplore.ieee.org/document/9430499), [XLM-R-Galén](https://ieeexplore.ieee.org/document/9430499) and [BETO-Galén](https://ieeexplore.ieee.org/document/9430499). The table below shows the F1 scores obtained: | Tasks/Models | bsc-bio-ehr-es | XLM-R-Galén | BETO-Galén | mBERT-Galén | mBERT | BioBERT | roberta-base-bne | |--------------|----------------|--------------------|--------------|--------------|--------------|--------------|------------------| | PharmaCoNER | **0.8913** | 0.8754 | 0.8537 | 0.8594 | 0.8671 | 0.8545 | 0.8474 | | CANTEMIST | **0.8340** | 0.8078 | 0.8153 | 0.8168 | 0.8116 | 0.8070 | 0.7875 | | ICTUSnet | **0.8756** | 0.8716 | 0.8498 | 0.8509 | 0.8631 | 0.8521 | 0.8677 | The fine-tuning scripts can be found in the official GitHub [repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). ## Additional information ### Author Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es) ### Contact information For further information, send an email to <plantl-gob-es@bsc.es> ### Copyright Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) ### Licensing information [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. ### Citing information If you use these models, please cite our work: ```bibtext @inproceedings{carrino-etal-2022-pretrained, title = "Pretrained Biomedical Language Models for Clinical {NLP} in {S}panish", author = "Carrino, Casimiro Pio and Llop, Joan and P{\`a}mies, Marc and Guti{\'e}rrez-Fandi{\~n}o, Asier and Armengol-Estap{\'e}, Jordi and Silveira-Ocampo, Joaqu{\'\i}n and Valencia, Alfonso and Gonzalez-Agirre, Aitor and Villegas, Marta", booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing", month = may, year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.bionlp-1.19", doi = "10.18653/v1/2022.bionlp-1.19", pages = "193--199", abstract = "This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.", } ``` ### Disclaimer <details> <summary>Click to expand</summary> The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions. When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner of the models (SEDIA – State Secretariat for Digitalization and Artificial Intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models. Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables. Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial. En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos. </details>

5200464acc7ad2a1a61c7c24884b4faf

arun100/whisper-medium-vi-2

arun100

whisper

24

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['vi']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,693

false

# Whisper Medium Vietnamese This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 vi dataset. It achieves the following results on the evaluation set: - Loss: 0.5686 - Wer: 18.8638 ## 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: 16 - 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: 1400 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0063 | 12.01 | 200 | 0.5238 | 19.2915 | | 0.0046 | 24.01 | 400 | 0.5686 | 18.8638 | | 0.0067 | 37.01 | 600 | 0.5924 | 20.6076 | | 0.0004 | 49.01 | 800 | 0.6239 | 19.8070 | | 0.0005 | 62.01 | 1000 | 0.6354 | 19.7631 | | 0.0001 | 74.01 | 1200 | 0.6447 | 19.5547 | | 0.0001 | 87.01 | 1400 | 0.6473 | 19.5547 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

1a27ef4473161daec92bb6d166cd49d7

rdyzakya/bert-indo-base-stance-cls

rdyzakya

bert

12

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

5,261

false

# bert-indo-base-stance-cls This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0156 - Accuracy: 0.6892 - Precision: 0.6848 - Recall: 0.6892 - F1: 0.6859 - Against: {'precision': 0.6185567010309279, 'recall': 0.5555555555555556, 'f1-score': 0.5853658536585366, 'support': 216} - For: {'precision': 0.7280453257790368, 'recall': 0.7764350453172205, 'f1-score': 0.7514619883040935, 'support': 331} ## 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 - 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 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Against | For | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-----------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 137 | 0.6423 | 0.6581 | 0.6894 | 0.6581 | 0.5917 | {'precision': 0.7543859649122807, 'recall': 0.19907407407407407, 'f1-score': 0.31501831501831506, 'support': 216} | {'precision': 0.6469387755102041, 'recall': 0.9577039274924471, 'f1-score': 0.7722289890377587, 'support': 331} | | No log | 2.0 | 274 | 0.6146 | 0.6600 | 0.6691 | 0.6600 | 0.6628 | {'precision': 0.5614754098360656, 'recall': 0.6342592592592593, 'f1-score': 0.5956521739130436, 'support': 216} | {'precision': 0.7392739273927392, 'recall': 0.676737160120846, 'f1-score': 0.7066246056782334, 'support': 331} | | No log | 3.0 | 411 | 0.7572 | 0.6545 | 0.6734 | 0.6545 | 0.6583 | {'precision': 0.550561797752809, 'recall': 0.6805555555555556, 'f1-score': 0.608695652173913, 'support': 216} | {'precision': 0.7535714285714286, 'recall': 0.6374622356495468, 'f1-score': 0.6906710310965631, 'support': 331} | | 0.4855 | 4.0 | 548 | 0.7405 | 0.6892 | 0.6842 | 0.6892 | 0.6851 | {'precision': 0.6210526315789474, 'recall': 0.5462962962962963, 'f1-score': 0.5812807881773399, 'support': 216} | {'precision': 0.7254901960784313, 'recall': 0.7824773413897281, 'f1-score': 0.7529069767441859, 'support': 331} | | 0.4855 | 5.0 | 685 | 1.1222 | 0.6856 | 0.6828 | 0.6856 | 0.6839 | {'precision': 0.6078431372549019, 'recall': 0.5740740740740741, 'f1-score': 0.5904761904761905, 'support': 216} | {'precision': 0.7317784256559767, 'recall': 0.7583081570996979, 'f1-score': 0.7448071216617211, 'support': 331} | | 0.4855 | 6.0 | 822 | 1.4960 | 0.6892 | 0.6830 | 0.6892 | 0.6827 | {'precision': 0.6292134831460674, 'recall': 0.5185185185185185, 'f1-score': 0.5685279187817258, 'support': 216} | {'precision': 0.7181571815718157, 'recall': 0.8006042296072508, 'f1-score': 0.7571428571428572, 'support': 331} | | 0.4855 | 7.0 | 959 | 1.6304 | 0.6801 | 0.6886 | 0.6801 | 0.6827 | {'precision': 0.5843621399176955, 'recall': 0.6574074074074074, 'f1-score': 0.6187363834422658, 'support': 216} | {'precision': 0.756578947368421, 'recall': 0.6948640483383686, 'f1-score': 0.7244094488188976, 'support': 331} | | 0.1029 | 8.0 | 1096 | 1.8381 | 0.6673 | 0.6727 | 0.6673 | 0.6693 | {'precision': 0.5726495726495726, 'recall': 0.6203703703703703, 'f1-score': 0.5955555555555555, 'support': 216} | {'precision': 0.7380191693290735, 'recall': 0.6978851963746223, 'f1-score': 0.717391304347826, 'support': 331} | | 0.1029 | 9.0 | 1233 | 1.9474 | 0.6929 | 0.6876 | 0.6929 | 0.6881 | {'precision': 0.6290322580645161, 'recall': 0.5416666666666666, 'f1-score': 0.582089552238806, 'support': 216} | {'precision': 0.7257617728531855, 'recall': 0.7915407854984894, 'f1-score': 0.7572254335260115, 'support': 331} | | 0.1029 | 10.0 | 1370 | 2.0156 | 0.6892 | 0.6848 | 0.6892 | 0.6859 | {'precision': 0.6185567010309279, 'recall': 0.5555555555555556, 'f1-score': 0.5853658536585366, 'support': 216} | {'precision': 0.7280453257790368, 'recall': 0.7764350453172205, 'f1-score': 0.7514619883040935, 'support': 331} | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

b34cce50c91d0435746c3e6225ab0de5

dumitrescustefan/t5-v1_1-base-romanian

dumitrescustefan

mt5

9

12

transformers

0

text2text-generation

true

false

true

apache-2.0

['ro']

null

0

[]

false

true

2,177

false

This is a pretrained-from-scratch **T5v1.1 base** model (**247M** parameters) on the [t5x](https://github.com/google-research/t5x) platform. Training was performed on a clean 80GB Romanian text corpus for 4M steps with these [scripts](https://github.com/dumitrescustefan/t5x_models). The model was trained with an encoder sequence length of 512 and a decoder sequence length of 256. **!! IMPORTANT !!** This model was pretrained on the span corruption MLM task, meaning this model is **not usable** in any downstream task **without finetuning** first! ### How to load a t5x model ```python from transformers import T5Tokenizer, T5Model tokenizer = T5Tokenizer.from_pretrained('dumitrescustefan/t5-v1_1-base-romanian') model = T5Model.from_pretrained('dumitrescustefan/t5-v1_1-base-romanian') input_ids = tokenizer("Acesta este un test", return_tensors="pt").input_ids # Batch size 1 decoder_input_ids = tokenizer("Acesta este", return_tensors="pt").input_ids # Batch size 1 # preprocess: Prepend decoder_input_ids with start token which is pad token for T5Model. # This is not needed for torch's T5ForConditionalGeneration as it does this internally using labels arg. decoder_input_ids = model._shift_right(decoder_input_ids) # forward pass outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) last_hidden_states = outputs.last_hidden_state print(last_hidden_states.shape) # this will print [1, 3, 768] ``` Remember to always sanitize your text! Replace ``ş`` and ``ţ`` cedilla-letters to comma-letters with : ```python text = text.replace("ţ", "ț").replace("ş", "ș").replace("Ţ", "Ț").replace("Ş", "Ș") ``` because the model was **not** trained on cedilla ``ş`` and ``ţ``s. If you don't, you will have decreased performance due to ``<UNK>``s and increased number of tokens per word. ### Acknowledgements We'd like to thank [TPU Research Cloud](https://sites.research.google/trc/about/) for providing the TPUv4 cores we used to train these models! ### Authors Yours truly, _[Stefan Dumitrescu](https://github.com/dumitrescustefan), [Mihai Ilie](https://github.com/iliemihai) and [Per Egil Kummervold](https://huggingface.co/north)_

7575f7c91a4a842f2b3a979a0b7673af

xysmalobia/sequence_classification

xysmalobia

bert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

['glue']

null

0

['generated_from_trainer']

true

1,388

false

# sequence_classification This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7738 - Accuracy: 0.8529 - F1: 0.8944 ## 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: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3519 | 0.8627 | 0.9 | | 0.4872 | 2.0 | 918 | 0.6387 | 0.8333 | 0.8893 | | 0.2488 | 3.0 | 1377 | 0.7738 | 0.8529 | 0.8944 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3

0b0d9a6a04717756902d99210df94094

jonathang/pprotein-thing

jonathang

null

17

26

diffusers

20

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', 'science']

false

true

2,523

false

# DreamBooth model for the pprotein concept trained by jonathang on the jonathang/dreambooth-hackathon-images-protein3 dataset. This is a Stable Diffusion model fine-tuned on the pprotein concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a 3d model of pprotein** 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! ## Examples <table> <tr> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Kandinsky"</td> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Van Gogh"</td> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Warhol"</td> </tr> <tr> <td align="center"><img src="https://imgur.com/lhDA041.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/iug4k7D.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/eIMiTVG.png" style="height:200px"> </td> </tr> <tr> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Leonardo da Vinci"</td> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Frida Kahlo"</td> <td>Generated Image of "a 3d diagram of pprotein in the style of "<br>"Salvador Dahli"</td> </tr> <tr> <td align="center"><img src="https://imgur.com/hzKGWC2.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/loc8rLa.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/8nK81TA.png" style="height:200px"> </td> </tr> <tr> <td>Generated Image of "Tree in the style of"<br>"3d diagram of pprotein"</td> <td>Generated Image of "Soda Can in the style of"<br>"3d diagram of pprotein"</td> <td>Generated Image of "Vase in the style of"<br>"3d diagram of pprotein"</td> </tr> <tr> <td align="center"><img src="https://imgur.com/czOlY11.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/uhwueGs.png" style="height:200px"> </td> <td align="center"><img src="https://imgur.com/gSIrHAh.png" style="height:200px"> </td> </tr> </table> ## Description This is a Stable Diffusion model fine-tuned on `thing` images for the science theme. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('jonathang/pprotein-thing') image = pipeline().images[0] image ```

de89c3e764f9b5d8b2460ab132acf9d2

yuhuizhang/finetuned_gpt2_sst2_negation0.8_pretrainedFalse

yuhuizhang

gpt2

11

2

transformers

0

text-generation

true

false

mit

null

['sst2']

null

0

['generated_from_trainer']

true

1,246

false

# finetuned_gpt2_sst2_negation0.8_pretrainedFalse This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 5.2177 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 4.7474 | 1.0 | 1111 | 5.4543 | | 4.378 | 2.0 | 2222 | 5.2688 | | 4.2047 | 3.0 | 3333 | 5.2177 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.13.1+cu117 - Datasets 2.5.2 - Tokenizers 0.12.1

e2c02d0c0cadbc0b2bd53be2b420f117

doc2query/msmarco-hindi-mt5-base-v1

doc2query

mt5

10

378

transformers

1

text2text-generation

true

false

apache-2.0

['hi']

['unicamp-dl/mmarco']

null

0

[]

false

true

3,825

false

# doc2query/msmarco-hindi-mt5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. In our [GPL-Paper](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM import torch model_name = 'doc2query/msmarco-hindi-mt5-base-v1' tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) text = "पाइथन एक सामान्य कार्यों के लिए उपयुक्त, उच्च स्तरीय प्रोग्रामिंग भाषा (General Purpose and High Level Programming language), इन्टरैक्टिव, ऑब्जेक्ट ओरिएन्टेड, स्क्रिप्टिंग भाषा है। इस भाषा को इस तरह से डिजाइन किया गया है ताकि इसमें लिखे गए कोड आसानी से पढ़े और समझे जा सकें।" def create_queries(para): input_ids = tokenizer.encode(para, return_tensors='pt') with torch.no_grad(): # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality sampling_outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, top_k=10, num_return_sequences=5 ) # Here we use Beam-search. It generates better quality queries, but with less diversity beam_outputs = model.generate( input_ids=input_ids, max_length=64, num_beams=5, no_repeat_ngram_size=2, num_return_sequences=5, early_stopping=True ) print("Paragraph:") print(para) print("\nBeam Outputs:") for i in range(len(beam_outputs)): query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') print("\nSampling Outputs:") for i in range(len(sampling_outputs)): query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') create_queries(text) ``` **Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it. ## Training This model fine-tuned [google/mt5-base](https://huggingface.co/google/mt5-base) for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the `train_script.py` in this repository. The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces. This model was trained on a (query, passage) from the [mMARCO dataset](https://github.com/unicamp-dl/mMARCO).

c85c489ac759a2fef66a889d592faa10

salesken/paraphrase_generation

salesken

gpt2

10

37

transformers

2

text-generation

true

false

true

apache-2.0

['en']

null

0

salesken

false

true

1,448

false

Use this model to generate variations to augment the training data used for NLU systems. ```python from transformers import AutoTokenizer, AutoModelWithLMHead import torch if torch.cuda.is_available(): device = torch.device("cuda") else : device = "cpu" tokenizer = AutoTokenizer.from_pretrained("salesken/paraphrase_generation") model = AutoModelWithLMHead.from_pretrained("salesken/paraphrase_generation").to(device) input_query="every moment is a fresh beginning" query= input_query + " ~~ " input_ids = tokenizer.encode(query.lower(), return_tensors='pt').to(device) sample_outputs = model.generate(input_ids, do_sample=True, num_beams=1, max_length=128, temperature=0.9, top_p= 0.99, top_k = 30, num_return_sequences=40) paraphrases = [] for i in range(len(sample_outputs)): r = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split('||')[0] r = r.split(' ~~ ')[1] if r not in paraphrases: paraphrases.append(r) print(paraphrases) ``` To evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation & rank the generated paraphrases, use the following model: https://huggingface.co/salesken/paraphrase_diversity_ranker

b8be1dcda5fd12f0f400212cc2ca7969

fathyshalab/all-roberta-large-v1-utility-7-16-5

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-utility-7-16-5 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: 2.3728 - Accuracy: 0.3956 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.8194 | 1.0 | 1 | 2.6027 | 0.3156 | | 2.2337 | 2.0 | 2 | 2.5079 | 0.3778 | | 1.7996 | 3.0 | 3 | 2.4293 | 0.3822 | | 1.4591 | 4.0 | 4 | 2.3728 | 0.3956 | | 1.3205 | 5.0 | 5 | 2.3439 | 0.3956 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

348776fadc344d75e41738be624b9f62

nsridhar/roberta-finetuned-country

nsridhar

roberta

13

10

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

982

false

# roberta-finetuned-country This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.2

db6c8e0d44264ed4b4f5f7b2b5c4686e

yogeshchandrasekharuni/bart-paraphrase-finetuned-xsum-v2

yogeshchandrasekharuni

bart

15

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

3,414

false

# bart-paraphrase-finetuned-xsum-v2 This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2329 - Rouge1: 100.0 - Rouge2: 100.0 - Rougel: 100.0 - Rougelsum: 100.0 - Gen Len: 9.2619 ## 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: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 21 | 1.2954 | 66.7012 | 60.8612 | 66.5163 | 66.4352 | 13.2857 | | No log | 2.0 | 42 | 0.6866 | 86.8284 | 82.7835 | 86.7208 | 86.784 | 9.5238 | | No log | 3.0 | 63 | 0.4652 | 95.1892 | 93.5619 | 95.2567 | 95.1657 | 10.3095 | | No log | 4.0 | 84 | 0.4280 | 97.7463 | 97.1782 | 97.8708 | 97.718 | 9.5 | | No log | 5.0 | 105 | 0.3712 | 99.6435 | 99.5767 | 99.6435 | 99.6435 | 9.3571 | | No log | 6.0 | 126 | 0.4451 | 99.2695 | 98.9418 | 99.1883 | 99.3506 | 9.3095 | | No log | 7.0 | 147 | 0.3169 | 99.246 | 99.0232 | 99.246 | 99.4048 | 9.619 | | No log | 8.0 | 168 | 0.2942 | 100.0 | 100.0 | 100.0 | 100.0 | 9.4048 | | No log | 9.0 | 189 | 0.3105 | 100.0 | 100.0 | 100.0 | 100.0 | 9.1667 | | No log | 10.0 | 210 | 0.3035 | 100.0 | 100.0 | 100.0 | 100.0 | 9.2619 | | No log | 11.0 | 231 | 0.2983 | 100.0 | 100.0 | 100.0 | 100.0 | 10.5714 | | No log | 12.0 | 252 | 0.2497 | 100.0 | 100.0 | 100.0 | 100.0 | 9.4286 | | No log | 13.0 | 273 | 0.2911 | 100.0 | 100.0 | 100.0 | 100.0 | 9.1667 | | No log | 14.0 | 294 | 0.2619 | 100.0 | 100.0 | 100.0 | 100.0 | 9.2143 | | No log | 15.0 | 315 | 0.2510 | 100.0 | 100.0 | 100.0 | 100.0 | 9.2381 | | No log | 16.0 | 336 | 0.2647 | 100.0 | 100.0 | 100.0 | 100.0 | 9.9048 | | No log | 17.0 | 357 | 0.2438 | 100.0 | 100.0 | 100.0 | 100.0 | 9.2143 | | No log | 18.0 | 378 | 0.2324 | 100.0 | 100.0 | 100.0 | 100.0 | 9.3095 | | No log | 19.0 | 399 | 0.2296 | 100.0 | 100.0 | 100.0 | 100.0 | 9.3095 | | No log | 20.0 | 420 | 0.2329 | 100.0 | 100.0 | 100.0 | 100.0 | 9.2619 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1

c3e23b1e1c57bb90cd3febe2ad98bba2

StonyBrookNLP/bart-large-iirc-gold

StonyBrookNLP

bart

9

3

transformers

0

text2text-generation

true

false

cc-by-4.0

null

0

['question-answering, multi-step-reasoning, multi-hop-reasoning']

false

true

2,624

false

# What's this? This is one of the models reported in the paper: ["Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts".](https://arxiv.org/abs/2205.12496). This paper proposes a procedure to synthetically generate a QA dataset, TeaBReaC, for pretraining language models for robust multi-step reasoning. Pretraining plain LMs like Bart, T5 and numerate LMs like NT5, PReasM, POET on TeaBReaC leads to improvemed downstream performance on several multi-step QA datasets. Please checkout out the paper for the details. We release the following models: - **A:** Base Models finetuned on target datasets: `{base_model}-{target_dataset}` - **B:** Base models pretrained on TeaBReaC: `teabreac-{base_model}` - **C:** Base models pretrained on TeaBReaC and then finetuned on target datasets: `teabreac-{base_model}-{target_dataset}` The `base_model` above can be from: `bart-large`, `t5-large`, `t5-3b`, `nt5-small`, `preasm-large`. The `target_dataset` above can be from: `drop`, `tatqa`, `iirc-gold`, `iirc-retrieved`, `numglue`. The **A** models are only released for completeness / reproducibility. In your end application you probably just want to use either **B** or **C**. # How to use it? Please checkout the details in our [github repository](https://github.com/stonybrooknlp/teabreac), but in a nutshell: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from digit_tokenization import enable_digit_tokenization # digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/bart-large-iirc-gold" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) # Fast doesn't work with digit tokenization model = AutoModelForSeq2SeqLM.from_pretrained(model_name) enable_digit_tokenization(tokenizer) input_texts = [ "answer_me: Who scored the first touchdown of the game?" + "context: ... Oakland would get the early lead in the first quarter as quarterback JaMarcus Russell completed a 20-yard touchdown pass to rookie wide receiver Chaz Schilens..." # Note: some models have slightly different qn/ctxt format. See the github repo. ] input_ids = tokenizer( input_texts, return_tensors="pt", truncation=True, max_length=800, add_special_tokens=True, padding=True, )["input_ids"] generated_ids = model.generate(input_ids, min_length=1, max_length=50) generated_predictions = tokenizer.batch_decode(generated_ids, skip_special_tokens=False) generated_predictions = [ tokenizer.fix_decoded_text(generated_prediction) for generated_prediction in generated_predictions ] # => ["Chaz Schilens"] ```

1bb3efb2b07e0715480c6cb485408a9f

mrm8488/xlm-roberta-base-finetuned-HC3-mix

mrm8488

xlm-roberta

11

90

transformers

2

text-classification

true

false

openrail

['multilingual', 'af', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'id', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', False, 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sa', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'xh', 'yi']

['Hello-SimpleAI/HC3']

null

0

[]

false

true

1,590

false

# XLM-RoBERTa (base) fine-tuned on HC3 for ChatGPT text detection **XLM-RoBERTa** (base) fine-tuned on [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI) **HC3** corpus for **ChatGPT** text detection. All credit to [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI) for their huge work! ## F1 score on test dataset: 0.9736 ## The model XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository. ## The dataset #### Human ChatGPT Comparison Corpus (HC3) The first human-ChatGPT comparison corpus, named **HC3** dataset by [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI) This dataset is introduced in the paper: - Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597) ## Metrics |metric| value| |------|------| |F1 |0.9736| ## Usage ```py from transformers import pipeline ckpt = "mrm8488/xlm-roberta-base-finetuned-HC3-mix" detector = pipeline('text-classification', model=ckpt) text = "Here your text..." result = detector(text) print(result) ``` ## Citation ``` @misc {manuel_romero_2023, author = { {Manuel Romero} }, title = { xlm-roberta-base-finetuned-HC3-mix (Revision b18de48) }, year = 2023, url = { https://huggingface.co/mrm8488/xlm-roberta-base-finetuned-HC3-mix }, doi = { 10.57967/hf/0306 }, publisher = { Hugging Face } } ```

6933f83e15e59fc4d42c6e350aa619c1

jayantapaul888/twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes

jayantapaul888

deberta

20

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,876

false

# twitter-data-microsoft-deberta-base-mnli-sentiment-finetuned-memes This model is a fine-tuned version of [microsoft/deberta-base-mnli](https://huggingface.co/microsoft/deberta-base-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2438 - Accuracy: 0.9296 - Precision: 0.9301 - Recall: 0.9296 - F1: 0.9296 ## 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: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.3622 | 1.0 | 1762 | 0.2933 | 0.9060 | 0.9065 | 0.9060 | 0.9057 | | 0.2601 | 2.0 | 3524 | 0.2593 | 0.9194 | 0.9196 | 0.9194 | 0.9192 | | 0.2282 | 3.0 | 5286 | 0.2365 | 0.9279 | 0.9287 | 0.9279 | 0.9280 | | 0.1977 | 4.0 | 7048 | 0.2325 | 0.9293 | 0.9298 | 0.9293 | 0.9293 | | 0.181 | 5.0 | 8810 | 0.2421 | 0.9291 | 0.9301 | 0.9291 | 0.9292 | | 0.1629 | 6.0 | 10572 | 0.2438 | 0.9296 | 0.9301 | 0.9296 | 0.9296 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

5855b9ff259072247d51c2df9139a592

innovation64/flyfood-pet-heywhale

innovation64

null

17

29

diffusers

0

text-to-image

true

false

creativeml-openrail-m

null

1

0

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

false

true

806

false

# DreamBooth model for the flyfood concept trained by innovation64. This is a Stable Diffusion model fine-tuned on the flyfood concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of flyfood pet** 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 `派蒙` images for the wildcard theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('innovation64/flyfood-pet-heywhale') image = pipeline().images[0] image ```

6e351eb7f9069ca934d2cbebac25be0b

jpabbuehl/distilbert-base-uncased-finetuned-cola

jpabbuehl

distilbert

13

4

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: 0.7588 - Matthews Correlation: 0.5230 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5261 | 1.0 | 535 | 0.5125 | 0.4124 | | 0.3502 | 2.0 | 1070 | 0.5439 | 0.5076 | | 0.2378 | 3.0 | 1605 | 0.6629 | 0.4946 | | 0.1809 | 4.0 | 2140 | 0.7588 | 0.5230 | | 0.1309 | 5.0 | 2675 | 0.8901 | 0.5056 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

9f06b7b15bae62f85799f05731a84a15

jed351/gpt2_tiny_zh-hk-shikoto

jed351

gpt2

16

5

transformers

0

text-generation

true

false

openrail

null

['jed351/shikoto_zh_hk']

null

0

['generated_from_trainer']

true

2,251

false

# gpt2-shikoto This model was trained on a dataset I obtained from an online novel site. **Please be aware that the stories (training data) might contain inappropriate content. This model is intended for research purposes only.** The base model can be found [here](https://huggingface.co/jed351/gpt2-tiny-zh-hk), which was obtained by patching a [GPT2 Chinese model](https://huggingface.co/ckiplab/gpt2-tiny-chinese) and its tokenizer with Cantonese characters. Refer to the base model for info on the patching process. ## Training procedure Please refer to the [script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) provided by Huggingface. The model was trained for 400,000 steps on 2 NVIDIA Quadro RTX6000 for around 15 hours at the Research Computing Services of Imperial College London. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 20 - eval_batch_size: 20 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 40 - total_eval_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400000 - mixed_precision_training: Native AMP ### Training results ### How to use it? ``` from transformers import AutoTokenizer from transformers import TextGenerationPipeline, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-tiny-zh-hk") model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_tiny_zh-hk-shikoto") # try messing around with the parameters generator = TextGenerationPipeline(model, tokenizer, max_new_tokens=200, no_repeat_ngram_size=3) #, device=0) #if you have a GPU input_string = "your input" output = generator(input_string) string = output[0]['generated_text'].replace(' ', '') print(string) ``` ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2

df0af9a12c709e7d4190a5bb0a973bd9

emmyapi/distilbart-podimo-data-eval-2

emmyapi

bart

13

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,203

false

# distilbart-podimo-data-eval-2 This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.5823 - Rouge1: 34.3971 - Rouge2: 7.95 - Rougel: 18.7271 - Rougelsum: 30.9024 - Gen Len: 131.919 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:| | 4.1512 | 0.98 | 44 | 3.7806 | 32.727 | 6.5788 | 17.5196 | 29.3777 | 137.2905 | | 3.6342 | 1.98 | 88 | 3.6421 | 32.709 | 6.7877 | 17.8668 | 29.4636 | 134.6648 | | 3.3512 | 2.98 | 132 | 3.5819 | 33.5128 | 7.519 | 18.6614 | 30.1142 | 132.2961 | | 3.141 | 3.98 | 176 | 3.5552 | 33.4795 | 7.3242 | 18.396 | 30.0854 | 132.757 | | 2.9787 | 4.98 | 220 | 3.5583 | 33.5862 | 7.391 | 18.3568 | 30.2461 | 132.4078 | | 2.8555 | 5.98 | 264 | 3.5650 | 34.1111 | 7.8008 | 18.7159 | 30.6055 | 131.3603 | | 2.7648 | 6.98 | 308 | 3.5729 | 34.0981 | 7.6556 | 18.6373 | 30.6269 | 131.2821 | | 2.6645 | 7.98 | 352 | 3.5823 | 34.3971 | 7.95 | 18.7271 | 30.9024 | 131.919 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.11.0 - Datasets 2.2.1 - Tokenizers 0.12.1

9be1dd95ea3d7335080e421c28555923

AndrewMcDowell/wav2vec2-xls-r-300m-german-de

AndrewMcDowell

wav2vec2

28

8

transformers

2

automatic-speech-recognition

true

false

apache-2.0

['de']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'de', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event']

true

6,772

false

# This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. It achieves the following results on the evaluation set: - Loss: 0.1768 - Wer: 0.2016 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - 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: 2000 - num_epochs: 3.4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.7531 | 0.04 | 500 | 5.4564 | 1.0 | | 2.9882 | 0.08 | 1000 | 3.0041 | 1.0 | | 2.1953 | 0.13 | 1500 | 1.1723 | 0.7121 | | 1.2406 | 0.17 | 2000 | 0.3656 | 0.3623 | | 1.1294 | 0.21 | 2500 | 0.2843 | 0.2926 | | 1.0731 | 0.25 | 3000 | 0.2554 | 0.2664 | | 1.051 | 0.3 | 3500 | 0.2387 | 0.2535 | | 1.0479 | 0.34 | 4000 | 0.2345 | 0.2512 | | 1.0026 | 0.38 | 4500 | 0.2270 | 0.2452 | | 0.9921 | 0.42 | 5000 | 0.2212 | 0.2353 | | 0.9839 | 0.47 | 5500 | 0.2141 | 0.2330 | | 0.9907 | 0.51 | 6000 | 0.2122 | 0.2334 | | 0.9788 | 0.55 | 6500 | 0.2114 | 0.2270 | | 0.9687 | 0.59 | 7000 | 0.2066 | 0.2323 | | 0.9777 | 0.64 | 7500 | 0.2033 | 0.2237 | | 0.9476 | 0.68 | 8000 | 0.2020 | 0.2194 | | 0.9625 | 0.72 | 8500 | 0.1977 | 0.2191 | | 0.9497 | 0.76 | 9000 | 0.1976 | 0.2175 | | 0.9781 | 0.81 | 9500 | 0.1956 | 0.2159 | | 0.9552 | 0.85 | 10000 | 0.1958 | 0.2191 | | 0.9345 | 0.89 | 10500 | 0.1964 | 0.2158 | | 0.9528 | 0.93 | 11000 | 0.1926 | 0.2154 | | 0.9502 | 0.98 | 11500 | 0.1953 | 0.2149 | | 0.9358 | 1.02 | 12000 | 0.1927 | 0.2167 | | 0.941 | 1.06 | 12500 | 0.1901 | 0.2115 | | 0.9287 | 1.1 | 13000 | 0.1936 | 0.2090 | | 0.9491 | 1.15 | 13500 | 0.1900 | 0.2104 | | 0.9478 | 1.19 | 14000 | 0.1931 | 0.2120 | | 0.946 | 1.23 | 14500 | 0.1914 | 0.2134 | | 0.9499 | 1.27 | 15000 | 0.1931 | 0.2173 | | 0.9346 | 1.32 | 15500 | 0.1913 | 0.2105 | | 0.9509 | 1.36 | 16000 | 0.1902 | 0.2137 | | 0.9294 | 1.4 | 16500 | 0.1895 | 0.2086 | | 0.9418 | 1.44 | 17000 | 0.1913 | 0.2183 | | 0.9302 | 1.49 | 17500 | 0.1884 | 0.2114 | | 0.9418 | 1.53 | 18000 | 0.1894 | 0.2108 | | 0.9363 | 1.57 | 18500 | 0.1886 | 0.2132 | | 0.9338 | 1.61 | 19000 | 0.1856 | 0.2078 | | 0.9185 | 1.66 | 19500 | 0.1852 | 0.2056 | | 0.9216 | 1.7 | 20000 | 0.1874 | 0.2095 | | 0.9176 | 1.74 | 20500 | 0.1873 | 0.2078 | | 0.9288 | 1.78 | 21000 | 0.1865 | 0.2097 | | 0.9278 | 1.83 | 21500 | 0.1869 | 0.2100 | | 0.9295 | 1.87 | 22000 | 0.1878 | 0.2095 | | 0.9221 | 1.91 | 22500 | 0.1852 | 0.2121 | | 0.924 | 1.95 | 23000 | 0.1855 | 0.2042 | | 0.9104 | 2.0 | 23500 | 0.1858 | 0.2105 | | 0.9284 | 2.04 | 24000 | 0.1850 | 0.2080 | | 0.9162 | 2.08 | 24500 | 0.1839 | 0.2045 | | 0.9111 | 2.12 | 25000 | 0.1838 | 0.2080 | | 0.91 | 2.17 | 25500 | 0.1889 | 0.2106 | | 0.9152 | 2.21 | 26000 | 0.1856 | 0.2026 | | 0.9209 | 2.25 | 26500 | 0.1891 | 0.2133 | | 0.9094 | 2.29 | 27000 | 0.1857 | 0.2089 | | 0.9065 | 2.34 | 27500 | 0.1840 | 0.2052 | | 0.9156 | 2.38 | 28000 | 0.1833 | 0.2062 | | 0.8986 | 2.42 | 28500 | 0.1789 | 0.2001 | | 0.9045 | 2.46 | 29000 | 0.1769 | 0.2022 | | 0.9039 | 2.51 | 29500 | 0.1819 | 0.2073 | | 0.9145 | 2.55 | 30000 | 0.1828 | 0.2063 | | 0.9081 | 2.59 | 30500 | 0.1811 | 0.2049 | | 0.9252 | 2.63 | 31000 | 0.1833 | 0.2086 | | 0.8957 | 2.68 | 31500 | 0.1795 | 0.2083 | | 0.891 | 2.72 | 32000 | 0.1809 | 0.2058 | | 0.9023 | 2.76 | 32500 | 0.1812 | 0.2061 | | 0.8918 | 2.8 | 33000 | 0.1775 | 0.1997 | | 0.8852 | 2.85 | 33500 | 0.1790 | 0.1997 | | 0.8928 | 2.89 | 34000 | 0.1767 | 0.2013 | | 0.9079 | 2.93 | 34500 | 0.1735 | 0.1986 | | 0.9032 | 2.97 | 35000 | 0.1793 | 0.2024 | | 0.9018 | 3.02 | 35500 | 0.1778 | 0.2027 | | 0.8846 | 3.06 | 36000 | 0.1776 | 0.2046 | | 0.8848 | 3.1 | 36500 | 0.1812 | 0.2064 | | 0.9062 | 3.14 | 37000 | 0.1800 | 0.2018 | | 0.9011 | 3.19 | 37500 | 0.1783 | 0.2049 | | 0.8996 | 3.23 | 38000 | 0.1810 | 0.2036 | | 0.893 | 3.27 | 38500 | 0.1805 | 0.2056 | | 0.897 | 3.31 | 39000 | 0.1773 | 0.2035 | | 0.8992 | 3.36 | 39500 | 0.1804 | 0.2054 | | 0.8987 | 3.4 | 40000 | 0.1768 | 0.2016 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test` ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset mozilla-foundation/common_voice_7_0 --config de --split test --log_outputs ``` 2. To evaluate on test dev data ```bash python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-german-de --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ```

5b64a67dad247ee04bcd4732934778e6

elopezlopez/xlnet-base-cased_fold_6_binary_v1

elopezlopez

xlnet

12

1

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

2,637

false

# xlnet-base-cased_fold_6_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6214 - F1: 0.8352 ## 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: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4174 | 0.7980 | | 0.4661 | 2.0 | 580 | 0.4118 | 0.8142 | | 0.4661 | 3.0 | 870 | 0.5152 | 0.8331 | | 0.2714 | 4.0 | 1160 | 0.6901 | 0.8242 | | 0.2714 | 5.0 | 1450 | 0.6853 | 0.8451 | | 0.1542 | 6.0 | 1740 | 0.8570 | 0.8399 | | 0.0935 | 7.0 | 2030 | 1.1342 | 0.8401 | | 0.0935 | 8.0 | 2320 | 1.1763 | 0.8397 | | 0.037 | 9.0 | 2610 | 1.3530 | 0.8215 | | 0.037 | 10.0 | 2900 | 1.3826 | 0.8402 | | 0.0351 | 11.0 | 3190 | 1.4057 | 0.8374 | | 0.0351 | 12.0 | 3480 | 1.4259 | 0.8455 | | 0.0159 | 13.0 | 3770 | 1.4270 | 0.8431 | | 0.0249 | 14.0 | 4060 | 1.4215 | 0.8442 | | 0.0249 | 15.0 | 4350 | 1.4245 | 0.8408 | | 0.0197 | 16.0 | 4640 | 1.4171 | 0.8353 | | 0.0197 | 17.0 | 4930 | 1.4537 | 0.8383 | | 0.0137 | 18.0 | 5220 | 1.4786 | 0.8430 | | 0.0068 | 19.0 | 5510 | 1.5635 | 0.8443 | | 0.0068 | 20.0 | 5800 | 1.5527 | 0.8378 | | 0.0062 | 21.0 | 6090 | 1.5917 | 0.8460 | | 0.0062 | 22.0 | 6380 | 1.6317 | 0.8318 | | 0.005 | 23.0 | 6670 | 1.6226 | 0.8340 | | 0.005 | 24.0 | 6960 | 1.6378 | 0.8310 | | 0.007 | 25.0 | 7250 | 1.6214 | 0.8352 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

6cea3b3d49b1a57756a62f6e007c1b1f

onefish51/dog_w_prior-preservation

onefish51

null

33

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

413

false

# LoRA DreamBooth - onefish51/dog_w_prior-preservation These are LoRA adaption weights for /data2/home/tyu/stable_diffusion/diffusers/stable-diffusion-v1-4. The weights were trained on a photo of sks panda using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

0804fb2e5b32a692a9543fec605309c4

sanchit-gandhi/whisper-medium-es-5k

sanchit-gandhi

whisper

15

6

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['es']

['facebook/multilingual_librispeech']

null

0

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

true

1,618

false

# Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset. It achieves the following results on the evaluation set: - Loss: 1.2668 - Wer: 60.1623 ## 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-08 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 2.2112 | 0.2 | 500 | 1.7394 | 61.1126 | | 1.4913 | 0.4 | 1000 | 1.3758 | 62.8143 | | 1.6651 | 0.6 | 1500 | 1.3100 | 61.3261 | | 1.7031 | 0.8 | 2000 | 1.2752 | 60.5261 | | 1.4289 | 1.0 | 2500 | 1.2668 | 60.1623 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.0 - Datasets 2.6.2.dev0 - Tokenizers 0.12.1

e44e96bc8c56c8ff0133ade09a4a2010

jfealko/wav2vec2-large-xls-r-300m-russian-colab-beam_search_test

jfealko

wav2vec2

13

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

2,769

false

# wav2vec2-large-xls-r-300m-russian-colab-beam_search_test 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: 0.7619 - Wer: 0.4680 ## 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: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.0158 | 4.16 | 100 | 5.4134 | 1.0 | | 4.0394 | 8.33 | 200 | 3.4304 | 1.0 | | 3.2721 | 12.49 | 300 | 3.2273 | 1.0 | | 3.1277 | 16.66 | 400 | 2.8023 | 0.9984 | | 1.3791 | 20.82 | 500 | 0.9888 | 0.8546 | | 0.3659 | 24.99 | 600 | 0.7602 | 0.6304 | | 0.1858 | 29.16 | 700 | 0.7965 | 0.6156 | | 0.1403 | 33.33 | 800 | 0.7998 | 0.5839 | | 0.1173 | 37.49 | 900 | 0.8353 | 0.5941 | | 0.0917 | 41.66 | 1000 | 0.8272 | 0.5522 | | 0.0743 | 45.82 | 1100 | 0.8342 | 0.5471 | | 0.063 | 49.99 | 1200 | 0.7988 | 0.5352 | | 0.0528 | 54.16 | 1300 | 0.7740 | 0.5201 | | 0.0456 | 58.33 | 1400 | 0.7636 | 0.5165 | | 0.0389 | 62.49 | 1500 | 0.7922 | 0.5161 | | 0.0329 | 66.66 | 1600 | 0.8035 | 0.5158 | | 0.0283 | 70.82 | 1700 | 0.7873 | 0.4832 | | 0.0255 | 74.99 | 1800 | 0.7853 | 0.4870 | | 0.0236 | 79.16 | 1900 | 0.8236 | 0.5045 | | 0.0202 | 83.33 | 2000 | 0.7661 | 0.4796 | | 0.0165 | 87.49 | 2100 | 0.7584 | 0.4680 | | 0.0156 | 91.66 | 2200 | 0.7685 | 0.4772 | | 0.0149 | 95.82 | 2300 | 0.7519 | 0.4696 | | 0.0126 | 99.99 | 2400 | 0.7619 | 0.4680 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

bf9ab34cbe900667a43e6b8634a3ad1c

microsoft/git-base

microsoft

git

10

5,552

transformers

5

image-to-text

true

false

mit

['en']

null

0

2

0

['vision', 'image-to-text', 'image-captioning']

false

true

2,982

false

# GIT (GenerativeImage2Text), base-sized GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [this repository](https://github.com/microsoft/GenerativeImage2Text). Disclaimer: The team releasing GIT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description GIT is a Transformer decoder conditioned on both CLIP image tokens and text tokens. The model is trained using "teacher forcing" on a lot of (image, text) pairs. The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens. The model has full access to (i.e. a bidirectional attention mask is used for) the image patch tokens, but only has access to the previous text tokens (i.e. a causal attention mask is used for the text tokens) when predicting the next text token. ![GIT architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg) This allows the model to be used for tasks like: - image and video captioning - visual question answering (VQA) on images and videos - even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text). ## Intended uses & limitations You can use the raw model for image captioning. See the [model hub](https://huggingface.co/models?search=microsoft/git) to look for fine-tuned versions on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/model_doc/git#transformers.GitForCausalLM.forward.example). ## Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al., 2021a), and an extra 0.6B data following a similar collection procedure in Hu et al. (2021a). => however this is for the model referred to as "GIT" in the paper, which is not open-sourced. This checkpoint is "GIT-base", which is a smaller variant of GIT trained on 10 million image-text pairs. See table 11 in the [paper](https://arxiv.org/abs/2205.14100) for more details. ### Preprocessing We refer to the original repo regarding details for preprocessing during training. During validation, one resizes the shorter edge of each image, after which center cropping is performed to a fixed-size resolution. Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation. ## Evaluation results For evaluation results, we refer readers to the [paper](https://arxiv.org/abs/2205.14100).

1691b34d1720e489cead4bde48e9d95e

oskarandrsson/mt-ru-sv-finetuned

oskarandrsson

marian

11

22

transformers

0

translation

true

false

apache-2.0

['ru', 'sv']

null

1

0

['generated_from_trainer', 'translation']

true

1,071

false

# mt-ru-sv-finetuned This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-sv](https://huggingface.co/Helsinki-NLP/opus-mt-ru-sv) on the None dataset. It achieves the following results on the Tatoeba.rus.swe evaluation set: - eval_loss: 0.6998 - eval_bleu: 54.4473 ## 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-06 - train_batch_size: 24 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.6.1 - Tokenizers 0.13.1

e5c7baac6e91aa35392e8de457dd30d9

dbmdz/bert-base-german-europeana-cased

dbmdz

bert

8

225

transformers

0

null

true

mit

['de']

null

0

['historic german']

false

true

2,328

false

# 🤗 + 📚 dbmdz BERT models In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources German Europeana BERT models 🎉 # German Europeana BERT We use the open source [Europeana newspapers](http://www.europeana-newspapers.eu/) that were provided by *The European Library*. The final training corpus has a size of 51GB and consists of 8,035,986,369 tokens. Detailed information about the data and pretraining steps can be found in [this repository](https://github.com/stefan-it/europeana-bert). ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | ------------------------------------------ | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-german-europeana-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-german-europeana-cased/vocab.txt) ## Results For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert). ## Usage With Transformers >= 2.3 our German Europeana BERT models can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-europeana-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-german-europeana-cased") ``` # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗

59408cf2568d71252a9e67bbfe8513a4

amlannayak/finetuning-sentiment-model-3000-samples

amlannayak

distilbert

10

2

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

0

['generated_from_trainer']

true

1,049

false

# finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3251 - Accuracy: 0.8767 - F1: 0.8787 ## 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 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

43e63788621b7208cea676363bb2570c

jonatasgrosman/exp_w2v2t_de_wav2vec2_s144

jonatasgrosman

wav2vec2

10

5

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

456

false

# exp_w2v2t_de_wav2vec2_s144 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) 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.

aa766c5d4e481891b71fdad5912b8342

google/t5-efficient-xxl

google

t5

12

128

transformers

7

text2text-generation

true

apache-2.0

['en']

['c4']

null

0

['deep-narrow']

false

true

6,208

false

# T5-Efficient-XXL (Deep-Narrow version) T5-Efficient-XXL 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-xxl** - is of model type **Xxl** with no variations. It has **11307.38** million parameters and thus requires *ca.* **45229.52 MB** of memory in full precision (*fp32*) or **22614.76 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.

ffff7d67ee102b7392d4270eb1b0a037

MoritzLaurer/mDeBERTa-v3-base-mnli-xnli

MoritzLaurer

deberta-v2

8

432,624

transformers

84

zero-shot-classification

true

false

mit

['multilingual', 'en', 'ar', 'bg', 'de', 'el', 'es', 'fr', 'hi', 'ru', 'sw', 'th', 'tr', 'ur', 'vi', 'zh']

['multi_nli', 'xnli']

null

0

1

0

1

['zero-shot-classification', 'text-classification', 'nli', 'pytorch']

false

true

5,846

false

# Multilingual mDeBERTa-v3-base-mnli-xnli ## Model description This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli). As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). If you are looking for a smaller, faster (but less performant) model, you can try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli). ### How to use the model #### Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" hypothesis = "Emmanuel Macron is the President of France" input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, but due to quality issues with these machine translations, this model was only trained on the professional translations from the XNLI development set and the original English MNLI training set (392 702 texts). Not using machine translated texts can avoid overfitting the model to the 15 languages; avoids catastrophic forgetting of the other 85 languages mDeBERTa was pre-trained on; and significantly reduces training costs. ### Training procedure mDeBERTa-v3-base-mnli-xnli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=2, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=16, # batch size per device during training per_device_eval_batch_size=16, # batch size for evaluation warmup_ratio=0.1, # number of warmup steps for learning rate scheduler weight_decay=0.06, # strength of weight decay ) ``` ### Eval results The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on the other 85 languages mDeBERTa was training on, but performance is most likely lower than for those languages available in XNLI. Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, the authors have most likely made a mistake during testing since non of the latest papers shows a multilingual average performance of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vi | zh ---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|---------- 0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116 ## Limitations and bias Please consult the original DeBERTa-V3 paper and literature on different NLI datasets for potential biases. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ## Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ## Debugging and issues Note that DeBERTa-v3 was released in late 2021 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 or higher might solve some issues. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77

fe5178492d59c00dba3cbe4ea9cfa580

nestoralvaro/mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base

nestoralvaro

mt5

12

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,483

false

# mt5-base-finetuned-xsum-RAW_data_prep_2021_12_26___t22027_162754.csv__google_mt5_base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.7721 - Rouge2: 0.0701 - Rougel: 0.7721 - Rougelsum: 0.7718 - Gen Len: 6.329 ## 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.0 | 1.0 | 131773 | nan | 0.7721 | 0.0701 | 0.7721 | 0.7718 | 6.329 | ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

36c33b7cee3c345ee0af5cb6c8e1fc85

ogimgio/bert-base-german-cased-finetuned

ogimgio

bert

16

3

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

1,382

false

# bert-base-german-cased-finetuned This model is a fine-tuned version of [ogimgio/bert-base-german-cased-issues-128](https://huggingface.co/ogimgio/bert-base-german-cased-issues-128) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4083 - Micro f1: 0.5637 - Macro f1: 0.5041 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.4609 | 1.0 | 103 | 0.4403 | 0.5551 | 0.4453 | | 0.362 | 2.0 | 206 | 0.4083 | 0.5637 | 0.5041 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

356d225aa2547ab4f07fe9163aeba946

jonatasgrosman/exp_w2v2t_sv-se_wav2vec2_s732

jonatasgrosman

wav2vec2

10

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['sv-SE']

['mozilla-foundation/common_voice_7_0']

null

0

['automatic-speech-recognition', 'sv-SE']

false

true

462

false

# exp_w2v2t_sv-se_wav2vec2_s732 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (sv-SE)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

d9439d7536d30330b0af06f2902f8b44

pig4431/TweetEval_ALBERT_5E

pig4431

albert

10

5

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

8,753

false

# TweetEval_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.1990 - Accuracy: 0.9267 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4636 | 0.04 | 50 | 0.3662 | 0.8667 | | 0.442 | 0.08 | 100 | 0.3471 | 0.84 | | 0.3574 | 0.12 | 150 | 0.3446 | 0.86 | | 0.392 | 0.16 | 200 | 0.6776 | 0.6267 | | 0.4801 | 0.2 | 250 | 0.4307 | 0.7667 | | 0.487 | 0.24 | 300 | 0.5127 | 0.8 | | 0.4414 | 0.28 | 350 | 0.3912 | 0.8133 | | 0.4495 | 0.32 | 400 | 0.4056 | 0.8333 | | 0.4637 | 0.37 | 450 | 0.3635 | 0.8533 | | 0.4231 | 0.41 | 500 | 0.4235 | 0.84 | | 0.4049 | 0.45 | 550 | 0.4094 | 0.8067 | | 0.4481 | 0.49 | 600 | 0.3977 | 0.7733 | | 0.4024 | 0.53 | 650 | 0.3361 | 0.8733 | | 0.3901 | 0.57 | 700 | 0.3014 | 0.8667 | | 0.3872 | 0.61 | 750 | 0.3363 | 0.8533 | | 0.377 | 0.65 | 800 | 0.3754 | 0.8 | | 0.459 | 0.69 | 850 | 0.3861 | 0.8 | | 0.437 | 0.73 | 900 | 0.3834 | 0.8333 | | 0.3823 | 0.77 | 950 | 0.3541 | 0.8733 | | 0.3561 | 0.81 | 1000 | 0.3177 | 0.84 | | 0.4536 | 0.85 | 1050 | 0.4291 | 0.78 | | 0.4457 | 0.89 | 1100 | 0.3193 | 0.86 | | 0.3478 | 0.93 | 1150 | 0.3159 | 0.8533 | | 0.4613 | 0.97 | 1200 | 0.3605 | 0.84 | | 0.4081 | 1.01 | 1250 | 0.4291 | 0.7867 | | 0.3849 | 1.06 | 1300 | 0.3114 | 0.8733 | | 0.4071 | 1.1 | 1350 | 0.2939 | 0.8667 | | 0.3484 | 1.14 | 1400 | 0.3212 | 0.84 | | 0.3869 | 1.18 | 1450 | 0.2717 | 0.8933 | | 0.3877 | 1.22 | 1500 | 0.3459 | 0.84 | | 0.4245 | 1.26 | 1550 | 0.3404 | 0.8733 | | 0.4148 | 1.3 | 1600 | 0.2863 | 0.8667 | | 0.3542 | 1.34 | 1650 | 0.3377 | 0.86 | | 0.4093 | 1.38 | 1700 | 0.2972 | 0.8867 | | 0.3579 | 1.42 | 1750 | 0.3926 | 0.86 | | 0.3892 | 1.46 | 1800 | 0.2870 | 0.8667 | | 0.3569 | 1.5 | 1850 | 0.4027 | 0.8467 | | 0.3493 | 1.54 | 1900 | 0.3069 | 0.8467 | | 0.36 | 1.58 | 1950 | 0.3197 | 0.8733 | | 0.3532 | 1.62 | 2000 | 0.3711 | 0.8667 | | 0.3311 | 1.66 | 2050 | 0.2897 | 0.8867 | | 0.346 | 1.7 | 2100 | 0.2938 | 0.88 | | 0.3389 | 1.75 | 2150 | 0.2734 | 0.8933 | | 0.3289 | 1.79 | 2200 | 0.2606 | 0.8867 | | 0.3558 | 1.83 | 2250 | 0.3070 | 0.88 | | 0.3277 | 1.87 | 2300 | 0.2757 | 0.8867 | | 0.3166 | 1.91 | 2350 | 0.2759 | 0.8733 | | 0.3223 | 1.95 | 2400 | 0.2053 | 0.9133 | | 0.317 | 1.99 | 2450 | 0.2307 | 0.8867 | | 0.3408 | 2.03 | 2500 | 0.2557 | 0.9067 | | 0.3212 | 2.07 | 2550 | 0.2508 | 0.8867 | | 0.2806 | 2.11 | 2600 | 0.2472 | 0.88 | | 0.3567 | 2.15 | 2650 | 0.2790 | 0.8933 | | 0.2887 | 2.19 | 2700 | 0.3197 | 0.88 | | 0.3222 | 2.23 | 2750 | 0.2943 | 0.8667 | | 0.2773 | 2.27 | 2800 | 0.2297 | 0.88 | | 0.2728 | 2.31 | 2850 | 0.2813 | 0.8733 | | 0.3115 | 2.35 | 2900 | 0.3470 | 0.8867 | | 0.3001 | 2.39 | 2950 | 0.2702 | 0.8933 | | 0.3464 | 2.44 | 3000 | 0.2855 | 0.9 | | 0.3041 | 2.48 | 3050 | 0.2366 | 0.8867 | | 0.2717 | 2.52 | 3100 | 0.3220 | 0.88 | | 0.2903 | 2.56 | 3150 | 0.2230 | 0.9 | | 0.2959 | 2.6 | 3200 | 0.2439 | 0.9067 | | 0.2753 | 2.64 | 3250 | 0.2918 | 0.8733 | | 0.2515 | 2.68 | 3300 | 0.2493 | 0.88 | | 0.295 | 2.72 | 3350 | 0.2673 | 0.8867 | | 0.2572 | 2.76 | 3400 | 0.2842 | 0.8733 | | 0.2988 | 2.8 | 3450 | 0.2306 | 0.9067 | | 0.2923 | 2.84 | 3500 | 0.2329 | 0.8933 | | 0.2856 | 2.88 | 3550 | 0.2374 | 0.88 | | 0.2867 | 2.92 | 3600 | 0.2294 | 0.8733 | | 0.306 | 2.96 | 3650 | 0.2169 | 0.92 | | 0.2312 | 3.0 | 3700 | 0.2456 | 0.88 | | 0.2438 | 3.04 | 3750 | 0.2134 | 0.8867 | | 0.2103 | 3.08 | 3800 | 0.2242 | 0.92 | | 0.2469 | 3.12 | 3850 | 0.2407 | 0.92 | | 0.2346 | 3.17 | 3900 | 0.1866 | 0.92 | | 0.2275 | 3.21 | 3950 | 0.2318 | 0.92 | | 0.2542 | 3.25 | 4000 | 0.2256 | 0.9 | | 0.2544 | 3.29 | 4050 | 0.2246 | 0.9133 | | 0.2468 | 3.33 | 4100 | 0.2436 | 0.8733 | | 0.2105 | 3.37 | 4150 | 0.2098 | 0.9067 | | 0.2818 | 3.41 | 4200 | 0.2304 | 0.88 | | 0.2041 | 3.45 | 4250 | 0.2430 | 0.8933 | | 0.28 | 3.49 | 4300 | 0.1990 | 0.9067 | | 0.1997 | 3.53 | 4350 | 0.2515 | 0.8933 | | 0.2409 | 3.57 | 4400 | 0.2315 | 0.9 | | 0.1969 | 3.61 | 4450 | 0.2160 | 0.8933 | | 0.2246 | 3.65 | 4500 | 0.1979 | 0.92 | | 0.2185 | 3.69 | 4550 | 0.2238 | 0.9 | | 0.259 | 3.73 | 4600 | 0.2011 | 0.9067 | | 0.2407 | 3.77 | 4650 | 0.1911 | 0.92 | | 0.2198 | 3.81 | 4700 | 0.2083 | 0.92 | | 0.235 | 3.86 | 4750 | 0.1724 | 0.9267 | | 0.26 | 3.9 | 4800 | 0.1640 | 0.9333 | | 0.2334 | 3.94 | 4850 | 0.1778 | 0.9267 | | 0.2121 | 3.98 | 4900 | 0.2062 | 0.8933 | | 0.173 | 4.02 | 4950 | 0.1987 | 0.92 | | 0.1942 | 4.06 | 5000 | 0.2509 | 0.8933 | | 0.1703 | 4.1 | 5050 | 0.2179 | 0.9 | | 0.1735 | 4.14 | 5100 | 0.2429 | 0.8867 | | 0.2098 | 4.18 | 5150 | 0.1938 | 0.9267 | | 0.2126 | 4.22 | 5200 | 0.1971 | 0.92 | | 0.164 | 4.26 | 5250 | 0.2539 | 0.9067 | | 0.2271 | 4.3 | 5300 | 0.1765 | 0.94 | | 0.2245 | 4.34 | 5350 | 0.1894 | 0.94 | | 0.182 | 4.38 | 5400 | 0.1790 | 0.9467 | | 0.1835 | 4.42 | 5450 | 0.2014 | 0.9333 | | 0.2185 | 4.46 | 5500 | 0.1881 | 0.9467 | | 0.2113 | 4.5 | 5550 | 0.1742 | 0.9333 | | 0.1997 | 4.55 | 5600 | 0.1762 | 0.94 | | 0.1959 | 4.59 | 5650 | 0.1657 | 0.9467 | | 0.2035 | 4.63 | 5700 | 0.1973 | 0.92 | | 0.228 | 4.67 | 5750 | 0.1769 | 0.9467 | | 0.1632 | 4.71 | 5800 | 0.1968 | 0.9267 | | 0.1468 | 4.75 | 5850 | 0.1822 | 0.9467 | | 0.1936 | 4.79 | 5900 | 0.1832 | 0.94 | | 0.1743 | 4.83 | 5950 | 0.1987 | 0.9267 | | 0.1654 | 4.87 | 6000 | 0.1943 | 0.9267 | | 0.1859 | 4.91 | 6050 | 0.1990 | 0.92 | | 0.2039 | 4.95 | 6100 | 0.1982 | 0.9267 | | 0.2325 | 4.99 | 6150 | 0.1990 | 0.9267 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2

f4389bdbd6ade0a8564eeeeb2f97f92c

muhtasham/tiny-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab

muhtasham

bert

12

2

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,696

false

# tiny-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.5263 ## 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 | |:-------------:|:-----:|:----:|:---------------:| | 9.1486 | 6.25 | 500 | 7.8470 | | 7.1116 | 12.5 | 1000 | 6.6165 | | 6.3036 | 18.75 | 1500 | 6.1976 | | 6.0919 | 25.0 | 2000 | 6.2290 | | 6.0014 | 31.25 | 2500 | 6.0136 | | 5.9682 | 37.5 | 3000 | 5.8730 | | 5.8571 | 43.75 | 3500 | 5.7612 | | 5.8144 | 50.0 | 4000 | 5.7921 | | 5.7654 | 56.25 | 4500 | 5.8279 | | 5.7322 | 62.5 | 5000 | 5.5263 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.1+cu116 - Datasets 2.9.1.dev0 - Tokenizers 0.13.2

2633b843de6240221b49e21ec2c85892

sgangireddy/whisper-base-cv-cs

sgangireddy

whisper

24

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['cs']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,563

false

# Whisper base Czech CV This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set: - Loss: 0.5394 - Wer: 33.9957 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.206 | 4.01 | 1000 | 0.4356 | 36.2443 | | 0.0332 | 8.02 | 2000 | 0.4583 | 34.0509 | | 0.0074 | 12.03 | 3000 | 0.5119 | 34.4395 | | 0.005 | 16.04 | 4000 | 0.5394 | 33.9957 | | 0.0045 | 21.01 | 5000 | 0.5461 | 34.1025 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2

e61e38bb4e9c4238e912d28958f8afff

federicopascual/finetuned-sentiment-analysis-model

federicopascual

distilbert

13

30

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

['generated_from_trainer']

true

1,074

false

# finetuned-sentiment-analysis-model 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: 0.2868 - Accuracy: 0.909 - Precision: 0.8900 - Recall: 0.9283 ## 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 ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

4b644e6248c9c585a909a7719c917bc5

dbmdz/distilbert-base-turkish-cased

dbmdz

distilbert

7

106,925

transformers

6

null

true

false

mit

['tr']

null

0

[]

false

true

3,108

false

# 🤗 + 📚 dbmdz Distilled Turkish BERT model In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a (cased) distilled model for Turkish 🎉 # 🇹🇷 DistilBERTurk DistilBERTurk is a community-driven cased distilled BERT model for Turkish. DistilBERTurk was trained on 7GB of the original training data that was used for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats), using the cased version of BERTurk as teacher model. *DistilBERTurk* was trained with the official Hugging Face implementation from [here](https://github.com/huggingface/transformers/tree/master/examples/distillation) for 5 days on 4 RTX 2080 TI. More details about distillation can be found in the ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) paper by Sanh et al. (2019). ## Model weights Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository! | Model | Downloads | --------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/distilbert-base-turkish-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/vocab.txt) ## Usage With Transformers >= 2.3 our DistilBERTurk model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased") model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased") ``` ## Results For results on PoS tagging or NER tasks, please refer to [this repository](https://github.com/stefan-it/turkish-bert). For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model. The overall performance difference between DistilBERTurk and the original (teacher) BERTurk model is ~1.18%. # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT models just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing us the Turkish NER dataset for evaluation. Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download both cased and uncased models from their S3 storage 🤗

e1a7004d765c92d32782b03e185f3cb5