davanstrien/model_cards_with_readmes · Datasets at Hugging Face

jonatasgrosman/exp_w2v2t_et_vp-nl_s353

jonatasgrosman

wav2vec2

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['et']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

469

false

# exp_w2v2t_et_vp-nl_s353 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (et)](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.

0518ff5feed68accbdb7ccaee0c6b303

gokuls/distilbert_sa_GLUE_Experiment_rte_96

gokuls

distilbert

17

4

transformers

0

text-classification

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

2,173

false

# distilbert_sa_GLUE_Experiment_rte_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6925 - Accuracy: 0.5271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6932 | 1.0 | 10 | 0.6928 | 0.5271 | | 0.6934 | 2.0 | 20 | 0.6927 | 0.5271 | | 0.6934 | 3.0 | 30 | 0.6932 | 0.4729 | | 0.6931 | 4.0 | 40 | 0.6930 | 0.5271 | | 0.6936 | 5.0 | 50 | 0.6932 | 0.4440 | | 0.6932 | 6.0 | 60 | 0.6927 | 0.5271 | | 0.6932 | 7.0 | 70 | 0.6926 | 0.5271 | | 0.6928 | 8.0 | 80 | 0.6932 | 0.4477 | | 0.6935 | 9.0 | 90 | 0.6932 | 0.4260 | | 0.6933 | 10.0 | 100 | 0.6925 | 0.5271 | | 0.6929 | 11.0 | 110 | 0.6932 | 0.4440 | | 0.693 | 12.0 | 120 | 0.6935 | 0.4729 | | 0.6926 | 13.0 | 130 | 0.6931 | 0.5307 | | 0.6916 | 14.0 | 140 | 0.6932 | 0.5199 | | 0.6903 | 15.0 | 150 | 0.6943 | 0.4440 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.8.0 - Tokenizers 0.13.2

5523ef341e57bf40aafef8306964c645

Lvxue/distilled-mt5-small-1-1

Lvxue

mt5

14

1

transformers

0

text2text-generation

true

false

apache-2.0

['en', 'ro']

['wmt16']

null

1

0

['generated_from_trainer']

true

1,034

false

# distilled-mt5-small-1-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8289 - Bleu: 6.6959 - Gen Len: 45.7539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

da0ec09716f568c416953b30498c529c

flamesbob/Yuko_model

flamesbob

null

4

0

null

0

null

false

creativeml-openrail-m

null

0

[]

false

true

955

false

To use draw emphasis from the training model include the word `m_yukoring` in your prompt. Yukoring is an artists that does a lot of anime watercolor style art. License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here

96d4e1e1910aeb40ab1df3ecff18724b

zendiode69/electra-base-squad2-finetuned-squad-12-trainedfor-3

zendiode69

electra

12

0

transformers

0

question-answering

true

false

cc-by-4.0

null

0

['generated_from_trainer']

true

1,298

false

# electra-base-squad2-finetuned-squad-12-trainedfor-3 This model is a fine-tuned version of [deepset/electra-base-squad2](https://huggingface.co/deepset/electra-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3064 ## 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: 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 | |:-------------:|:-----:|:----:|:---------------:| | 0.6128 | 1.0 | 578 | 0.3142 | | 0.4583 | 2.0 | 1156 | 0.3072 | | 0.415 | 3.0 | 1734 | 0.3064 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

9a70bfbf6fe35b88a7e8f27c4bc33795

Martha-987/whisper-small-Arabic

Martha-987

whisper

24

2

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ar']

['mozilla-foundation/common_voice_11_0']

null

0

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

true

1,296

false

# Whisper Small Ar- Martha 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: 0.3837 - Wer: 51.1854 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.2726 | 0.42 | 1000 | 0.3837 | 51.1854 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.0 - Tokenizers 0.13.2

c3e578f6ad21da408cbae02f45d61a7d

asapp/sew-d-tiny-100k

asapp

sew-d

5

126

transformers

0

feature-extraction

true

false

apache-2.0

['en']

['librispeech_asr']

null

0

['speech']

false

true

1,699

false

# SEW-D-tiny [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.

71cb2d3f37d9bbb03327f419eeb83e88

KoichiYasuoka/roberta-base-korean-hanja

KoichiYasuoka

roberta

7

73

transformers

1

fill-mask

true

false

cc-by-sa-4.0

['ko']

null

0

['korean', 'masked-lm']

false

true

775

false

# roberta-base-korean-hanja ## Model Description This is a RoBERTa model pre-trained on Korean texts, derived from [klue/roberta-base](https://huggingface.co/klue/roberta-base). Token-embeddings are enhanced to include all 한문 교육용 기초 한자 and 인명용 한자 characters. You can fine-tune `roberta-base-korean-hanja` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-korean-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-korean-ud-goeswith), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-korean-hanja") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-korean-hanja") ```

9444e26a24aa70e106eba1ad42e105d0

sukhendrasingh/finetuning-sentiment-model-3000-samples

sukhendrasingh

distilbert

13

11

transformers

0

text-classification

true

false

apache-2.0

null

['imdb']

null

1

0

['generated_from_trainer']

true

1,056

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: 0.3323 - Accuracy: 0.8733 - F1: 0.8797 ## 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 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

212f80c1139b3a3b41ef73daafc2f5df

EleutherAI/pythia-1b

EleutherAI

gpt_neox

7

7,595

transformers

3

text-generation

true

false

apache-2.0

['en']

['the_pile']

null

1

0

1

0

['pytorch', 'causal-lm', 'pythia']

false

true

10,783

false

The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/EleutherAI). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact model parameter counts. ## Pythia-1B ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:contact@eleuther.ai). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10-4 | — | <figcaption>Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-1B for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-1B as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-1B will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-1B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-1B may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-1B. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model. For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). The Pile was **not** deduplicated before being used to train Pythia-1B. #### Training procedure Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps). See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training). ### Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json). February 2023 note: select evaluations and comparison with OPT and BLOOM models will be added here at a later date. ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

b7fa2eb99d59ee6a3ecec8cb473eedda

sd-concepts-library/scratch-project

sd-concepts-library

null

16

0

null

0

null

false

mit

null

0

[]

false

true

1,906

false

### Scratch project on Stable Diffusion This is the `<scratch-project>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<scratch-project> 0](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/2.jpeg) ![<scratch-project> 1](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/10.jpeg) ![<scratch-project> 2](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/0.jpeg) ![<scratch-project> 3](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/1.jpeg) ![<scratch-project> 4](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/9.jpeg) ![<scratch-project> 5](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/7.jpeg) ![<scratch-project> 6](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/4.jpeg) ![<scratch-project> 7](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/3.jpeg) ![<scratch-project> 8](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/6.jpeg) ![<scratch-project> 9](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/5.jpeg) ![<scratch-project> 10](https://huggingface.co/sd-concepts-library/scratch-project/resolve/main/concept_images/8.jpeg)

9025fb3f45049e252c9eb387ce2468cd

Haakf/allsides_left_text_padded_overfit

Haakf

distilbert

8

4

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

2,467

false

# Haakf/allsides_left_text_padded_overfit This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9591 - Validation Loss: 1.9856 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -712, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0625 | 1.8988 | 0 | | 2.0063 | 1.9757 | 1 | | 2.0061 | 1.9345 | 2 | | 1.9730 | 1.9248 | 3 | | 1.9572 | 1.8433 | 4 | | 1.9645 | 1.9104 | 5 | | 1.9584 | 1.9017 | 6 | | 1.9508 | 1.9430 | 7 | | 1.9716 | 1.9498 | 8 | | 1.9613 | 1.9312 | 9 | | 1.9625 | 1.8820 | 10 | | 1.9573 | 1.8768 | 11 | | 1.9612 | 1.8837 | 12 | | 1.9501 | 1.9325 | 13 | | 1.9471 | 1.9231 | 14 | | 1.9567 | 1.8987 | 15 | | 1.9605 | 1.9159 | 16 | | 1.9661 | 1.9157 | 17 | | 1.9513 | 1.8840 | 18 | | 1.9591 | 1.9856 | 19 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

c5dc687e358f92e689cc89c35172278c

research-backup/bart-base-squadshifts-vanilla-reddit-qg

research-backup

bart

15

1

transformers

0

text2text-generation

true

false

cc-by-4.0

['en']

['lmqg/qg_squadshifts']

null

0

['question generation']

true

4,148

false

# Model Card of `research-backup/bart-base-squadshifts-vanilla-reddit-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: reddit) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (reddit) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="research-backup/bart-base-squadshifts-vanilla-reddit-qg") # model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squadshifts-vanilla-reddit-qg") output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-reddit-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | Score | Type | Dataset | |:-----------|--------:|:-------|:---------------------------------------------------------------------------| | BERTScore | 91.89 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_1 | 25.35 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_2 | 16.53 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_3 | 10.97 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | Bleu_4 | 7.52 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | METEOR | 21.32 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | MoverScore | 61.44 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | | ROUGE_L | 24.67 | reddit | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: reddit - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 6 - batch: 8 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-reddit-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

cf2ff92a65d8a73fd4c4cf79025e7034

Helsinki-NLP/opus-mt-pl-sv

Helsinki-NLP

marian

10

16

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

770

false

### opus-mt-pl-sv * source languages: pl * target languages: sv * OPUS readme: [pl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/pl-sv/opus-2020-01-24.zip) * test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-sv/opus-2020-01-24.test.txt) * test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-sv/opus-2020-01-24.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.pl.sv | 58.9 | 0.717 |

ea05d5b8cdb8ae18c9fd6b1fe33dd38e

JTH/results

JTH

distilbert

10

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

921

false

# results This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 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 4.21.1 - Pytorch 1.12.0+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

fbaa51094a310b8bf0ee81d405720cc4

natedog/my_awesome_billsum_model

natedog

t5

14

0

transformers

0

text2text-generation

true

false

apache-2.0

null

['billsum']

null

0

['generated_from_trainer']

true

1,203

false

# my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 3.5089 | 0.1247 | 0.0333 | 0.1056 | 0.1055 | 19.0 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.9.0 - Tokenizers 0.13.2

e20d75e29c267137e8b973c401b3629c

openmmlab/upernet-swin-base

openmmlab

upernet

5

29

transformers

0

image-segmentation

true

false

mit

['en']

null

0

['vision', 'image-segmentation']

false

true

1,520

false

# UperNet, Swin Transformer base-sized backbone UperNet framework for semantic segmentation, leveraging a Swin Transformer backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a Swin Transformer backbone was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030). Disclaimer: The team releasing UperNet + Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel. ![UperNet architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/upernet_architecture.jpg) ## Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for fine-tuned versions (with various backbones) on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).

493ffea0e4665b131757555da3d51ff8

sd-concepts-library/im-poppy

sd-concepts-library

null

21

0

null

3

null

false

mit

null

0

[]

false

true

2,236

false

### im-poppy on Stable Diffusion This is the `im-poppy` 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`: ![im-poppy 0](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/6.jpeg) ![im-poppy 1](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/9.jpeg) ![im-poppy 2](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/1.jpeg) ![im-poppy 3](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/5.jpeg) ![im-poppy 4](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/15.jpeg) ![im-poppy 5](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/14.jpeg) ![im-poppy 6](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/2.jpeg) ![im-poppy 7](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/11.jpeg) ![im-poppy 8](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/3.jpeg) ![im-poppy 9](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/12.jpeg) ![im-poppy 10](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/10.jpeg) ![im-poppy 11](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/13.jpeg) ![im-poppy 12](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/0.jpeg) ![im-poppy 13](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/4.jpeg) ![im-poppy 14](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/7.jpeg) ![im-poppy 15](https://huggingface.co/sd-concepts-library/im-poppy/resolve/main/concept_images/8.jpeg)

3766baf7adcbf928086c3536e59c92f5

kasrahabib/200-500-bucket-finetunned

kasrahabib

bert

10

5

transformers

0

text-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,724

false

# kasrahabib/200-500-bucket-finetunned This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0280 - Validation Loss: 0.3784 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3110, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0739 | 0.6559 | 0 | | 0.4665 | 0.4309 | 1 | | 0.2473 | 0.3669 | 2 | | 0.1437 | 0.3746 | 3 | | 0.0825 | 0.3663 | 4 | | 0.0592 | 0.3649 | 5 | | 0.0451 | 0.3523 | 6 | | 0.0345 | 0.3710 | 7 | | 0.0292 | 0.3705 | 8 | | 0.0280 | 0.3784 | 9 | ### Framework versions - Transformers 4.26.0 - TensorFlow 2.9.2 - Datasets 2.9.0 - Tokenizers 0.13.2

95a84dfa0e0943f27c2ec257247e0cf8

WALIDALI/cynthiasly

WALIDALI

null

18

2

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

420

false

### cynthiasly 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:

4ec1640cbe26bc35afa3293468688c5d

frahman/distilbert-base-uncased-distilled-clinc

frahman

distilbert

10

3

transformers

0

text-classification

true

false

apache-2.0

null

['clinc_oos']

null

0

['generated_from_trainer']

true

1,793

false

# distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1002 - Accuracy: 0.9406 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9039 | 1.0 | 318 | 0.5777 | 0.7335 | | 0.4486 | 2.0 | 636 | 0.2860 | 0.8768 | | 0.2528 | 3.0 | 954 | 0.1792 | 0.9210 | | 0.176 | 4.0 | 1272 | 0.1398 | 0.9274 | | 0.1417 | 5.0 | 1590 | 0.1209 | 0.9329 | | 0.1245 | 6.0 | 1908 | 0.1110 | 0.94 | | 0.1135 | 7.0 | 2226 | 0.1061 | 0.9390 | | 0.1074 | 8.0 | 2544 | 0.1026 | 0.94 | | 0.1032 | 9.0 | 2862 | 0.1006 | 0.9410 | | 0.1017 | 10.0 | 3180 | 0.1002 | 0.9406 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

8dc285a1cbb7c9119222736c974e13dd

meongracun/nmt-mpst-id-en-lr_1e-05-ep_20-seq_128_bs-32

meongracun

t5

9

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,548

false

# nmt-mpst-id-en-lr_1e-05-ep_20-seq_128_bs-32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7787 - Bleu: 0.0338 - Meteor: 0.1312 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 202 | 3.1965 | 0.0132 | 0.0696 | | No log | 2.0 | 404 | 3.0644 | 0.0224 | 0.0975 | | 3.5509 | 3.0 | 606 | 2.9995 | 0.0255 | 0.1075 | | 3.5509 | 4.0 | 808 | 2.9538 | 0.0269 | 0.1106 | | 3.2374 | 5.0 | 1010 | 2.9221 | 0.0277 | 0.1134 | | 3.2374 | 6.0 | 1212 | 2.8996 | 0.0286 | 0.1165 | | 3.2374 | 7.0 | 1414 | 2.8750 | 0.0291 | 0.1177 | | 3.143 | 8.0 | 1616 | 2.8611 | 0.0297 | 0.1197 | | 3.143 | 9.0 | 1818 | 2.8466 | 0.0303 | 0.1209 | | 3.092 | 10.0 | 2020 | 2.8330 | 0.0312 | 0.1229 | | 3.092 | 11.0 | 2222 | 2.8234 | 0.0318 | 0.1247 | | 3.092 | 12.0 | 2424 | 2.8130 | 0.0322 | 0.1264 | | 3.0511 | 13.0 | 2626 | 2.8058 | 0.0323 | 0.1269 | | 3.0511 | 14.0 | 2828 | 2.7970 | 0.0324 | 0.1272 | | 3.0288 | 15.0 | 3030 | 2.7914 | 0.033 | 0.1288 | | 3.0288 | 16.0 | 3232 | 2.7877 | 0.0331 | 0.1299 | | 3.0288 | 17.0 | 3434 | 2.7837 | 0.0333 | 0.1302 | | 3.0133 | 18.0 | 3636 | 2.7809 | 0.0336 | 0.1308 | | 3.0133 | 19.0 | 3838 | 2.7792 | 0.0337 | 0.131 | | 3.0028 | 20.0 | 4040 | 2.7787 | 0.0338 | 0.1312 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.0 - Tokenizers 0.13.2

9ab48c64bbbd301c596a81dc18e59809

schorndorfer/distilroberta-base-finetuned-wikitext2

schorndorfer

roberta

9

4

transformers

0

fill-mask

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,267

false

# distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8347 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0853 | 1.0 | 2406 | 1.9214 | | 1.986 | 2.0 | 4812 | 1.8799 | | 1.9568 | 3.0 | 7218 | 1.8202 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1

7643a16fe5243196a3125d2e7ebf8158

IDEA-CCNL/Randeng-T5-Char-57M-Chinese

IDEA-CCNL

mt5

8

15

transformers

0

text2text-generation

true

false

apache-2.0

['zh']

null

0

['T5', 'chinese', 'sentencepiece']

false

true

2,724

false

# Randeng-T5-Char-57M-Chinese - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) ## 简介 Brief Introduction 善于处理NLT任务，中文版的T5-small，采用了BertTokenizer和中文字级别词典。 Good at handling NLT tasks, Chinese T5-small， use BertTokenizer and chinese vocab. ## 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言转换 NLT | 燃灯 Randeng | T5 | 57M | 中文-Chinese | ## 模型信息 Model Information 对比T5-small，训练了它的中文版。为了更好适用于中文任务，我们仅使用BertTokenzier，和支持中英文的词表，并且使用了语料库自适应预训练(Corpus-Adaptive Pre-Training, CAPT)技术在悟道语料库(180G版本)继续预训练。预训练目标为破坏span。具体地，我们在预训练阶段中使用了[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)大概花费了8张A100约24小时。 Compared with T5-samll, we implement its Chinese version. In order to use for chinese tasks, we use BertTokenizer and Chinese vocabulary, and Corpus-Adaptive Pre-Training (CAPT) on the WuDao Corpora (180 GB version). The pretraining objective is span corruption. Specifically, we use the [fengshen framework](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen) in the pre-training phase which cost about 24 hours with 8 A100 GPUs. ## 使用 Usage ```python from transformers import T5ForConditionalGeneration, BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese', add_special_tokens=False) model=T5ForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-T5-Char-57M-Chinese') ``` ## 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970): ```text @article{fengshenbang, author = {Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen and Ruyi Gan and Jiaxing Zhang}, title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence}, journal = {CoRR}, volume = {abs/2209.02970}, year = {2022} } ``` 也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/): You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/): ```text @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2021}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

66f925954581b239a7d667e4a6372853

jo0hnd0e/mt5-small-finetuned-amazon-en-es

jo0hnd0e

mt5

8

1

transformers

0

text2text-generation

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,649

false

# jo0hnd0e/mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.9844 - Validation Loss: 3.3610 - Epoch: 7 ## 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': 5.6e-05, 'decay_steps': 9672, '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 | |:----------:|:---------------:|:-----:| | 9.6302 | 4.2399 | 0 | | 5.7657 | 3.7191 | 1 | | 4.9972 | 3.5931 | 2 | | 4.6081 | 3.5038 | 3 | | 4.3425 | 3.4322 | 4 | | 4.1758 | 3.3950 | 5 | | 4.0512 | 3.3649 | 6 | | 3.9844 | 3.3610 | 7 | ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.0 - Datasets 2.2.2 - Tokenizers 0.12.1

07bedf8f59fc110d64dbabbd8228df8e

TalTechNLP/voxlingua107-epaca-tdnn

TalTechNLP

null

8

24,084

speechbrain

20

audio-classification

true

false

apache-2.0

['multilingual']

['VoxLingua107']

null

2

0

1

0

['audio-classification', 'speechbrain', 'embeddings', 'Language', 'Identification', 'pytorch', 'ECAPA-TDNN', 'TDNN', 'VoxLingua107']

false

true

5,487

false

# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model ## Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). #### How to use ```python import torchaudio from speechbrain.pretrained import EncoderClassifier language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp") # Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3") prediction = language_id.classify_batch(signal) print(prediction) (tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508, 0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997, 0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256, 0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944, 0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950, 0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777, 0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193, 0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364, 0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017, 0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464, 0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838, 0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th']) # The scores in the prediction[0] tensor can be interpreted as cosine scores between # the languages and the given utterance (i.e., the larger the better) # The identified language ISO code is given in prediction[3] print(prediction[3]) ['th'] # Alternatively, use the utterance embedding extractor: emb = language_id.encode_batch(signal) print(emb.shape) torch.Size([1, 1, 256]) ``` #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on subjective experiments, it doesn't work well on speech with a foreign accent - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model. Training recipe will be published soon. ## Evaluation results Error rate: 7% on the development dataset ### BibTeX entry and citation info ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ```

97fcf0587724b1a6bdf6a728f1d109bc

polejowska/swin-tiny-patch4-window7-224-eurosat

polejowska

swin

18

13

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

1

0

1

0

['generated_from_trainer']

true

1,606

false

# swin-tiny-patch4-window7-224-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0447 - Accuracy: 0.9852 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1547 | 0.99 | 147 | 0.0956 | 0.9711 | | 0.0707 | 1.99 | 294 | 0.0759 | 0.9733 | | 0.0537 | 2.99 | 441 | 0.0680 | 0.9768 | | 0.0302 | 3.99 | 588 | 0.0447 | 0.9852 | | 0.0225 | 4.99 | 735 | 0.0489 | 0.9837 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.0+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

ea9fed40410a5ed9153800c580640cfc

Anjoe/german-poetry-gpt2-large

Anjoe

gpt2

15

170

transformers

0

text-generation

true

false

mit

null

0

['generated_from_trainer']

true

1,179

false

# german-poetry-gpt2-large This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on German poems. It achieves the following results on the evaluation set: - eval_loss: 3.5753 - eval_runtime: 100.7173 - eval_samples_per_second: 51.6 - eval_steps_per_second: 25.805 - epoch: 4.0 - step: 95544 ## Model description large version of gpt-2 ## Intended uses & limitations It could be used for poetry generation ## Training and evaluation data The model was trained on german poems from projekt Gutenberg ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 6 ### Framework versions - Transformers 4.19.4 - Pytorch 1.11.0+cu113 - Datasets 2.3.0 - Tokenizers 0.12.1

fec6841219380c04f06fbc69fa7f3f28

mideind/IceBERT-xlmr-ic3

mideind

roberta

6

418

transformers

0

fill-mask

true

false

agpl-3.0

['is']

null

0

['roberta', 'icelandic', 'masked-lm', 'pytorch']

false

true

1,604

false

# IceBERT-xlmr-ic3 This model was trained with fairseq using the RoBERTa-base architecture. The model `xlm-roberta-base` was used as a starting point. It is one of many models we have trained for Icelandic, see the paper referenced below for further details. The training data used is shown in the table below. | Dataset | Size | Tokens | |------------------------------------------------------|---------|--------| | Icelandic Common Crawl Corpus (IC3) | 4.9 GB | 824M | ## Citation The model is described in this paper [https://arxiv.org/abs/2201.05601](https://arxiv.org/abs/2201.05601). Please cite the paper if you make use of the model. ``` @article{DBLP:journals/corr/abs-2201-05601, author = {V{\'{e}}steinn Sn{\ae}bjarnarson and Haukur Barri S{\'{\i}}monarson and P{\'{e}}tur Orri Ragnarsson and Svanhv{\'{\i}}t Lilja Ing{\'{o}}lfsd{\'{o}}ttir and Haukur P{\'{a}}ll J{\'{o}}nsson and Vilhj{\'{a}}lmur {\TH}orsteinsson and Hafsteinn Einarsson}, title = {A Warm Start and a Clean Crawled Corpus - {A} Recipe for Good Language Models}, journal = {CoRR}, volume = {abs/2201.05601}, year = {2022}, url = {https://arxiv.org/abs/2201.05601}, eprinttype = {arXiv}, eprint = {2201.05601}, timestamp = {Thu, 20 Jan 2022 14:21:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-05601.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

b10133e36a22903eabb2491180928fd1

KoichiYasuoka/bert-large-japanese-upos

KoichiYasuoka

bert

9

11

transformers

2

token-classification

true

false

cc-by-sa-4.0

['ja']

['universal_dependencies']

null

0

['japanese', 'token-classification', 'pos', 'wikipedia', 'dependency-parsing']

false

true

1,120

false

# bert-large-japanese-upos ## Model Description This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). ## How to Use ```py import torch from transformers import AutoTokenizer,AutoModelForTokenClassification tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-upos") model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-upos") s="国境の長いトンネルを抜けると雪国であった。" p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] print(list(zip(s,p))) ``` or ```py import esupar nlp=esupar.load("KoichiYasuoka/bert-large-japanese-upos") print(nlp("国境の長いトンネルを抜けると雪国であった。")) ``` ## See Also [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models

1d7c5f0f065aaee61cb9351a6a1b3f01

42MARU/ko-ctc-kenlm-spelling-only-wiki

42MARU

null

10

0

kenlm

0

text2text-generation

false

apache-2.0

['ko']

['korean-wiki']

null

0

['audio', 'automatic-speech-recognition', 'text2text-generation']

false

true

2,218

false

# ko-ctc-kenlm-spelling-only-wiki ## Table of Contents - [ko-ctc-kenlm-spelling-only-wiki](#ko-ctc-kenlm-spelling-only-wiki) - [Table of Contents](#table-of-contents) - [Model Details](#model-details) - [How to Get Started With the Model](#how-to-get-started-with-the-model) ## Model Details - **Model Description** - 음향 모델을 위한 N-gram Base의 LM으로 자소별 단어기반으로 만들어졌으며, KenLM으로 학습되었습니다. 해당 모델은 [ko-spelling-wav2vec2-conformer-del-1s](https://huggingface.co/42MARU/ko-spelling-wav2vec2-conformer-del-1s)과 사용하십시오. - HuggingFace Transformers Style로 불러와 사용할 수 있도록 처리했습니다. - pyctcdecode lib을 이용해서도 바로 사용가능합니다. - data는 wiki korean을 사용했습니다. spelling vocab data에 없는 문장은 전부 제거하여, 오히려 LM으로 Outlier가 발생할 소요를 최소화 시켰습니다. 해당 모델은 **철자전사** 기준의 데이터로 학습된 모델입니다. (숫자와 영어는 각 표기법을 따름) - **Developed by:** TADev (@lIlBrother) - **Language(s):** Korean - **License:** apache-2.0 ## How to Get Started With the Model ```python import librosa from pyctcdecode import build_ctcdecoder from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoTokenizer, Wav2Vec2ProcessorWithLM, ) from transformers.pipelines import AutomaticSpeechRecognitionPipeline audio_path = "" # 모델과 토크나이저, 예측을 위한 각 모듈들을 불러옵니다. model = AutoModelForCTC.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") feature_extractor = AutoFeatureExtractor.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") tokenizer = AutoTokenizer.from_pretrained("42MARU/ko-spelling-wav2vec2-conformer-del-1s") processor = Wav2Vec2ProcessorWithLM("42MARU/ko-ctc-kenlm-spelling-only-wiki") # 실제 예측을 위한 파이프라인에 정의된 모듈들을 삽입. asr_pipeline = AutomaticSpeechRecognitionPipeline( model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, decoder=processor.decoder, device=-1, ) # 음성파일을 불러오고 beamsearch 파라미터를 특정하여 예측을 수행합니다. raw_data, _ = librosa.load(audio_path, sr=16000) kwargs = {"decoder_kwargs": {"beam_width": 100}} pred = asr_pipeline(inputs=raw_data, **kwargs)["text"] # 모델이 자소 분리 유니코드 텍스트로 나오므로, 일반 String으로 변환해줄 필요가 있습니다. result = unicodedata.normalize("NFC", pred) print(result) # 안녕하세요 123 테스트입니다. ```

357153ea48981220b2834c23fd847186

rafiulrumy/wav2vec2-large-xlsr-53-demo-colab

rafiulrumy

wav2vec2

21

8

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

2,657

false

# wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 6.7860 - Wer: 1.1067 ## 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: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.2273 | 44.42 | 400 | 3.3544 | 1.0 | | 0.9228 | 88.84 | 800 | 4.7054 | 1.1601 | | 0.1423 | 133.32 | 1200 | 5.9489 | 1.1578 | | 0.0751 | 177.74 | 1600 | 5.5939 | 1.1717 | | 0.0554 | 222.21 | 2000 | 6.1230 | 1.1717 | | 0.0356 | 266.63 | 2400 | 6.2845 | 1.1613 | | 0.0288 | 311.11 | 2800 | 6.6109 | 1.2100 | | 0.0223 | 355.53 | 3200 | 6.5605 | 1.1299 | | 0.0197 | 399.95 | 3600 | 7.1242 | 1.1682 | | 0.0171 | 444.42 | 4000 | 7.2452 | 1.1578 | | 0.0149 | 488.84 | 4400 | 7.4048 | 1.0684 | | 0.0118 | 533.32 | 4800 | 6.6227 | 1.1172 | | 0.011 | 577.74 | 5200 | 6.7909 | 1.1566 | | 0.0095 | 622.21 | 5600 | 6.8088 | 1.1102 | | 0.0077 | 666.63 | 6000 | 7.4451 | 1.1311 | | 0.0062 | 711.11 | 6400 | 6.8486 | 1.0777 | | 0.0051 | 755.53 | 6800 | 6.8812 | 1.1241 | | 0.0051 | 799.95 | 7200 | 6.9987 | 1.1450 | | 0.0041 | 844.42 | 7600 | 7.3048 | 1.1323 | | 0.0044 | 888.84 | 8000 | 6.6644 | 1.1125 | | 0.0031 | 933.32 | 8400 | 6.6298 | 1.1148 | | 0.0027 | 977.74 | 8800 | 6.7860 | 1.1067 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3

d025d802c993a50a1286bab7e281fbe8

vblagoje/dpr-question_encoder-single-lfqa-base

vblagoje

dpr

7

111

transformers

0

feature-extraction

true

false

mit

['en']

['vblagoje/lfqa']

null

0

[]

false

true

1,618

false

## Introduction The question encoder model based on [DPRQuestionEncoder](https://huggingface.co/docs/transformers/master/en/model_doc/dpr#transformers.DPRQuestionEncoder) architecture. It uses the transformer's pooler outputs as question representations. ## Training We trained vblagoje/dpr-question_encoder-single-lfqa-base using FAIR's dpr-scale starting with PAQ based pretrained checkpoint and fine-tuned the retriever on the question-answer pairs from the LFQA dataset. As dpr-scale requires DPR formatted training set input with positive, negative, and hard negative samples - we created a training file with an answer being positive, negatives being question unrelated answers, while hard negative samples were chosen from answers on questions between 0.55 and 0.65 of cosine similarity. ## Performance LFQA DPR-based retriever (vblagoje/dpr-question_encoder-single-lfqa-base and vblagoje/dpr-ctx_encoder-single-lfqa-base) had a score of 6.69 for R-precision and 14.5 for Recall@5 on KILT benchmark. ## Usage ```python from transformers import DPRContextEncoder, DPRContextEncoderTokenizer model = DPRQuestionEncoder.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base").to(device) tokenizer = AutoTokenizer.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base") input_ids = tokenizer("Why do airplanes leave contrails in the sky?", return_tensors="pt")["input_ids"] embeddings = model(input_ids).pooler_output ``` ## Author - Vladimir Blagojevic: `dovlex [at] gmail.com` [Twitter](https://twitter.com/vladblagoje) | [LinkedIn](https://www.linkedin.com/in/blagojevicvladimir/)

4ac79235cdc4bb8a1a6f2c38f6513404

drhyrum/bert-tiny-torch-vuln

drhyrum

bert

5

69

transformers

2

null

true

false

['mit']

['en']

null

0

['BERT', 'MNLI', 'NLI', 'transformer', 'pre-training']

false

true

2,457

false

The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) Other models to check out: - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)

9d6ced45b14770e55665a3c235da784c

PaddlePaddle/ernie-1.0-large-zh-cw

PaddlePaddle

ernie

7

0

paddlenlp

0

fill-mask

false

apache-2.0

['zh']

null

0

['fill-mask']

false

true

1,688

false

[![paddlenlp-banner](https://user-images.githubusercontent.com/1371212/175816733-8ec25eb0-9af3-4380-9218-27c154518258.png)](https://github.com/PaddlePaddle/PaddleNLP) # PaddlePaddle/ernie-1.0-large-zh-cw ## Introduction We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhanced by knowledge masking strategies, which includes entity-level masking and phrase-level masking. Entity-level strategy masks entities which are usually composed of multiple words. Phrase-level strategy masks the whole phrase which is composed of several words standing together as a conceptual unit. Experimental results show that ERNIE outperforms other baseline methods, achieving new state-of-the-art results on five Chinese natural language processing tasks including natural language inference, semantic similarity, named entity recognition, sentiment analysis and question answering. We also demonstrate that ERNIE has more powerful knowledge inference capacity on a cloze test. More detail: https://arxiv.org/abs/1904.09223 ## Available Models - ernie-1.0-base-zh - ernie-1.0-large-zh-cw ## How to Use? Click on the *Use in paddlenlp* button on the top right! ## Citation Info ```text @article{ernie2.0, title = {ERNIE: Enhanced Representation through Knowledge Integration}, author = {Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Chen, Xuyi and Zhang, Han and Tian, Xin and Zhu, Danxiang and Tian, Hao and Wu, Hua}, journal={arXiv preprint arXiv:1904.09223}, year = {2019}, } ```

acf97185027333132a9c563f9a1cca0c

jbreuch/bert-news-v2

jbreuch

bert

4

2

transformers

0

fill-mask

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,323

false

# bert-news-v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: ## 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': 7052, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.19.2 - TensorFlow 2.8.0 - Datasets 2.2.2 - Tokenizers 0.12.1

4714c1db2b858a68ecf80037ae8ba3e3

Duskfallcrew/duskfall-s-vaporwave-aesthetic

Duskfallcrew

null

21

5

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

['text-to-image']

false

true

1,293

false

[![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/Duskfallcrew/duskfall-s-vaporwave-aesthetic) ### Duskfall's Vaporwave Aesthetic Dreambooth model trained by Duskfallcrew with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You 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). Don't forget to use the concept prompts! ALL UPDATES TO ALL MODELS after training: https://civitai.com/user/duskfallcrew I'm having trouble RE uploading files after doing a dumb. SO you'll have to find the model when it's uploaded to civit! If you want to donate towards costs and don't want to subscribe: https://ko-fi.com/DUSKFALLcrew If you want to monthly support the EARTH & DUSK media projects and not just AI: https://www.patreon.com/earthndusk Discord: https://discord.gg/Da7s8d3KJ7 Discord server DOES have pluralkit installed.. This is because DUSKFALL and Earth & Dusk are plural friendly As well as the fact it's well known that Duskfallcrew and models are centered around a lot of the aesthetic and feeling of their battles with Neurodivergency and DID.

95c1b94e980f59eea6ccb2a30a76031b

asapp/sew-d-base-plus-400k

asapp

sew-d

5

2

transformers

0

feature-extraction

true

false

apache-2.0

['en']

['librispeech_asr']

null

0

['speech']

false

true

1,701

false

# SEW-D-base+ [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi **Abstract** This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.

9811fe399d6c2a6eb39eb16aa0191ed4

tomekkorbak/keen_clarke

tomekkorbak

gpt2

23

1

transformers

0

null

true

false

mit

['en']

['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000']

null

0

['generated_from_trainer']

true

8,886

false

# keen_clarke This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekkorbak/detoxify-pile-chunk3-250000-300000, the tomekkorbak/detoxify-pile-chunk3-300000-350000, the tomekkorbak/detoxify-pile-chunk3-350000-400000, the tomekkorbak/detoxify-pile-chunk3-400000-450000, the tomekkorbak/detoxify-pile-chunk3-450000-500000, the tomekkorbak/detoxify-pile-chunk3-500000-550000, the tomekkorbak/detoxify-pile-chunk3-550000-600000, the tomekkorbak/detoxify-pile-chunk3-600000-650000, the tomekkorbak/detoxify-pile-chunk3-650000-700000, the tomekkorbak/detoxify-pile-chunk3-700000-750000, the tomekkorbak/detoxify-pile-chunk3-750000-800000, the tomekkorbak/detoxify-pile-chunk3-800000-850000, the tomekkorbak/detoxify-pile-chunk3-850000-900000, the tomekkorbak/detoxify-pile-chunk3-900000-950000, the tomekkorbak/detoxify-pile-chunk3-950000-1000000, the tomekkorbak/detoxify-pile-chunk3-1000000-1050000, the tomekkorbak/detoxify-pile-chunk3-1050000-1100000, the tomekkorbak/detoxify-pile-chunk3-1100000-1150000, the tomekkorbak/detoxify-pile-chunk3-1150000-1200000, the tomekkorbak/detoxify-pile-chunk3-1200000-1250000, the tomekkorbak/detoxify-pile-chunk3-1250000-1300000, the tomekkorbak/detoxify-pile-chunk3-1300000-1350000, the tomekkorbak/detoxify-pile-chunk3-1350000-1400000, the tomekkorbak/detoxify-pile-chunk3-1400000-1450000, the tomekkorbak/detoxify-pile-chunk3-1450000-1500000, the tomekkorbak/detoxify-pile-chunk3-1500000-1550000, the tomekkorbak/detoxify-pile-chunk3-1550000-1600000, the tomekkorbak/detoxify-pile-chunk3-1600000-1650000, the tomekkorbak/detoxify-pile-chunk3-1650000-1700000, the tomekkorbak/detoxify-pile-chunk3-1700000-1750000, the tomekkorbak/detoxify-pile-chunk3-1750000-1800000, the tomekkorbak/detoxify-pile-chunk3-1800000-1850000, the tomekkorbak/detoxify-pile-chunk3-1850000-1900000 and the tomekkorbak/detoxify-pile-chunk3-1900000-1950000 datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 3147 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.24.0 - Pytorch 1.11.0+cu113 - Datasets 2.5.1 - Tokenizers 0.11.6 # Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', 'tomekkorbak/detoxify-pile-chunk3-200000-250000', 'tomekkorbak/detoxify-pile-chunk3-250000-300000', 'tomekkorbak/detoxify-pile-chunk3-300000-350000', 'tomekkorbak/detoxify-pile-chunk3-350000-400000', 'tomekkorbak/detoxify-pile-chunk3-400000-450000', 'tomekkorbak/detoxify-pile-chunk3-450000-500000', 'tomekkorbak/detoxify-pile-chunk3-500000-550000', 'tomekkorbak/detoxify-pile-chunk3-550000-600000', 'tomekkorbak/detoxify-pile-chunk3-600000-650000', 'tomekkorbak/detoxify-pile-chunk3-650000-700000', 'tomekkorbak/detoxify-pile-chunk3-700000-750000', 'tomekkorbak/detoxify-pile-chunk3-750000-800000', 'tomekkorbak/detoxify-pile-chunk3-800000-850000', 'tomekkorbak/detoxify-pile-chunk3-850000-900000', 'tomekkorbak/detoxify-pile-chunk3-900000-950000', 'tomekkorbak/detoxify-pile-chunk3-950000-1000000', 'tomekkorbak/detoxify-pile-chunk3-1000000-1050000', 'tomekkorbak/detoxify-pile-chunk3-1050000-1100000', 'tomekkorbak/detoxify-pile-chunk3-1100000-1150000', 'tomekkorbak/detoxify-pile-chunk3-1150000-1200000', 'tomekkorbak/detoxify-pile-chunk3-1200000-1250000', 'tomekkorbak/detoxify-pile-chunk3-1250000-1300000', 'tomekkorbak/detoxify-pile-chunk3-1300000-1350000', 'tomekkorbak/detoxify-pile-chunk3-1350000-1400000', 'tomekkorbak/detoxify-pile-chunk3-1400000-1450000', 'tomekkorbak/detoxify-pile-chunk3-1450000-1500000', 'tomekkorbak/detoxify-pile-chunk3-1500000-1550000', 'tomekkorbak/detoxify-pile-chunk3-1550000-1600000', 'tomekkorbak/detoxify-pile-chunk3-1600000-1650000', 'tomekkorbak/detoxify-pile-chunk3-1650000-1700000', 'tomekkorbak/detoxify-pile-chunk3-1700000-1750000', 'tomekkorbak/detoxify-pile-chunk3-1750000-1800000', 'tomekkorbak/detoxify-pile-chunk3-1800000-1850000', 'tomekkorbak/detoxify-pile-chunk3-1850000-1900000', 'tomekkorbak/detoxify-pile-chunk3-1900000-1950000'], 'is_split_by_sentences': True}, 'generation': {'every_n_steps': 16, 'force_call_on': [25354], 'metrics_configs': [{}, {'n': 1}, {'n': 2}, {'n': 5}], 'scenario_configs': [{'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 2048}, {'generate_kwargs': {'do_sample': True, 'max_length': 128, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'challenging_rtp', 'num_samples': 2048, 'prompts_path': 'resources/challenging_rtp.jsonl'}], 'scorer_config': {'device': 'cuda:0'}}, 'kl_gpt3_callback': {'every_n_steps': 16, 'force_call_on': [25354], 'max_tokens': 64, 'num_samples': 4096}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'value_head_config': {'is_detached': False}}, 'path_or_name': 'gpt2'}, 'objective': {'alpha': 1, 'beta': 10, 'name': 'AWR'}, 'tokenizer': {'path_or_name': 'gpt2'}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 1024, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'keen_clarke', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0005, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000, 'output_dir': 'training_output104340', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 1673, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/tomekkorbak/apo/runs/38k781c0

1abf80753dc3a86ee4f1553a517b02d0

PublicPrompts/All-In-One-Pixel-Model

PublicPrompts

null

18

309

diffusers

92

null

false

creativeml-openrail-m

null

4

0

4

0

[]

false

true

1,547

false

Stable Diffusion model trained using dreambooth to create pixel art, in 2 styles the sprite art can be used with the trigger word "pixelsprite" the scene art can be used with the trigger word "16bitscene" the art is not pixel perfect, but it can be fixed with pixelating tools like https://pinetools.com/pixelate-effect-image (they also have bulk pixelation) some example generations ![03044-1966091207-godzilla, in pixelsprite style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023237949-63507e5e18a4f616c9dfba19.png) ![00366-443747549-cute_cat_full_bodyin_pixelsprite_style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023239268-63507e5e18a4f616c9dfba19.png) ![02827-0-street in a sunny day. in 16bitscene style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023288054-63507e5e18a4f616c9dfba19.png) ![02829-0-magical alice in wonderland forest, in 16bitscene style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023291263-63507e5e18a4f616c9dfba19.png) ![02831-1-car driving away, synthwave outrun style wallpaper, in 16bitscene style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023267399-63507e5e18a4f616c9dfba19.png) ![02863-7-isometric living room, detailed, in 16bitscene style.png](https://s3.amazonaws.com/moonup/production/uploads/1668023243698-63507e5e18a4f616c9dfba19.png) ![02935-1805121122-dark arcade room, pink neon lights, detailed, in 16bitscene style,.png](https://s3.amazonaws.com/moonup/production/uploads/1668023243346-63507e5e18a4f616c9dfba19.png)

5adddcba414842c33d5abc2a5ba0fb23

bdh240901/wav2vec2-large-xls-r-300m-vi-colab

bdh240901

wav2vec2

13

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

['common_voice']

null

0

['generated_from_trainer']

true

1,100

false

# wav2vec2-large-xls-r-300m-vi-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

e3aaf39a2c57e325cc320a6005e3fa22

hyunjongkimmath/notation_identification

hyunjongkimmath

null

4

0

fastai

0

null

false

gpl-2.0

null

0

['fastai']

false

true

918

false

Details coming soon, in the meantime, see [`trouver`](https://github.com/hyunjongkimmath/trouver#use-an-ml-model-to-find-notations-introduced-in-text) to see how this model is used. # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

07c69aca34cba713912dc6aa2fe625a2

arize-ai/XLM-RoBERTa-xtreme-en

arize-ai

xlm-roberta

11

7

transformers

0

token-classification

true

false

mit

null

['xtreme_en']

null

0

['generated_from_trainer']

true

1,383

false

# XLM-RoBERTa-xtreme-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme_en dataset. It achieves the following results on the evaluation set: - Loss: 0.2838 - Accuracy: 0.9109 - F1: 0.7544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6502 | 1.0 | 235 | 0.3328 | 0.8995 | 0.7251 | | 0.3239 | 2.0 | 470 | 0.2897 | 0.9101 | 0.7473 | | 0.2644 | 3.0 | 705 | 0.2838 | 0.9109 | 0.7544 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

62cc4bc89ff6d1db5dd7a1272eb7e81d

sail/poolformer_m48

sail

poolformer

5

13

transformers

0

image-classification

true

false

apache-2.0

null

['imagenet']

null

0

['image-classification', 'vision']

false

true

5,124

false

# PoolFormer (M48 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) 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 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_m48') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_m48') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | # params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://huggingface.co/sail/poolformer_s12 | | PoolFormer-S24 | 80.3 | 21M | https://huggingface.co/sail/poolformer_s24 | | PoolFormer-S36 | 81.4 | 31M | https://huggingface.co/sail/poolformer_s36 | | PoolFormer-M36 | 82.1 | 56M | https://huggingface.co/sail/poolformer_m36 | | **PoolFormer-M48** | **82.5** | **73M** | **https://huggingface.co/sail/poolformer_m48** | ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```

de7fb73797698675d379eeb2481d0148

Davincilee/closure_system_door_inne-roberta-base

Davincilee

roberta

14

4

transformers

0

fill-mask

true

false

mit

null

0

['generated_from_trainer']

true

1,150

false

# closure_system_door_inne-roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6038 ## 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: 6 - eval_batch_size: 6 - 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 | |:-------------:|:-----:|:----:|:---------------:| | 1.3302 | 1.0 | 3 | 1.6837 | ### Framework versions - Transformers 4.19.0 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1

0b14c808861ea0783835303aebba3525

Helsinki-NLP/opus-mt-lt-fr

Helsinki-NLP

marian

10

36

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

768

false

### opus-mt-lt-fr * source languages: lt * target languages: fr * OPUS readme: [lt-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lt-fr/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/lt-fr/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lt-fr/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lt-fr/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.lt.fr | 22.0 | 0.428 |

a01b846083ee3c441dc1bf091697a56f

fathyshalab/all-roberta-large-v1-small_talk-6-16-5

fathyshalab

roberta

11

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,515

false

# all-roberta-large-v1-small_talk-6-16-5 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: 2.3566 - Accuracy: 0.3855 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7259 | 1.0 | 1 | 2.5917 | 0.2551 | | 2.217 | 2.0 | 2 | 2.5059 | 0.3275 | | 1.7237 | 3.0 | 3 | 2.4355 | 0.3768 | | 1.4001 | 4.0 | 4 | 2.3837 | 0.3739 | | 1.1937 | 5.0 | 5 | 2.3566 | 0.3855 | ### Framework versions - Transformers 4.20.0 - Pytorch 1.11.0+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

f0913442eece163391a4b3cc849cbe6c

anas-awadalla/roberta-large-houlsby-few-shot-k-32-finetuned-squad-seed-0

anas-awadalla

null

19

0

null

0

null

false

mit

null

['squad']

null

0

['generated_from_trainer']

true

1,096

false

# roberta-large-houlsby-few-shot-k-32-finetuned-squad-seed-0 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 0 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 75 ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cu113 - Datasets 2.0.0 - Tokenizers 0.11.6

c218605d1b3b0e89e3234497da991522

theovercomer8/proto-amy1

theovercomer8

null

18

0

diffusers

0

text-to-image

false

creativeml-openrail-m

null

0

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

false

true

425

false

### proto-amy1 Dreambooth model trained by theovercomer8 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:

8bb04309cc7ab1a512af838108c1adfc

halffried/midas_v3_dpt_large_384

halffried

null

3

0

null

0

null

false

mit

null

0

[]

false

true

754

false

## What is it? Just a mirror of a model from https://github.com/isl-org/MiDaS, to allow downloading with Huggingface Hub tools ## Citation ```bibtex @ARTICLE {Ranftl2022, author = "Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun", title = "Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer", journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence", year = "2022", volume = "44", number = "3" } ``` ```bibtex @article{Ranftl2021, author = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun}, title = {Vision Transformers for Dense Prediction}, journal = {ICCV}, year = {2021}, } ```

78e43932935f854e66ac6382ea120a3d

abdouaziiz/wav2vec2-xls-r-300m-wolof-lm

abdouaziiz

wav2vec2

13

21

transformers

0

automatic-speech-recognition

true

false

mit

null

0

['automatic-speech-recognition', 'asr', 'pytorch', 'wav2vec2', 'wolof', 'wo']

false

true

4,378

false

# wav2vec2-xls-r-300m-wolof-lm Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well represented, there are few resources in the field of Text en speech In this sense we aim to bring our contribution to this, it is in this sense that enters this repo. This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) ,with a language model that is fine-tuned with the largest available speech dataset of the [ALFFA_PUBLIC](https://github.com/besacier/ALFFA_PUBLIC/tree/master/ASR/WOLOF) It achieves the following results on the evaluation set: - Loss: 0.367826 - Wer: 0.212565 ## Model description The duration of the training data is 16.8 hours, which we have divided into 10,000 audio files for the training and 3,339 for the test. ## Training and evaluation data We eval the model at every 1500 step , and log it . and save at every 33340 step ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-4 - train_batch_size: 3 - eval_batch_size : 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10.0 ### Training results | Step | Training Loss | Validation Loss | Wer | |:-------:|:-------------:|:---------------:|:------:| | 1500 | 2.854200 |0.642243 |0.543964 | | 3000 | 0.599200 | 0.468138 | 0.429549| | 4500 | 0.468300 | 0.433436 | 0.405644| | 6000 | 0.427000 | 0.384873 | 0.344150| | 7500 | 0.377000 | 0.374003 | 0.323892| | 9000 | 0.337000 | 0.363674 | 0.306189| | 10500 | 0.302400 | 0.349884 |0 .283908 | | 12000 | 0.264100 | 0.344104 |0.277120| | 13500 |0 .254000 |0.341820 |0.271316| | 15000 | 0.208400| 0.326502 | 0.260695| | 16500 | 0.203500| 0.326209 | 0.250313| | 18000 |0.159800 |0.323539 | 0.239851| | 19500 | 0.158200 | 0.310694 | 0.230028| | 21000 | 0.132800 | 0.338318 | 0.229283| | 22500 | 0.112800 | 0.336765 | 0.224145| | 24000 | 0.103600 | 0.350208 | 0.227073 | | 25500 | 0.091400 | 0.353609 | 0.221589 | | 27000 | 0.084400 | 0.367826 | 0.212565 | ## Usage The model can be used directly as follows: ```python import librosa import warnings from transformers import AutoProcessor, AutoModelForCTC from datasets import Dataset, DatasetDict from datasets import load_metric wer_metric = load_metric("wer") wolof = pd.read_csv('Test.csv') # wolof contains the columns of file , and transcription wolof = DatasetDict({'test': Dataset.from_pandas(wolof)}) chars_to_ignore_regex = '[\"\?\.\!\-\;\:\,]' def remove_special_characters(batch): batch["transcription"] = re.sub(chars_to_ignore_regex, '', batch["transcription"]).lower() + " " return batch wolof = wolof.map(remove_special_characters) processor = AutoProcessor.from_pretrained("abdouaziiz/wav2vec2-xls-r-300m-wolof-lm") model = AutoModelForCTC.from_pretrained("abdouaziiz/wav2vec2-xls-r-300m-wolof-lm") warnings.filterwarnings("ignore") def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["file"], sr = 16000) batch["speech"] = speech_array.astype('float16') batch["sampling_rate"] = sampling_rate batch["target_text"] = batch["transcription"] return batch wolof = wolof.map(speech_file_to_array_fn, remove_columns=wolof.column_names["test"], num_proc=1) def map_to_result(batch): model.to("cuda") input_values = processor( batch["speech"], sampling_rate=batch["sampling_rate"], return_tensors="pt" ).input_values.to("cuda") with torch.no_grad(): logits = model(input_values).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_str"] = processor.batch_decode(pred_ids)[0] return batch results = wolof["test"].map(map_to_result) print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["transcription"]))) ``` ## PS: The results obtained can be improved by using : - Wav2vec2 + language model . - Build a Spellcheker from the text of the data - Sentence Edit Distance

71d6201fabac57746c2b6dfc847f5c8f

chandank/bart-base-finetuned-kaggglenews-batch8

chandank

bart

13

1

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,245

false

# bart-base-finetuned-kaggglenews-batch8 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:| | No log | 1.0 | 495 | 1.6409 | 27.9647 | 15.4352 | 23.611 | 25.107 | 20.0 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

d2b08cd91ecf93f2c33fcc07b78ef111

nlp-waseda/roberta-base-japanese

nlp-waseda

roberta

7

3,787

transformers

15

fill-mask

true

false

cc-by-sa-4.0

['ja']

['wikipedia', 'cc100']

null

0

1

0

1

[]

false

true

2,126

false

# nlp-waseda/roberta-base-japanese ## Model description This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-base-japanese") sentence = '早稲田大学で自然言語処理を [MASK] する。' # input should be segmented into words by Juman++ in advance encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. ## Tokenization The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). `BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-base-japanese-with-auto-jumanpp) without any data preprocessing. ## Vocabulary The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). ## Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took a week using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 1e-4 - per_device_train_batch_size: 256 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 4096 - max_seq_length: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 700000 - warmup_steps: 10000 - mixed_precision_training: Native AMP ## Performance on JGLUE See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.

d92f67924d835b4c8f4b9f57e7bbe79f

omar47/wav2vec2-large-xls-r-300m-urdu-v2

omar47

wav2vec2

19

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

4,132

false

# wav2vec2-large-xls-r-300m-urdu-CV_8_0-and-PRUS_v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3541 - Wer: 0.6532 ## 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: 25 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 14.8521 | 0.52 | 32 | 20.0617 | 1.0 | | 9.2152 | 1.05 | 64 | 7.8943 | 1.0 | | 4.8598 | 1.57 | 96 | 5.1558 | 1.0 | | 3.866 | 2.1 | 128 | 3.9680 | 1.0 | | 3.3517 | 2.62 | 160 | 3.4201 | 1.0 | | 3.2029 | 3.15 | 192 | 3.2355 | 1.0 | | 3.1509 | 3.67 | 224 | 3.2337 | 1.0 | | 3.1399 | 4.2 | 256 | 3.1627 | 1.0 | | 3.0848 | 4.72 | 288 | 3.0550 | 1.0 | | 2.9806 | 5.25 | 320 | 2.8343 | 0.9996 | | 2.3814 | 5.77 | 352 | 2.0685 | 0.9523 | | 1.2936 | 6.3 | 384 | 1.5907 | 0.8657 | | 0.8656 | 6.82 | 416 | 1.3810 | 0.8235 | | 0.7014 | 7.34 | 448 | 1.3838 | 0.7920 | | 0.6015 | 7.87 | 480 | 1.3479 | 0.8046 | | 0.5341 | 8.39 | 512 | 1.2613 | 0.7757 | | 0.5031 | 8.92 | 544 | 1.2818 | 0.7890 | | 0.4349 | 9.44 | 576 | 1.3171 | 0.7739 | | 0.4198 | 9.97 | 608 | 1.2420 | 0.7750 | | 0.3593 | 10.49 | 640 | 1.2991 | 0.7587 | | 0.3252 | 11.02 | 672 | 1.2653 | 0.7228 | | 0.2715 | 11.54 | 704 | 1.2488 | 0.7350 | | 0.2733 | 12.07 | 736 | 1.2639 | 0.7110 | | 0.2338 | 12.59 | 768 | 1.3733 | 0.7454 | | 0.2403 | 13.11 | 800 | 1.3908 | 0.7228 | | 0.2106 | 13.64 | 832 | 1.3384 | 0.7224 | | 0.2041 | 14.16 | 864 | 1.3770 | 0.7050 | | 0.1814 | 14.69 | 896 | 1.3526 | 0.6932 | | 0.1742 | 15.21 | 928 | 1.3486 | 0.6895 | | 0.1658 | 15.74 | 960 | 1.3210 | 0.6936 | | 0.1455 | 16.26 | 992 | 1.3292 | 0.6858 | | 0.1399 | 16.79 | 1024 | 1.3521 | 0.6828 | | 0.1325 | 17.31 | 1056 | 1.3339 | 0.6876 | | 0.1256 | 17.84 | 1088 | 1.3389 | 0.6836 | | 0.1219 | 18.36 | 1120 | 1.3496 | 0.6769 | | 0.1212 | 18.89 | 1152 | 1.3277 | 0.6776 | | 0.1097 | 19.41 | 1184 | 1.3594 | 0.6762 | | 0.1129 | 19.93 | 1216 | 1.3448 | 0.6688 | | 0.1036 | 20.46 | 1248 | 1.3295 | 0.6710 | | 0.1035 | 20.98 | 1280 | 1.3243 | 0.6577 | | 0.094 | 21.51 | 1312 | 1.3832 | 0.6591 | | 0.0912 | 22.03 | 1344 | 1.3857 | 0.6584 | | 0.0815 | 22.56 | 1376 | 1.3739 | 0.6547 | | 0.0864 | 23.08 | 1408 | 1.3649 | 0.6554 | | 0.0772 | 23.61 | 1440 | 1.3791 | 0.6458 | | 0.0894 | 24.13 | 1472 | 1.3630 | 0.6488 | | 0.0776 | 24.66 | 1504 | 1.3541 | 0.6532 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1

a6b838dbd5e1561b0ea74cfdef943026

marifulhaque/wav2vec2-large-teacher-en-asr-timit

marifulhaque

wav2vec2

16

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,768

false

# wav2vec2-large-teacher-en-asr-timit This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4160 - Wer: 0.2984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.4952 | 3.17 | 200 | 3.0555 | 1.0 | | 2.5341 | 6.35 | 400 | 0.8144 | 0.7441 | | 0.694 | 9.52 | 600 | 0.4154 | 0.4572 | | 0.3593 | 12.7 | 800 | 0.4260 | 0.3890 | | 0.2567 | 15.87 | 1000 | 0.4166 | 0.3614 | | 0.1988 | 19.05 | 1200 | 0.3912 | 0.3346 | | 0.1338 | 22.22 | 1400 | 0.4000 | 0.3178 | | 0.1044 | 25.4 | 1600 | 0.4425 | 0.3071 | | 0.0786 | 28.57 | 1800 | 0.4160 | 0.2984 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

2cb241c3bb136ddd070814c32a907917

fatenghali/text_classification_model

fatenghali

distilbert

16

43

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,266

false

# text_classification_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3686 - F1: 0.8968 ## 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 | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2356 | 1.0 | 7215 | 0.3704 | 0.8946 | | 0.2011 | 2.0 | 14430 | 0.3686 | 0.8968 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

e1f0defb5039c4ed6feb2ffc59426b00

Saisam/gpt-neo-math-small

Saisam

gpt_neo

9

2

transformers

0

text-generation

true

false

apache-2.0

null

0

[]

false

true

656

false

# GPT-NEO-Model for Lean Tactics In the project, we used an HuggingFace GPT-NEO small model and fine-tuned the tactic dataset. The Input should be of the form ``` <GOAL> Goal <PROOFSTEP> ``` The model can easily be accessed using the following code. ``` from transformers import GPT2Tokenizer, GPTNeoForCausalLM import torch tokenizer = GPT2Tokenizer.from_pretrained("Saisam/gpt-neo-math-small") model = GPTNeoForCausalLM.from_pretrained("Saisam/gpt-neo-math-small") ``` More Information can be found at https://github.com/saisurbehera/mathProof. The current model beats the GPT-F for minif2f benchmark Worked along with Xihao Xhang and Moya Zhu

9f2eaa2f3cb164d08aaab7633e3f530e

yhavinga/ul2-small-dutch-english

yhavinga

t5

15

57

transformers

0

text2text-generation

true

false

true

apache-2.0

['nl', 'en', 'multilingual']

['yhavinga/mc4_nl_cleaned', 'yhavinga/nedd_wiki_news']

null

0

['dutch', 'english', 't5', 't5x', 'ul2', 'seq2seq']

false

true

10,327

false

# ul2-small-dutch-english for Dutch and English Pretrained T5 model on Dutch and English using a UL2 (Mixture-of-Denoisers) objective. The T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was introduced in [this paper](https://arxiv.org/abs/2205.05131) and first released at [this page](https://github.com/google-research/google-research/tree/master/ul2). **Note:** The Hugging Face inference widget is deactivated because this model needs a text-to-text fine-tuning on a specific downstream task to be useful in practice. ## Model description T5 is an encoder-decoder model and treats all NLP problems in a text-to-text format. `ul2-small-dutch-english` T5 is a transformers model pretrained on a very large corpus of Dutch and 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 outputs from those texts. This model used the [T5 v1.1](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in the feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off during pre-training. Dropout should be re-enabled during fine-tuning - Pre-trained on self-supervised objective only without mixing in the downstream tasks - No parameter sharing between embedding and classifier layer ### UL2 pretraining objective This model was pretrained with the UL2's Mixture-of-Denoisers (MoD) objective, that combines diverse pre-training paradigms together. UL2 frames different objective functions for training language models as denoising tasks, where the model has to recover missing sub-sequences of a given input. During pre-training it uses a novel mixture-of-denoisers that samples from a varied set of such objectives, each with different configurations. UL2 is trained using a mixture of three denoising tasks: 1. R-denoising (or regular span corruption), which emulates the standard T5 span corruption objective; 2. X-denoising (or extreme span corruption); and 3. S-denoising (or sequential PrefixLM). During pre-training, we sample from the available denoising tasks based on user-specified ratios. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training denoising task. During the pre-training, a paradigm token is inserted to the input (`[NLU]` for R-denoising, `[NLG]` for X-denoising, or `[S2S]` for S-denoising) indicating the denoising task at hand. Then, during fine-tuning the same input token should be inserted to get the best performance for different downstream fine-tuning tasks. ## Intended uses & limitations This model was only pretrained in a self-supervised way excluding any supervised training. Therefore, this model has to be fine-tuned before it is usable on a downstream task, like text classification, unlike the Google's original T5 model. **Note:** You most likely need to fine-tune these T5/UL2 models without mixed precision so fine-tune them with full fp32 precision. Fine-tuning with Flax in bf16 - `model.to_bf16()` - is possible if you set the mask correctly to exclude layernorm and embedding layers. Also note that the T5x pre-training and fine-tuning configs set `z_loss` to 1e-4, which is used to keep the loss scale from underflowing. You can also find more fine-tuning tips from [here](https://discuss.huggingface.co/t/t5-finetuning-tips), for example. **Note**: For fine-tuning, most likely you can get better results if you insert a prefix token of `[NLU]`, `[NLG]`, or `[S2S]` to your input texts. For general language understanding fine-tuning tasks, you could use the `[NLU]` token. For GPT-style causal language generation, you could use the `[S2S]` token. The token `[NLG]` of the X-denoising pretrain task is somewhat mix between the language understanding and causal language generation so the token `[NLG]` could maybe be used for language generation fine-tuning too. ### How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch-english", use_fast=False) model = T5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch-english") ``` and in Flax: ```python from transformers import T5Tokenizer, FlaxT5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("yhavinga/ul2-small-dutch-english", use_fast=False) model = FlaxT5ForConditionalGeneration.from_pretrained("yhavinga/ul2-small-dutch-english") ``` ### Limitations and bias The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data The `ul2-small-dutch-english` T5 model was pre-trained simultaneously on a combination of several datasets, including the `full_en_nl` config of the "mc4_nl_cleaned" dataset, which is a cleaned version of Common Crawl's web crawl corpus, Dutch books, the Dutch subset of Wikipedia (2022-03-20), the English subset of Wikipedia (2022-03-01), and a subset of "mc4_nl_cleaned" containing only texts from Dutch and Belgian newspapers. This last dataset is oversampled to bias the model towards descriptions of events in the Netherlands and Belgium. ## Training procedure ### Preprocessing The ul2-small-dutch-english T5 model uses a SentencePiece unigram tokenizer with a vocabulary of 32,000 tokens. The tokenizer includes the special tokens `<pad>`, `</s>`, `<unk>`, known from the original T5 paper, `[NLU]`, `[NLG]` and `[S2S]` for the MoD pre-training, and `<n>` for newline. During pre-training with the UL2 objective, input and output sequences consist of 512 consecutive tokens. The tokenizer does not lowercase texts and is therefore case-sensitive; it distinguises between `dutch` and `Dutch`. Additionally, 100+28 extra tokens were added for pre-training tasks, resulting in a total of 32,128 tokens. ### Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1000000 steps with a batch size of 128 (in total 65 B tokens). The optimizer used was AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and then an inverse square root decay (exponential decay) of the learning rate after. The model was trained with Google's Jax/Flax based [t5x framework](https://github.com/google-research/t5x) with help from [Stephenn Fernandes](https://huggingface.co/StephennFernandes) to get started writing task definitions that wrap HF datasets. The UL2 training objective code used with the [t5x framework](https://github.com/google-research/t5x) was copied and slightly modified from the [UL2 paper](https://arxiv.org/pdf/2205.05131.pdf) appendix chapter 9.2 by the authors of the Finnish ul2 models. Used UL2 objective code is available in the repository [Finnish-NLP/ul2-base-nl36-finnish](https://huggingface.co/Finnish-NLP/ul2-base-nl36-finnish) in the files `ul2_objective.py` and `tasks.py`. UL2's mixture-of-denoisers configuration was otherwise equal to the UL2 paper but for the rate of mixing denoisers, 20% for S-denoising was used (suggested at the paper chapter 4.5) and the rest was divided equally between the R-denoising and X-denoising (i.e. 40% for both). ### Model list Models in this series: | | ul2-base-dutch-english | ul2-large-dutch-english | ul2-small-dutch-english | |:---------------------|:-------------------------|:--------------------------|:--------------------------| | model_type | t5 | t5 | t5 | | _pipeline_tag | text2text-generation | text2text-generation | text2text-generation | | d_model | 768 | 1024 | 512 | | d_ff | 2048 | 2816 | 1024 | | num_heads | 12 | 16 | 6 | | d_kv | 64 | 64 | 64 | | num_layers | 12 | 24 | 8 | | num_decoder_layers | 12 | 24 | 8 | | feed_forward_proj | gated-gelu | gated-gelu | gated-gelu | | dense_act_fn | gelu_new | gelu_new | gelu_new | | vocab_size | 32128 | 32128 | 32128 | | tie_word_embeddings | 0 | 0 | 0 | | torch_dtype | float32 | float32 | float32 | | _gin_batch_size | 128 | 64 | 128 | | _gin_z_loss | 0.0001 | 0.0001 | 0.0001 | | _gin_t5_config_dtype | 'bfloat16' | 'bfloat16' | 'bfloat16' | ## Evaluation results See the evaluation section in the interactive [Pre-training Dutch T5 Models](https://huggingface.co/spaces/yhavinga/pre-training-dutch-t5-models) blog. ## Acknowledgements This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/). Thanks to the [Finnish-NLP](https://huggingface.co/Finnish-NLP) authors for releasing their code for the UL2 objective and associated task definitions. Thanks to [Stephenn Fernandes](https://huggingface.co/StephennFernandes) for helping me get started with the t5x framework. Created by [Yeb Havinga](https://www.linkedin.com/in/yeb-havinga-86530825/)

a90e3dff1e499ef32db307f7fc00b77b

muhtasham/small-mlm-glue-stsb-target-glue-qqp

muhtasham

bert

10

4

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,934

false

# small-mlm-glue-stsb-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-stsb](https://huggingface.co/muhtasham/small-mlm-glue-stsb) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3294 - Accuracy: 0.8525 - F1: 0.8131 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4739 | 0.04 | 500 | 0.4259 | 0.7919 | 0.7514 | | 0.4186 | 0.09 | 1000 | 0.3841 | 0.8190 | 0.7709 | | 0.3984 | 0.13 | 1500 | 0.3737 | 0.8228 | 0.7757 | | 0.3853 | 0.18 | 2000 | 0.3725 | 0.8228 | 0.7878 | | 0.3761 | 0.22 | 2500 | 0.3558 | 0.8362 | 0.7969 | | 0.3616 | 0.26 | 3000 | 0.3434 | 0.8418 | 0.8010 | | 0.3616 | 0.31 | 3500 | 0.3286 | 0.8504 | 0.8008 | | 0.3528 | 0.35 | 4000 | 0.3293 | 0.8513 | 0.8110 | | 0.358 | 0.4 | 4500 | 0.3213 | 0.8539 | 0.8104 | | 0.3428 | 0.44 | 5000 | 0.3294 | 0.8525 | 0.8131 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

743e91398e6a321e6bf6105d347407d2

jonatasgrosman/wav2vec2-large-xlsr-53-portuguese

jonatasgrosman

wav2vec2

24

11,152

transformers

10

automatic-speech-recognition

true

false

true

apache-2.0

['pt']

['common_voice', 'mozilla-foundation/common_voice_6_0']

null

0

['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'pt', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week']

true

4,047

false

# Fine-tuned XLSR-53 large model for speech recognition in Portuguese Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint ## Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-portuguese") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "pt" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) ``` | Reference | Prediction | | ------------- | ------------- | | NEM O RADAR NEM OS OUTROS INSTRUMENTOS DETECTARAM O BOMBARDEIRO STEALTH. | NEMHUM VADAN OS OLTWES INSTRUMENTOS DE TTÉÃN UM BOMBERDEIRO OSTER | | PEDIR DINHEIRO EMPRESTADO ÀS PESSOAS DA ALDEIA | E DIR ENGINHEIRO EMPRESTAR AS PESSOAS DA ALDEIA | | OITO | OITO | | TRANCÁ-LOS | TRANCAUVOS | | REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA | REALIZAR UMA INVESTIGAÇÃO PARA RESOLVER O PROBLEMA | | O YOUTUBE AINDA É A MELHOR PLATAFORMA DE VÍDEOS. | YOUTUBE AINDA É A MELHOR PLATAFOMA DE VÍDEOS | | MENINA E MENINO BEIJANDO NAS SOMBRAS | MENINA E MENINO BEIJANDO NAS SOMBRAS | | EU SOU O SENHOR | EU SOU O SENHOR | | DUAS MULHERES QUE SENTAM-SE PARA BAIXO LENDO JORNAIS. | DUAS MIERES QUE SENTAM-SE PARA BAICLANE JODNÓI | | EU ORIGINALMENTE ESPERAVA | EU ORIGINALMENTE ESPERAVA | ## Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset mozilla-foundation/common_voice_6_0 --config pt --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-portuguese --dataset speech-recognition-community-v2/dev_data --config pt --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-portuguese, title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}ortuguese}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese}}, year={2021} } ```

1bae249e8dec2c85bd82191387574a28

google/multiberts-seed_4-step_180k

google

bert

8

12

transformers

0

null

true

false

apache-2.0

['en']

null

0

['multiberts', 'multiberts-seed_4', 'multiberts-seed_4-step_180k']

false

true

3,521

false

# MultiBERTs, Intermediate Checkpoint - Seed 4, Step 180k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model #4, captured at step 180k (max: 2000k, i.e., 2M steps). ## Model Description This model was captured during a reproduction of [BERT-base uncased](https://github.com/google-research/bert), for English: it is a Transformers model pretrained on a large corpus of English data, using the Masked Language Modelling (MLM) and the Next Sentence Prediction (NSP) objectives. The intended uses, limitations, training data and training procedure for the fully trained model are similar to [BERT-base uncased](https://github.com/google-research/bert). Two major differences with the original model: * We pre-trained the MultiBERTs models for 2 million steps using sequence length 512 (instead of 1 million steps using sequence length 128 then 512). * We used an alternative version of Wikipedia and Books Corpus, initially collected for [Turc et al., 2019](https://arxiv.org/abs/1908.08962). This is a best-effort reproduction, and so it is probable that differences with the original model have gone unnoticed. The performance of MultiBERTs on GLUE after full training is oftentimes comparable to that of original BERT, but we found significant differences on the dev set of SQuAD (MultiBERTs outperforms original BERT). See our [technical report](https://arxiv.org/abs/2106.16163) for more details. ### How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_180k') model = TFBertModel.from_pretrained("google/multiberts-seed_4-step_180k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_4-step_180k') model = BertModel.from_pretrained("google/multiberts-seed_4-step_180k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ## Citation info ```bibtex @article{sellam2021multiberts, title={The MultiBERTs: BERT Reproductions for Robustness Analysis}, author={Thibault Sellam and Steve Yadlowsky and Jason Wei and Naomi Saphra and Alexander D'Amour and Tal Linzen and Jasmijn Bastings and Iulia Turc and Jacob Eisenstein and Dipanjan Das and Ian Tenney and Ellie Pavlick}, journal={arXiv preprint arXiv:2106.16163}, year={2021} } ```

14b2658e0b4ac9c27aa39a9d831e22ba

akusov/durka-fusion

akusov

null

16

185

diffusers

0

text-to-image

false

creativeml-openrail-m

null

1

0

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

false

true

420

false

### durka-fusion Dreambooth model trained by akusov 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:

b261c1ac793fe06aa494d9fa62fb5dd2

schoenml/bert-emotion

schoenml

distilbert

12

3

transformers

0

text-classification

true

false

apache-2.0

null

['tweet_eval']

null

0

['generated_from_trainer']

true

1,455

false

# bert-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.1531 - Precision: 0.7296 - Recall: 0.7266 - Fscore: 0.7278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8418 | 1.0 | 815 | 0.8129 | 0.7960 | 0.6242 | 0.6420 | | 0.5222 | 2.0 | 1630 | 0.9663 | 0.7584 | 0.7196 | 0.7324 | | 0.2662 | 3.0 | 2445 | 1.1531 | 0.7296 | 0.7266 | 0.7278 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

f6582e31914fb984c365686b6c9c1de0

Helsinki-NLP/opus-mt-toi-en

Helsinki-NLP

marian

10

11

transformers

0

translation

true

false

apache-2.0

null

0

['translation']

false

true

776

false

### opus-mt-toi-en * source languages: toi * target languages: en * OPUS readme: [toi-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/toi-en/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/toi-en/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/toi-en/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/toi-en/opus-2020-01-16.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | JW300.toi.en | 39.0 | 0.539 |

c4520396bd0e2224892ce84263361a77

s50227harry/TCFD-BERT

s50227harry

roberta

10

5

transformers

0

fill-mask

true

false

apache-2.0

null

0

1

0

['generated_from_trainer']

true

1,843

false

Using the ClimateBERT-f model as starting point,the TCFD-BERT language model is additionally pre-trained to include precise paragraphs related to climate change.  # TCFD-BERT It achieves the following results on the evaluation set: - Loss: 1.1325 ## 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: 8 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.865 | 0.37 | 500 | 1.4460 | | 1.6601 | 0.73 | 1000 | 1.3491 | | 1.593 | 1.1 | 1500 | 1.3190 | | 1.5336 | 1.46 | 2000 | 1.2801 | | 1.5081 | 1.83 | 2500 | 1.2446 | | 1.4547 | 2.19 | 3000 | 1.2281 | | 1.4358 | 2.56 | 3500 | 1.2065 | | 1.4121 | 2.92 | 4000 | 1.1874 | | 1.396 | 3.29 | 4500 | 1.1817 | | 1.383 | 3.65 | 5000 | 1.1747 | | 1.3662 | 4.02 | 5500 | 1.1717 | | 1.3545 | 4.38 | 6000 | 1.1567 | | 1.3441 | 4.75 | 6500 | 1.1325 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.0+cu102 - Datasets 2.1.0 - Tokenizers 0.12.1

bc3d1221f2f020354e2be38644138f51

patrickvonplaten/deberta_v3_amazon_reviews

patrickvonplaten

deberta-v2

19

5

transformers

0

text-classification

true

false

mit

null

1

0

['generated_from_trainer']

true

984

false

# deberta_v3_amazon_reviews This model is a fine-tuned version of [patrickvonplaten/deberta_v3_amazon_reviews](https://huggingface.co/patrickvonplaten/deberta_v3_amazon_reviews) 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 2 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

9a0ccc8454f2af4d8f425ebb365bbda0

nlpaueb/bert-base-uncased-eurlex

nlpaueb

bert

8

264

transformers

5

fill-mask

true

cc-by-sa-4.0

['en']

null

0

['legal']

false

true

10,956

false

# LEGAL-BERT: The Muppets straight out of Law School <img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/> LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-train the different variations of LEGAL-BERT, we collected 12 GB of diverse English legal text from several fields (e.g., legislation, court cases, contracts) scraped from publicly available resources. Sub-domain variants (CONTRACTS-, EURLEX-, ECHR-) and/or general LEGAL-BERT perform better than using BERT out of the box for domain-specific tasks. This is the sub-domain variant pre-trained on EU legislation. --- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, N. Aletras and I. Androutsopoulos. "LEGAL-BERT: The Muppets straight out of Law School". In Findings of Empirical Methods in Natural Language Processing (EMNLP 2020) (Short Papers), to be held online, 2020. (https://aclanthology.org/2020.findings-emnlp.261) --- ## Pre-training corpora The pre-training corpora of LEGAL-BERT include: * 116,062 documents of EU legislation, publicly available from EURLEX (http://eur-lex.europa.eu), the repository of EU Law running under the EU Publication Office. * 61,826 documents of UK legislation, publicly available from the UK legislation portal (http://www.legislation.gov.uk). * 19,867 cases from the European Court of Justice (ECJ), also available from EURLEX. * 12,554 cases from HUDOC, the repository of the European Court of Human Rights (ECHR) (http://hudoc.echr.coe.int/eng). * 164,141 cases from various courts across the USA, hosted in the Case Law Access Project portal (https://case.law). * 76,366 US contracts from EDGAR, the database of US Securities and Exchange Commission (SECOM) (https://www.sec.gov/edgar.shtml). ## Pre-training details * We trained BERT using the official code provided in Google BERT's GitHub repository (https://github.com/google-research/bert). * We released a model similar to the English BERT-BASE model (12-layer, 768-hidden, 12-heads, 110M parameters). * We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4. * We were able to use a single Google Cloud TPU v3-8 provided for free from [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc), while also utilizing [GCP research credits](https://edu.google.com/programs/credits/research). Huge thanks to both Google programs for supporting us! ## Models list | Model name | Model Path | Training corpora | | ------------------- | ------------------------------------ | ------------------- | | CONTRACTS-BERT-BASE | `nlpaueb/bert-base-uncased-contracts` | US contracts | | EURLEX-BERT-BASE | `nlpaueb/bert-base-uncased-eurlex` | EU legislation | | ECHR-BERT-BASE | `nlpaueb/bert-base-uncased-echr` | ECHR cases | | LEGAL-BERT-BASE * | `nlpaueb/legal-bert-base-uncased` | All | | LEGAL-BERT-SMALL | `nlpaueb/legal-bert-small-uncased` | All | \* LEGAL-BERT-BASE is the model referred to as LEGAL-BERT-SC in Chalkidis et al. (2020); a model trained from scratch in the legal corpora mentioned below using a newly created vocabulary by a sentence-piece tokenizer trained on the very same corpora. \*\* As many of you expressed interest in the LEGAL-BERT-FP models (those relying on the original BERT-BASE checkpoint), they have been released in Archive.org (https://archive.org/details/legal_bert_fp), as these models are secondary and possibly only interesting for those who aim to dig deeper in the open questions of Chalkidis et al. (2020). ## Load Pretrained Model ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-uncased-eurlex") model = AutoModel.from_pretrained("nlpaueb/bert-base-uncased-eurlex") ``` ## Use LEGAL-BERT variants as Language Models | Corpus | Model | Masked token | Predictions | | --------------------------------- | ---------------------------------- | ------------ | ------------ | | | **BERT-BASE-UNCASED** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('new', '0.09'), ('current', '0.04'), ('proposed', '0.03'), ('marketing', '0.03'), ('joint', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.32'), ('rape', '0.22'), ('abuse', '0.14'), ('death', '0.04'), ('violence', '0.03') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('farm', '0.25'), ('livestock', '0.08'), ('draft', '0.06'), ('domestic', '0.05'), ('wild', '0.05') | | **CONTRACTS-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('letter', '0.38'), ('dealer', '0.04'), ('employment', '0.03'), ('award', '0.03'), ('contribution', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('death', '0.39'), ('imprisonment', '0.07'), ('contempt', '0.05'), ('being', '0.03'), ('crime', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | (('domestic', '0.18'), ('laboratory', '0.07'), ('household', '0.06'), ('personal', '0.06'), ('the', '0.04') | | **EURLEX-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('supply', '0.11'), ('cooperation', '0.08'), ('service', '0.07'), ('licence', '0.07'), ('distribution', '0.05') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.66'), ('death', '0.07'), ('imprisonment', '0.07'), ('murder', '0.04'), ('rape', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.43'), ('pet', '0.28'), ('certain', '0.05'), ('fur', '0.03'), ('the', '0.02') | | **ECHR-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('second', '0.24'), ('latter', '0.10'), ('draft', '0.05'), ('bilateral', '0.05'), ('arbitration', '0.04') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.99'), ('death', '0.01'), ('inhuman', '0.00'), ('beating', '0.00'), ('rape', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('pet', '0.17'), ('all', '0.12'), ('slaughtered', '0.10'), ('domestic', '0.07'), ('individual', '0.05') | | **LEGAL-BERT-BASE** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('settlement', '0.26'), ('letter', '0.23'), ('dealer', '0.04'), ('master', '0.02'), ('supplemental', '0.02') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '1.00'), ('detention', '0.00'), ('arrest', '0.00'), ('rape', '0.00'), ('death', '0.00') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('live', '0.67'), ('beef', '0.17'), ('farm', '0.03'), ('pet', '0.02'), ('dairy', '0.01') | | **LEGAL-BERT-SMALL** | | (Contracts) | This [MASK] Agreement is between General Motors and John Murray . | employment | ('license', '0.09'), ('transition', '0.08'), ('settlement', '0.04'), ('consent', '0.03'), ('letter', '0.03') | (ECHR) | The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Security Directorate | torture | ('torture', '0.59'), ('pain', '0.05'), ('ptsd', '0.05'), ('death', '0.02'), ('tuberculosis', '0.02') | (EURLEX) | Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and beef products . | bovine | ('all', '0.08'), ('live', '0.07'), ('certain', '0.07'), ('the', '0.07'), ('farm', '0.05') ## Evaluation on downstream tasks Consider the experiments in the article "LEGAL-BERT: The Muppets straight out of Law School". Chalkidis et al., 2020, (https://aclanthology.org/2020.findings-emnlp.261) ## Author - Publication ``` @inproceedings{chalkidis-etal-2020-legal, title = "{LEGAL}-{BERT}: The Muppets straight out of Law School", author = "Chalkidis, Ilias and Fergadiotis, Manos and Malakasiotis, Prodromos and Aletras, Nikolaos and Androutsopoulos, Ion", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", doi = "10.18653/v1/2020.findings-emnlp.261", pages = "2898--2904" } ``` ## About Us [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. [Ilias Chalkidis](https://iliaschalkidis.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr) | Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) |

2fec84f90a208d74917e9c3c5dc0fc4a

hady/wav2vec2-base-timit-demo-colab

hady

wav2vec2

14

7

transformers

0

automatic-speech-recognition

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,014

false

# wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

545cd28a2a32bf455ae6d18699569139

ZubairAzimMiazi/whisper-small-bn

ZubairAzimMiazi

whisper

7

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['bn']

['mozilla-foundation/common_voice_11_0']

null

0

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

true

966

false

# Whisper Small Bn - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1 - Datasets 2.8.0 - Tokenizers 0.13.2

cf56f2113922ce5039476a0664b607fb

wietsedv/xlm-roberta-base-ft-udpos28-la

wietsedv

xlm-roberta

8

7

transformers

0

token-classification

true

false

apache-2.0

['la']

['universal_dependencies']

null

0

['part-of-speech', 'token-classification']

true

565

false

# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Latin This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-la") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-la") ```

3c9e284177e4d276fd0e677c9cca0f45

monakth/distilbert-base-cased-finetuned-squad

monakth

distilbert

12

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

0

['generated_from_trainer']

true

1,278

false

# distilbert-base-cased-finetuned-squad This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1787 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2453 | 1.0 | 5546 | 1.2056 | | 0.9606 | 2.0 | 11092 | 1.1385 | | 0.7447 | 3.0 | 16638 | 1.1787 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.12.1

082611090884d2128b007daf9e192a4a

microsoft/deberta-v2-xxlarge

microsoft

deberta-v2

8

9,165

transformers

12

fill-mask

true

false

mit

['en']

null

1

0

1

0

['deberta', 'fill-mask']

false

true

4,606

false

## DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. This is the DeBERTa V2 xxlarge model with 48 layers, 1536 hidden size. The total parameters are 1.5B and it is trained with 160GB raw data. ### Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and several GLUE benchmark tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m/mm | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B | |---------------------------|-----------|-----------|-------------|-------|------|------|--------|-------|-------|------| | | F1/EM | F1/EM | Acc | Acc | Acc | MCC | Acc |Acc/F1 |Acc/F1 |P/S | | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6/- | 93.2 | 92.3 | 60.6 | 70.4 | 88.0/- | 91.3/- |90.0/- | | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2/- | 96.4 | 93.9 | 68.0 | 86.6 | 90.9/- | 92.2/- |92.4/- | | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8/- | 97.0 | 94.9 | 69.0 | 85.9 | 90.8/- | 92.3/- |92.5/- | | [DeBERTa-Large](https://huggingface.co/microsoft/deberta-large)1 | 95.5/90.1 | 90.7/88.0 | 91.3/91.1| 96.5|95.3| 69.5| 91.0| 92.6/94.6| 92.3/- |92.8/92.5 | | [DeBERTa-XLarge](https://huggingface.co/microsoft/deberta-xlarge)1 | -/- | -/- | 91.5/91.2| 97.0 | - | - | 93.1 | 92.1/94.3 | - |92.9/92.7| | [DeBERTa-V2-XLarge](https://huggingface.co/microsoft/deberta-v2-xlarge)1|95.8/90.8| 91.4/88.9|91.7/91.6| **97.5**| 95.8|71.1|**93.9**|92.0/94.2|92.3/89.8|92.9/92.9| |**[DeBERTa-V2-XXLarge](https://huggingface.co/microsoft/deberta-v2-xxlarge)1,2**|**96.1/91.4**|**92.2/89.7**|**91.7/91.9**|97.2|**96.0**|**72.0**| 93.5| **93.1/94.9**|**92.7/90.3** |**93.2/93.1** | -------- #### Notes. - 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on [DeBERTa-Large-MNLI](https://huggingface.co/microsoft/deberta-large-mnli), [DeBERTa-XLarge-MNLI](https://huggingface.co/microsoft/deberta-xlarge-mnli), [DeBERTa-V2-XLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xlarge-mnli), [DeBERTa-V2-XXLarge-MNLI](https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli). The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks. - 2 To try the **XXLarge** model with **[HF transformers](https://huggingface.co/transformers/main_classes/trainer.html)**, we recommand using **deepspeed** as it's faster and saves memory. Run with `Deepspeed`, ```bash pip install datasets pip install deepspeed # Download the deepspeed config file wget https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/ds_config.json -O ds_config.json export TASK_NAME=mnli output_dir="ds_results" num_gpus=8 batch_size=8 python -m torch.distributed.launch --nproc_per_node=${num_gpus} \\ run_glue.py \\ --model_name_or_path microsoft/deberta-v2-xxlarge \\ --task_name $TASK_NAME \\ --do_train \\ --do_eval \\ --max_seq_length 256 \\ --per_device_train_batch_size ${batch_size} \\ --learning_rate 3e-6 \\ --num_train_epochs 3 \\ --output_dir $output_dir \\ --overwrite_output_dir \\ --logging_steps 10 \\ --logging_dir $output_dir \\ --deepspeed ds_config.json ``` You can also run with `--sharded_ddp` ```bash cd transformers/examples/text-classification/ export TASK_NAME=mnli python -m torch.distributed.launch --nproc_per_node=8 run_glue.py --model_name_or_path microsoft/deberta-v2-xxlarge \\ --task_name $TASK_NAME --do_train --do_eval --max_seq_length 256 --per_device_train_batch_size 8 \\ --learning_rate 3e-6 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16 ``` ### Citation If you find DeBERTa useful for your work, please cite the following paper: ``` latex @inproceedings{ he2021deberta, title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION}, author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=XPZIaotutsD} } ```

cd6d277a49f79712e7fee66d3e5cb811

annahaz/distilbert-base-multilingual-cased-finetuned-misogyny

annahaz

distilbert

9

3

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

2,366

false

# distilbert-base-multilingual-cased-finetuned-misogyny This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0045 - Accuracy: 0.9990 - F1: 0.9989 - Precision: 0.9989 - Recall: 0.9989 - Mae: 0.0010 ## 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 | Accuracy | F1 | Precision | Recall | Mae | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:| | 0.2987 | 1.0 | 1759 | 0.3910 | 0.8164 | 0.8186 | 0.7793 | 0.8621 | 0.1836 | | 0.2507 | 2.0 | 3518 | 0.2399 | 0.9029 | 0.9043 | 0.8589 | 0.9547 | 0.0971 | | 0.1793 | 3.0 | 5277 | 0.1412 | 0.9479 | 0.9483 | 0.9068 | 0.9937 | 0.0521 | | 0.1062 | 4.0 | 7036 | 0.0570 | 0.9828 | 0.9823 | 0.9702 | 0.9947 | 0.0172 | | 0.0732 | 5.0 | 8795 | 0.0293 | 0.9924 | 0.9921 | 0.9885 | 0.9958 | 0.0076 | | 0.0461 | 6.0 | 10554 | 0.0157 | 0.9960 | 0.9958 | 0.9937 | 0.9979 | 0.0040 | | 0.037 | 7.0 | 12313 | 0.0126 | 0.9975 | 0.9974 | 0.9948 | 1.0 | 0.0025 | | 0.0311 | 8.0 | 14072 | 0.0092 | 0.9980 | 0.9979 | 0.9958 | 1.0 | 0.0020 | | 0.0141 | 9.0 | 15831 | 0.0065 | 0.9985 | 0.9984 | 0.9979 | 0.9989 | 0.0015 | | 0.0119 | 10.0 | 17590 | 0.0045 | 0.9990 | 0.9989 | 0.9989 | 0.9989 | 0.0010 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.9.0+cu111 - Datasets 2.3.2 - Tokenizers 0.12.1

da7bfe2c2cf5a7e569e0aa69262fcf52

Zia/distilbert-base-uncased-finetuned-emotion

Zia

distilbert

16

29

transformers

0

text-classification

true

false

apache-2.0

null

['emotion']

null

1

0

['generated_from_trainer']

true

1,410

false

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1707 - Accuracy: 0.9365 - F1: 0.9367 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0746 | 1.0 | 250 | 0.1932 | 0.9335 | 0.9330 | | 0.0565 | 2.0 | 500 | 0.1774 | 0.939 | 0.9391 | | 0.0539 | 3.0 | 750 | 0.1707 | 0.9365 | 0.9367 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3

be1a85a813f867633bd447321bf26a5b

NerdyRodent/rodent-diffusion-1-5

NerdyRodent

null

8

0

null

11

text-to-image

false

creativeml-openrail-m

['en']

null

2

0

2

0

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

false

true

7,539

false

# Rodent Diffusion 1.5 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Rodent-Diffusion-1-5** checkpoint was created with a custom Stable Diffusion v1.4 model as the base. From the base model, small merges (0.1-0.3) were included from the models listed below. Some keywords may exist, but for the most part you don't need anything special. Files are located in the "Files and versions" tab. <a href="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/blob/main/rodent-diffusion-1.5.safetensors">Safetensors file</a> Models: - analogDiffusion - Knolling Case - RPGDiffusion - classicnegative - cuteRich - inkpunk - evoartMj4 - dreamshaper - deliberate # Examples <img src="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/resolve/main/00806-Professional%2C_full-colour%2C_HD_digital_portrait_photo_of_a_hipster._Detailed%2C_intricate_hair%2C_high_definition._Focused%2C_crisp%2C_cl_3642035934_Euler%20a.png" width="30%"/> Professional, full-colour, HD digital portrait photo of a hipster. Detailed, intricate hair, high definition. Focused, crisp, clear and sharp. Ultra-realistic cinematic film still. taken with the Canon m50, 50mm focal. pastel shades AND professional photo of a hipster with vivid, vibrant earthy tones. 1960s Technicolor 16mm celluloid film look. Coffee bar in the background. Decaf latte. Negative prompt: blurry, smudge, smear, painting, anime, sketch, doodle, illustration, drawing Steps: 42, Sampler: Euler a, CFG scale: 5.25, Seed: 3642035934, Size: 512x640, Denoising strength: 0.666, Hires upscale: 1.689, Hires upscaler: Latent (bicubic antialiased) <img src="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/resolve/main/Rodent.png" width="30%"/> Professional, full-colour, HD digital portrait photo of a humanoid rat. Detailed, intricate hair, high definition. Focused, crisp, clear and sharp. Ultra-realistic cinematic film still. taken with the Canon m50, 50mm focal. pastel shades AND professional photo of a rodent druid wearing amazing armour. Vibrant earthy tones. 1960s Technicolor 16mm celluloid film look. Gothic castle background. Negative prompt: blurry, smudge, smear, painting, anime, sketch, doodle, illustration, drawing Steps: 42, Sampler: Euler a, CFG scale: 5.25, Seed: 2537406181, Size: 512x640, Denoising strength: 0.666, Hires upscale: 1.689, Hires upscaler: Latent (bicubic antialiased) <img src="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/resolve/main/00827-Amazing_painting_of_a_stunning_African_woman._Incredible_hairstyle%2C_high_definition._Focused%2C_crisp%2C_clear_and_sharp._Ultra-real_3784463460_Euler%20a.png" width="30%"/> Amazing painting of a stunning African woman. Incredible hairstyle, high definition. Focused, crisp, clear and sharp. Ultra-realistic. vibrant colours. AND matte portrait painting, cute African lady from the future. Vibrant brush strokes. oil on canvas, realism, acrylic impressionism neo-science fiction aesthetic with fantasy undertones mixed to create a warm feeling. 80's look and feel Negative prompt: 3d, render, blurry, smudge, smear, photo Steps: 42, Sampler: Euler a, CFG scale: 5.25, Seed: 3784463462, Size: 512x640, Denoising strength: 0.666, Hires upscale: 1.689, Hires upscaler: Latent (bicubic antialiased) <img src="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/resolve/main/00841-Anime_style_painting_of_a_Tokyo_street._Calm_and_peaceful._Relaxing._Incredible_definition_and_detail._Crisp%2C_clear_and_sharp_fo_2306894277_Euler%20a.png" width="30%"/> Anime style painting of a Tokyo street. Calm and peaceful. Relaxing. Incredible definition and detail. Crisp, clear and sharp focus. AND Anime inspired cinematic film still from the future the depicts a serene street during golden hour. Cel shading. Pastel shades and chilled vibes. Negative prompt: 3d, render, blurry, smudge, smear, photo Steps: 42, Sampler: Euler a, CFG scale: 5.25, Seed: 2306894277, Size: 512x640, Denoising strength: 0.666, Hires upscale: 1.689, Hires upscaler: Latent (bicubic antialiased) <img src="https://huggingface.co/NerdyRodent/rodent-diffusion-1-5/resolve/main/00849-Matte_painting_of_a_cat%2C_psychedelic_fractal_fur%2C_illusion%2C_ethereal_AND_oil_painting_of_a_surreal_cat_with_wild%2C_human-like_eye_2534465260_Euler%20a.png" width="30%"/> Matte painting of a cat, psychedelic fractal fur, illusion, ethereal AND oil painting of a surreal cat with wild, human-like eyes and a massive grin Negative prompt: 3d, render, blurry, smudge, smear, photo Steps: 42, Sampler: Euler a, CFG scale: 5.25, Seed: 2534465260, Size: 512x640, Denoising strength: 0.666, Hires upscale: 1.689, Hires upscaler: Latent (bicubic antialiased) Due to the strange licence mix, this model is for personal use only though I am working on an update with less restrictions. ## Original Stable Diffusion Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

ea217d5ab1b342451229670e072c0b02

Gokulapriyan/swin-tiny-patch4-window7-224-finetuned-new_dataset_50e

Gokulapriyan

swin

18

6

transformers

0

image-classification

true

false

apache-2.0

null

['imagefolder']

null

0

['generated_from_trainer']

true

4,415

false

# swin-tiny-patch4-window7-224-finetuned-new_dataset_50e This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6407 - Accuracy: 0.7973 ## 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: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.94 | 4 | 0.7081 | 0.6081 | | No log | 1.94 | 8 | 0.7104 | 0.6351 | | 0.5516 | 2.94 | 12 | 0.6911 | 0.6351 | | 0.5516 | 3.94 | 16 | 0.7156 | 0.7027 | | 0.537 | 4.94 | 20 | 0.7345 | 0.7297 | | 0.537 | 5.94 | 24 | 0.6745 | 0.6892 | | 0.537 | 6.94 | 28 | 0.7146 | 0.7297 | | 0.5333 | 7.94 | 32 | 0.7057 | 0.6892 | | 0.5333 | 8.94 | 36 | 0.6531 | 0.7027 | | 0.4871 | 9.94 | 40 | 0.6405 | 0.7027 | | 0.4871 | 10.94 | 44 | 0.6126 | 0.6892 | | 0.4871 | 11.94 | 48 | 0.6303 | 0.7027 | | 0.4432 | 12.94 | 52 | 0.6264 | 0.7027 | | 0.4432 | 13.94 | 56 | 0.6347 | 0.7432 | | 0.3669 | 14.94 | 60 | 0.6698 | 0.6622 | | 0.3669 | 15.94 | 64 | 0.6346 | 0.7568 | | 0.3669 | 16.94 | 68 | 0.6510 | 0.6892 | | 0.3704 | 17.94 | 72 | 0.6491 | 0.6892 | | 0.3704 | 18.94 | 76 | 0.5947 | 0.7568 | | 0.3624 | 19.94 | 80 | 0.6248 | 0.7027 | | 0.3624 | 20.94 | 84 | 0.6580 | 0.7027 | | 0.3624 | 21.94 | 88 | 0.6345 | 0.7162 | | 0.3164 | 22.94 | 92 | 0.6092 | 0.7568 | | 0.3164 | 23.94 | 96 | 0.6498 | 0.7162 | | 0.2777 | 24.94 | 100 | 0.6915 | 0.7703 | | 0.2777 | 25.94 | 104 | 0.6482 | 0.7838 | | 0.2777 | 26.94 | 108 | 0.6407 | 0.7973 | | 0.2946 | 27.94 | 112 | 0.6135 | 0.7838 | | 0.2946 | 28.94 | 116 | 0.6819 | 0.7568 | | 0.2546 | 29.94 | 120 | 0.6401 | 0.7568 | | 0.2546 | 30.94 | 124 | 0.6370 | 0.7432 | | 0.2546 | 31.94 | 128 | 0.6488 | 0.7703 | | 0.2477 | 32.94 | 132 | 0.6429 | 0.7973 | | 0.2477 | 33.94 | 136 | 0.6540 | 0.7703 | | 0.1968 | 34.94 | 140 | 0.5895 | 0.7973 | | 0.1968 | 35.94 | 144 | 0.6242 | 0.7568 | | 0.1968 | 36.94 | 148 | 0.6575 | 0.7568 | | 0.2235 | 37.94 | 152 | 0.6263 | 0.7703 | | 0.2235 | 38.94 | 156 | 0.6225 | 0.7838 | | 0.2005 | 39.94 | 160 | 0.6731 | 0.7703 | | 0.2005 | 40.94 | 164 | 0.6844 | 0.7703 | | 0.2005 | 41.94 | 168 | 0.6550 | 0.7703 | | 0.2062 | 42.94 | 172 | 0.6700 | 0.7703 | | 0.2062 | 43.94 | 176 | 0.6661 | 0.7703 | | 0.1933 | 44.94 | 180 | 0.6606 | 0.7838 | | 0.1933 | 45.94 | 184 | 0.6757 | 0.7703 | | 0.1933 | 46.94 | 188 | 0.6889 | 0.7568 | | 0.1895 | 47.94 | 192 | 0.6940 | 0.7568 | | 0.1895 | 48.94 | 196 | 0.6919 | 0.7568 | | 0.1666 | 49.94 | 200 | 0.6899 | 0.7432 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2

d6253aa433710a61e0277336d055065f

mehdidn/finetuned_bert_fa_zwnj_base_ner

mehdidn

bert

16

5

transformers

0

token-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,782

false

# finetuned_parsBERT_NER_fa This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on the mixed NER dataset collected from ARMAN, PEYMA, and WikiANN. It achieves the following results on the evaluation set: - Loss: 0.0297 - Precision: 0.9481 - Recall: 0.9582 - F1: 0.9531 - Accuracy: 0.9942 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.12 | 1.0 | 1821 | 0.0543 | 0.8387 | 0.8577 | 0.8481 | 0.9830 | | 0.0381 | 2.0 | 3642 | 0.0360 | 0.8941 | 0.9247 | 0.9091 | 0.9898 | | 0.0168 | 3.0 | 5463 | 0.0282 | 0.9273 | 0.9452 | 0.9362 | 0.9927 | | 0.0078 | 4.0 | 7284 | 0.0284 | 0.9391 | 0.9551 | 0.9470 | 0.9938 | | 0.0033 | 5.0 | 9105 | 0.0297 | 0.9481 | 0.9582 | 0.9531 | 0.9942 | ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

0e9ca388739a7ff355074f3fd61970ef

DrishtiSharma/whisper-large-v2-hungarian

DrishtiSharma

whisper

15

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['hu']

['mozilla-foundation/common_voice_11_0']

null

0

['whisper-event', 'generated_from_trainer']

true

1,308

false

# Whisper Large-V2 Hungarian 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: 0.2075 - Wer: 17.4533 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1751 | 0.67 | 1000 | 0.2075 | 17.4533 | ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu116 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2

48b8098688b42ffd110b013bfedad5f5

clementchadebec/reproduced_hvae

clementchadebec

null

7

0

pythae

0

null

false

apache-2.0

['en']

null

0

['pythae', 'reproducibility']

false

true

603

false

This model was trained with pythae. It can be downloaded or reloaded using the method `load_from_hf_hub` ```python >>> from pythae.models import AutoModel >>> model = AutoModel.load_from_hf_hub(hf_hub_path="clementchadebec/reproduced_hvae") ``` ## Reproducibility This trained model reproduces the results of Table 1 in [1]. | Model | Dataset | Metric | Obtained value | Reference value | |:---:|:---:|:---:|:---:|:---:| | HVAE (n_lf=4) | Binary MNIST | NLL (1000 IS) | 86.21 (0.01) | 86.40 | [1] Samlimans, T. et al, *Markov chain monte carlo and variational inference: Bridging the gap*, ICML 2015

cd6106d08f5d827c0ef928e1f445e763

henryscheible/mrpc_bert-base-uncased_81_v2

henryscheible

null

13

0

null

0

null

true

false

apache-2.0

['en']

['glue']

null

0

['generated_from_trainer']

true

1,057

false

# mrpc_bert-base-uncased_81_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.6390 - Accuracy: 0.8088 - F1: 0.8717 - Combined Score: 0.8403 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1 - Datasets 2.6.1 - Tokenizers 0.13.1

aaa3eb34412bf92164fa936d0706694b

KoichiYasuoka/roberta-base-ainu

KoichiYasuoka

roberta

8

117

transformers

0

fill-mask

true

false

cc-by-sa-4.0

['ain']

null

0

['ainu', 'masked-lm']

false

true

790

false

# roberta-base-ainu ## Model Description This is a RoBERTa model pre-trained on Ainu texts written in カタカナ, Roman, and Кириллица. You can fine-tune `roberta-base-ainu` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-ainu-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-ainu-ud-goeswith), and so on. ## How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-ainu") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-ainu") ``` ## Reference 安岡孝一: [ローマ字・カタカナ・キリル文字併用アイヌ語RoBERTa・DeBERTaモデルの開発](http://id.nii.ac.jp/1001/00224072/), 情報処理学会研究報告, Vol.2023-CH-131『人文科学とコンピュータ』, No.7 (2023年2月18日), pp.1-7.

1c785aca3d1beb03efaedbc32d32ee55

hjjeon/ddpm-butterflies-128

hjjeon

null

18

2

diffusers

0

null

false

apache-2.0

['en']

['huggan/smithsonian_butterflies_subset']

null

0

[]

false

true

1,415

false

# ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python from diffusers import DDPMPipeline model_id = "hjjeon/ddpm-butterflies-128" # load model and scheduler pipeline = DDPMPipeline.from_pretrained(model_id) # run pipeline in inference image = pipeline()["sample"] # save image image[0].save("butterfly.png") ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/hjjeon/ddpm-butterflies-128/tensorboard?#scalars)

1404c7f91e3d106815e8a905948a3485

roschmid/distilbert-base-uncased-finetuned-ner

roschmid

distilbert

13

3

transformers

0

token-classification

true

false

apache-2.0

null

['conll2003']

null

1

0

['generated_from_trainer']

true

1,555

false

# distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0631 - Precision: 0.9207 - Recall: 0.9352 - F1: 0.9279 - Accuracy: 0.9832 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2399 | 1.0 | 878 | 0.0678 | 0.9097 | 0.9211 | 0.9154 | 0.9804 | | 0.0502 | 2.0 | 1756 | 0.0628 | 0.9152 | 0.9320 | 0.9235 | 0.9820 | | 0.0299 | 3.0 | 2634 | 0.0631 | 0.9207 | 0.9352 | 0.9279 | 0.9832 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

ed2386426c2a094ebc9dc3e1023b0a2c

jonatasgrosman/exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s82

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['fr']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

474

false

# exp_w2v2r_fr_xls-r_age_teens-2_sixties-8_s82 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

b8e7ae5fe83b8bd96eef7be2d36d2d6c

ganchengguang/RoBERTa-base-janpanese

ganchengguang

roberta

6

13

transformers

1

fill-mask

true

false

apache-2.0

null

0

[]

false

true

826

false

This is RoBERTa model pretrained on texts in the Japanese language. 3.45GB wikipedia text trained 1.65M step use the sentencepiece tokenizer. If you want to fine-tune model. Please use ```python from transformers import BertTokenizer, RobertaModel BertTokenizer.from_pretrained('') RoBERTModel.from_pretrained('') ``` The accuracy in JGLUE-marc_ja-v1.0 binary sentiment classification 95.4% Contribute by Yokohama Nationaly University Mori Lab @article{liu2019roberta, title={Roberta: A robustly optimized bert pretraining approach}, author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1907.11692}, year={2019} }

65d3429bd1113815da6142bf91d1e116

yazdipour/text-to-sparql-t5-small

yazdipour

t5

11

8

transformers

0

text2text-generation

true

false

apache-2.0

null

[]

null

0

['generated_from_trainer']

false

true

1,780

false

# text-to-sparql-t5-small-2021-10-19_10-17_lastDS This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2335 - Gen Len: 19.0 - P: 0.5580 - R: 0.0884 - F1: 0.3129 - Score: 5.9585 - Bleu-precisions: [90.11303396628615, 80.34125695971072, 73.81487011728768, 69.48796722990271] - Bleu-bp: 0.0763 ## 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 - 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 | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:----------------------------------------------------------------------------:|:-------:| | 0.3166 | 1.0 | 4807 | 0.2335 | 19.0 | 0.5580 | 0.0884 | 0.3129 | 5.9585 | [90.11303396628615, 80.34125695971072, 73.81487011728768, 69.48796722990271] | 0.0763 | ### Framework versions - Transformers 4.10.0 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

773ea491a5983ce532eafb5693b40179

din0s/bart-large-asqa-cb

din0s

bart

11

4

transformers

0

text2text-generation

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,814

false

# bart-large-asqa-cb This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4791 - Rougelsum: 38.2862 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:---------:| | 3.347 | 1.0 | 545 | 2.5353 | 37.3812 | | 2.7829 | 2.0 | 1090 | 2.5087 | 37.6431 | | 2.6973 | 3.0 | 1635 | 2.4906 | 37.9194 | | 2.6125 | 4.0 | 2180 | 2.4812 | 38.1180 | | 2.5697 | 5.0 | 2725 | 2.4762 | 38.1616 | | 2.5086 | 6.0 | 3270 | 2.4773 | 38.1370 | | 2.4678 | 7.0 | 3815 | 2.4831 | 37.9346 | | 2.4404 | 8.0 | 4360 | 2.4896 | 38.1150 | | 2.3866 | 9.0 | 4905 | 2.4775 | 38.2222 | | 2.3791 | 10.0 | 5450 | 2.4791 | 38.2862 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu102 - Datasets 2.4.0 - Tokenizers 0.12.1

7e59005da97bb6b4664913058d31dacb

Akihiro2/bert-finetuned-squad

Akihiro2

bert

12

3

transformers

0

question-answering

true

false

apache-2.0

null

['squad']

null

1

0

['generated_from_trainer']

true

954

false

# bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

5f8db0a78a86230b1bbe56f31143878a

malcolm/TSC_SentimentA_IMDBAmznTSC_2

malcolm

distilbert

13

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,044

false

# TSC_SentimentA_IMDBAmznTSC_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1985 - Accuracy: 0.9365 - F1: 0.9373 ## 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 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

9af4e7c23d0dbd13ec605129c543968d

jonatasgrosman/exp_w2v2t_ja_unispeech-sat_s635

jonatasgrosman

unispeech-sat

10

5

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['ja']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

463

false

# exp_w2v2t_ja_unispeech-sat_s635 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (ja)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.

4d42549d90bade60834ac2dc73e5718c

jonatasgrosman/exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s717

jonatasgrosman

wav2vec2

10

0

transformers

0

automatic-speech-recognition

true

false

apache-2.0

['en']

['mozilla-foundation/common_voice_7_0']

null

0

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

false

true

475

false

# exp_w2v2r_en_xls-r_age_teens-2_sixties-8_s717 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](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.

09d6c067e6abf80325cbdaaffe62530c

MultiBertGunjanPatrick/multiberts-seed-0-80k

MultiBertGunjanPatrick

bert

7

3

transformers

0

null

true

false

apache-2.0

['en']

['bookcorpus', 'wikipedia']

null

0

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

false

true

6,479

false

# MultiBERTs Seed 0 Checkpoint 80k (uncased) Seed 0 intermediate checkpoint 80k 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-0-80k') model = BertModel.from_pretrained("multiberts-seed-0-80k") 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>

b3db93d0a52aae654520401aa475c007

dminiotas05/distilbert-base-uncased-finetuned-ft1500_norm300_aug9

dminiotas05

distilbert

14

1

transformers

0

text-classification

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,542

false

# distilbert-base-uncased-finetuned-ft1500_norm300_aug9 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: 1.0639 - Mse: 4.2557 - Mae: 1.3660 - R2: 0.4773 - Accuracy: 0.3664 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| | 0.7595 | 1.0 | 3242 | 1.1009 | 4.4036 | 1.4148 | 0.4591 | 0.3440 | | 0.6024 | 2.0 | 6484 | 1.0896 | 4.3582 | 1.3732 | 0.4647 | 0.3690 | | 0.3745 | 3.0 | 9726 | 1.0639 | 4.2557 | 1.3660 | 0.4773 | 0.3664 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

d6234bee6136f7dd17a0f835ccd7bc73

sd-concepts-library/test-man

sd-concepts-library

null

8

0

null

0

null

false

mit

null

0

[]

false

true

906

false

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

bbcfee1323c3d7c226bee1fab60532e8

Helsinki-NLP/opus-mt-zle-en

Helsinki-NLP

marian

11

13

transformers

0

translation

true

false

apache-2.0

['be', 'ru', 'uk', 'zle', 'en']

null

1

0

['translation']

false

true

2,798

false

### zle-eng * source group: East Slavic languages * target group: English * OPUS readme: [zle-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md) * model: transformer * source language(s): bel bel_Latn orv_Cyrl rue rus ukr * target language(s): eng * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newstest2012-ruseng.rus.eng | 31.1 | 0.579 | | newstest2013-ruseng.rus.eng | 24.9 | 0.522 | | newstest2014-ruen-ruseng.rus.eng | 27.9 | 0.563 | | newstest2015-enru-ruseng.rus.eng | 26.8 | 0.541 | | newstest2016-enru-ruseng.rus.eng | 25.8 | 0.535 | | newstest2017-enru-ruseng.rus.eng | 29.1 | 0.561 | | newstest2018-enru-ruseng.rus.eng | 25.4 | 0.537 | | newstest2019-ruen-ruseng.rus.eng | 26.8 | 0.545 | | Tatoeba-test.bel-eng.bel.eng | 38.3 | 0.569 | | Tatoeba-test.multi.eng | 50.1 | 0.656 | | Tatoeba-test.orv-eng.orv.eng | 6.9 | 0.217 | | Tatoeba-test.rue-eng.rue.eng | 15.4 | 0.345 | | Tatoeba-test.rus-eng.rus.eng | 52.5 | 0.674 | | Tatoeba-test.ukr-eng.ukr.eng | 52.1 | 0.673 | ### System Info: - hf_name: zle-eng - source_languages: zle - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['be', 'ru', 'uk', 'zle', 'en'] - src_constituents: {'bel', 'orv_Cyrl', 'bel_Latn', 'rus', 'ukr', 'rue'} - tgt_constituents: {'eng'} - src_multilingual: True - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/zle-eng/opus2m-2020-08-01.test.txt - src_alpha3: zle - tgt_alpha3: eng - short_pair: zle-en - chrF2_score: 0.6559999999999999 - bleu: 50.1 - brevity_penalty: 0.97 - ref_len: 69599.0 - src_name: East Slavic languages - tgt_name: English - train_date: 2020-08-01 - src_alpha2: zle - tgt_alpha2: en - prefer_old: False - long_pair: zle-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

c5bc6035f6c6c33b267723e8c0e873ae

Helsinki-NLP/opus-mt-tc-big-ar-en

Helsinki-NLP

marian

13

279

transformers

0

translation

true

false

cc-by-4.0

['ar', 'en']

null

2

1

0

['translation', 'opus-mt-tc']

true

5,261

false

# opus-mt-tc-big-ar-en Neural machine translation model for translating from Arabic (ar) to English (en). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). * Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ``` @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Model info * Release: 2022-03-09 * source language(s): afb ara arz * target language(s): eng * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-03-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.zip) * more information released models: [OPUS-MT ara-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ara-eng/README.md) ## Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "اتبع قلبك فحسب.", "وين راهي دّوش؟" ] model_name = "pytorch-models/opus-mt-tc-big-ar-en" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Just follow your heart. # Wayne Rahi Dosh? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-ar-en") print(pipe("اتبع قلبك فحسب.")) # expected output: Just follow your heart. ``` ## Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-03-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ara-eng/opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | ara-eng | tatoeba-test-v2021-08-07 | 0.63477 | 47.3 | 10305 | 76975 | | ara-eng | flores101-devtest | 0.66987 | 42.6 | 1012 | 24721 | | ara-eng | tico19-test | 0.68521 | 44.4 | 2100 | 56323 | ## Acknowledgements The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland. ## Model conversion info * transformers version: 4.16.2 * OPUS-MT git hash: 3405783 * port time: Wed Apr 13 18:17:57 EEST 2022 * port machine: LM0-400-22516.local

aed0dff6d87de75432fc18ab1c91612b

dvitel/h2

dvitel

gpt2

12

2

transformers

0

text-generation

true

false

apache-2.0

null

['dvitel/hearthstone']

null

0

['distigpt2', 'hearthstone']

true

4,544

false

# h2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [hearthstone](https://huggingface.co/datasets/dvitel/hearthstone). [GitHub repo](https://github.com/dvitel/nlp-sem-parsing/blob/master/h2.py). It achieves the following results on the evaluation set: - Loss: 2.5771 - Exact Match: 0.0 - Bleu: 0.6619 - Codebleu: 0.5374 - Ngram Match Score: 0.4051 - Weighted Ngram Match Score: 0.4298 - Syntax Match Score: 0.5605 - Dataflow Match Score: 0.7541 - Chrf: 73.9625 ## 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: 17 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | Chrf | |:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|:-------:| | 1.2052 | 11.94 | 1600 | 1.2887 | 0.0 | 0.6340 | 0.4427 | 0.3384 | 0.3614 | 0.5263 | 0.5446 | 70.8004 | | 0.3227 | 23.88 | 3200 | 1.4484 | 0.0 | 0.6575 | 0.5050 | 0.3767 | 0.3995 | 0.5955 | 0.6485 | 72.9553 | | 0.205 | 35.82 | 4800 | 1.6392 | 0.0 | 0.6598 | 0.5174 | 0.3788 | 0.4022 | 0.5821 | 0.7063 | 73.2766 | | 0.1392 | 47.76 | 6400 | 1.8219 | 0.0 | 0.6584 | 0.5279 | 0.3922 | 0.4159 | 0.5742 | 0.7294 | 73.5022 | | 0.0979 | 59.7 | 8000 | 1.9416 | 0.0 | 0.6635 | 0.5305 | 0.4012 | 0.4248 | 0.5699 | 0.7261 | 73.8081 | | 0.0694 | 71.64 | 9600 | 2.1793 | 0.0 | 0.6593 | 0.5400 | 0.4027 | 0.4271 | 0.5562 | 0.7739 | 73.6746 | | 0.0512 | 83.58 | 11200 | 2.2547 | 0.0 | 0.6585 | 0.5433 | 0.4040 | 0.4283 | 0.5486 | 0.7921 | 73.7670 | | 0.0399 | 95.52 | 12800 | 2.3037 | 0.0 | 0.6585 | 0.5354 | 0.4040 | 0.4282 | 0.5454 | 0.7640 | 73.7431 | | 0.0316 | 107.46 | 14400 | 2.4113 | 0.0 | 0.6577 | 0.5294 | 0.4006 | 0.4257 | 0.5504 | 0.7409 | 73.7004 | | 0.0254 | 119.4 | 16000 | 2.4407 | 0.0 | 0.6607 | 0.5412 | 0.4041 | 0.4285 | 0.5598 | 0.7723 | 73.8828 | | 0.0208 | 131.34 | 17600 | 2.4993 | 0.0 | 0.6637 | 0.5330 | 0.4042 | 0.4286 | 0.5684 | 0.7310 | 74.1760 | | 0.0176 | 143.28 | 19200 | 2.5138 | 0.0 | 0.6627 | 0.5434 | 0.4050 | 0.4295 | 0.5620 | 0.7772 | 74.0546 | | 0.0158 | 155.22 | 20800 | 2.5589 | 0.0 | 0.6616 | 0.5347 | 0.4044 | 0.4291 | 0.5512 | 0.7541 | 73.9516 | | 0.0147 | 167.16 | 22400 | 2.5554 | 0.0 | 0.6620 | 0.5354 | 0.4049 | 0.4295 | 0.5630 | 0.7442 | 73.9461 | | 0.0134 | 179.1 | 24000 | 2.5696 | 0.0 | 0.6607 | 0.5395 | 0.4046 | 0.4293 | 0.5602 | 0.7640 | 73.8383 | | 0.0135 | 191.04 | 25600 | 2.5771 | 0.0 | 0.6619 | 0.5374 | 0.4051 | 0.4298 | 0.5605 | 0.7541 | 73.9625 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.6.1 - Tokenizers 0.13.1

77096afd9524752dfd31f12dc1254ac8

Shaier/BERT_MC_OpenBookQA_w_wrong_context

Shaier

bert

10

0

transformers

0

multiple-choice

true

false

apache-2.0

null

0

['generated_from_trainer']

true

1,840

false

# BERT_MC_OpenBookQA_w_wrong_context This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7450 - Accuracy: 0.922 ## 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: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3525 | 1.0 | 1859 | 0.2696 | 0.906 | | 0.2084 | 2.0 | 3718 | 0.3284 | 0.9143 | | 0.1263 | 3.0 | 5577 | 0.4205 | 0.9143 | | 0.0734 | 4.0 | 7436 | 0.4688 | 0.9203 | | 0.0437 | 5.0 | 9295 | 0.6266 | 0.9173 | | 0.0357 | 6.0 | 11154 | 0.6934 | 0.9207 | | 0.0264 | 7.0 | 13013 | 0.6947 | 0.92 | | 0.0098 | 8.0 | 14872 | 0.6800 | 0.9197 | | 0.0104 | 9.0 | 16731 | 0.7393 | 0.923 | | 0.0067 | 10.0 | 18590 | 0.7846 | 0.9217 | | 0.0034 | 11.0 | 20449 | 0.7450 | 0.922 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0

5c76b6436bbd9748a128de6c8b69a66e

corbt/roberta-lora-2

corbt

roberta

8

4

transformers

0

text-classification

true

false

mit

null

0

['generated_from_trainer']

true

8,586

false

# roberta-lora-2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5790 - Mse: 0.5790 - Mae: 0.5751 - R2: 0.5572 - Accuracy: 0.5465 ## 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: 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 | Mse | Mae | R2 | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:--------:| | 0.9268 | 0.02 | 2500 | 0.7467 | 0.7467 | 0.6737 | 0.4290 | 0.4621 | | 0.7651 | 0.05 | 5000 | 0.7631 | 0.7631 | 0.6773 | 0.4164 | 0.4582 | | 0.7399 | 0.07 | 7500 | 0.9654 | 0.9654 | 0.7675 | 0.2616 | 0.4104 | | 0.7249 | 0.1 | 10000 | 0.7259 | 0.7259 | 0.6579 | 0.4449 | 0.4763 | | 0.7122 | 0.12 | 12500 | 0.7292 | 0.7292 | 0.6596 | 0.4423 | 0.4753 | | 0.7035 | 0.15 | 15000 | 0.7039 | 0.7039 | 0.6425 | 0.4616 | 0.4889 | | 0.6992 | 0.17 | 17500 | 0.8192 | 0.8192 | 0.7018 | 0.3735 | 0.4485 | | 0.6885 | 0.2 | 20000 | 0.8312 | 0.8312 | 0.7040 | 0.3643 | 0.4480 | | 0.6974 | 0.22 | 22500 | 0.6822 | 0.6822 | 0.6317 | 0.4782 | 0.4987 | | 0.6933 | 0.25 | 25000 | 0.7079 | 0.7079 | 0.6426 | 0.4586 | 0.4936 | | 0.6972 | 0.27 | 27500 | 0.7470 | 0.7470 | 0.6638 | 0.4287 | 0.4768 | | 0.6838 | 0.29 | 30000 | 0.6918 | 0.6918 | 0.6362 | 0.4709 | 0.5009 | | 0.6766 | 0.32 | 32500 | 0.6597 | 0.6597 | 0.6199 | 0.4955 | 0.5035 | | 0.6746 | 0.34 | 35000 | 0.7049 | 0.7049 | 0.6431 | 0.4609 | 0.4897 | | 0.6742 | 0.37 | 37500 | 0.6701 | 0.6701 | 0.6240 | 0.4875 | 0.5096 | | 0.6772 | 0.39 | 40000 | 0.6616 | 0.6616 | 0.6176 | 0.4940 | 0.5120 | | 0.6717 | 0.42 | 42500 | 0.6548 | 0.6548 | 0.6187 | 0.4992 | 0.5072 | | 0.6849 | 0.44 | 45000 | 0.6486 | 0.6486 | 0.6157 | 0.5039 | 0.5087 | | 0.6727 | 0.47 | 47500 | 0.6829 | 0.6829 | 0.6294 | 0.4777 | 0.5030 | | 0.7081 | 0.49 | 50000 | 0.6777 | 0.6777 | 0.6299 | 0.4817 | 0.5037 | | 0.6692 | 0.52 | 52500 | 0.6634 | 0.6634 | 0.6206 | 0.4927 | 0.5078 | | 0.6676 | 0.54 | 55000 | 0.6760 | 0.6760 | 0.6261 | 0.4830 | 0.5068 | | 0.6575 | 0.56 | 57500 | 0.6301 | 0.6301 | 0.6060 | 0.5181 | 0.5172 | | 0.6661 | 0.59 | 60000 | 0.6626 | 0.6626 | 0.6168 | 0.4933 | 0.5153 | | 0.653 | 0.61 | 62500 | 0.6516 | 0.6516 | 0.6176 | 0.5017 | 0.5106 | | 0.6583 | 0.64 | 65000 | 0.7014 | 0.7014 | 0.6400 | 0.4636 | 0.4951 | | 0.6617 | 0.66 | 67500 | 0.6620 | 0.6620 | 0.6207 | 0.4937 | 0.5090 | | 0.6475 | 0.69 | 70000 | 0.6286 | 0.6286 | 0.6037 | 0.5193 | 0.5223 | | 0.6455 | 0.71 | 72500 | 0.7304 | 0.7304 | 0.6545 | 0.4414 | 0.4863 | | 0.6464 | 0.74 | 75000 | 0.6246 | 0.6246 | 0.6006 | 0.5223 | 0.5199 | | 0.646 | 0.76 | 77500 | 0.6414 | 0.6414 | 0.6124 | 0.5095 | 0.5126 | | 0.6502 | 0.79 | 80000 | 0.6131 | 0.6131 | 0.5988 | 0.5311 | 0.5245 | | 0.6443 | 0.81 | 82500 | 0.6376 | 0.6376 | 0.6064 | 0.5123 | 0.5229 | | 0.641 | 0.83 | 85000 | 0.6399 | 0.6399 | 0.6096 | 0.5106 | 0.5163 | | 0.6495 | 0.86 | 87500 | 0.6709 | 0.6709 | 0.6239 | 0.4869 | 0.5093 | | 0.642 | 0.88 | 90000 | 0.6025 | 0.6025 | 0.5952 | 0.5392 | 0.5212 | | 0.636 | 0.91 | 92500 | 0.6870 | 0.6870 | 0.6317 | 0.4746 | 0.5006 | | 0.633 | 0.93 | 95000 | 0.6190 | 0.6190 | 0.5949 | 0.5266 | 0.5270 | | 0.6316 | 0.96 | 97500 | 0.6053 | 0.6053 | 0.5926 | 0.5371 | 0.5280 | | 0.6224 | 0.98 | 100000 | 0.6098 | 0.6098 | 0.5956 | 0.5336 | 0.5217 | | 0.6304 | 1.01 | 102500 | 0.6124 | 0.6124 | 0.5949 | 0.5317 | 0.5280 | | 0.6238 | 1.03 | 105000 | 0.6138 | 0.6138 | 0.5950 | 0.5306 | 0.5313 | | 0.6228 | 1.06 | 107500 | 0.6302 | 0.6302 | 0.6038 | 0.5180 | 0.5189 | | 0.6218 | 1.08 | 110000 | 0.6198 | 0.6198 | 0.5958 | 0.5260 | 0.5274 | | 0.6164 | 1.1 | 112500 | 0.6045 | 0.6045 | 0.5895 | 0.5377 | 0.5327 | | 0.6295 | 1.13 | 115000 | 0.6040 | 0.6040 | 0.5884 | 0.5381 | 0.5352 | | 0.614 | 1.15 | 117500 | 0.5956 | 0.5956 | 0.5863 | 0.5445 | 0.5346 | | 0.6016 | 1.18 | 120000 | 0.6208 | 0.6208 | 0.5994 | 0.5252 | 0.5246 | | 0.6103 | 1.2 | 122500 | 0.6060 | 0.6060 | 0.5888 | 0.5366 | 0.5343 | | 0.614 | 1.23 | 125000 | 0.6198 | 0.6198 | 0.5995 | 0.5259 | 0.5293 | | 0.6113 | 1.25 | 127500 | 0.6010 | 0.6010 | 0.5874 | 0.5403 | 0.5340 | | 0.6131 | 1.28 | 130000 | 0.6118 | 0.6118 | 0.5926 | 0.5321 | 0.5303 | | 0.6069 | 1.3 | 132500 | 0.5914 | 0.5914 | 0.5815 | 0.5477 | 0.5406 | | 0.6016 | 1.33 | 135000 | 0.5908 | 0.5908 | 0.5825 | 0.5482 | 0.5417 | | 0.6053 | 1.35 | 137500 | 0.6166 | 0.6166 | 0.5939 | 0.5285 | 0.5317 | | 0.5927 | 1.37 | 140000 | 0.5910 | 0.5910 | 0.5840 | 0.5480 | 0.5392 | | 0.5942 | 1.4 | 142500 | 0.5965 | 0.5965 | 0.5856 | 0.5438 | 0.5387 | | 0.5966 | 1.42 | 145000 | 0.6121 | 0.6121 | 0.5923 | 0.5319 | 0.5358 | | 0.5941 | 1.45 | 147500 | 0.5889 | 0.5889 | 0.5814 | 0.5496 | 0.5373 | | 0.6007 | 1.47 | 150000 | 0.5833 | 0.5833 | 0.5770 | 0.5539 | 0.5436 | | 0.6024 | 1.5 | 152500 | 0.5862 | 0.5862 | 0.5786 | 0.5517 | 0.5423 | | 0.5896 | 1.52 | 155000 | 0.5913 | 0.5913 | 0.5813 | 0.5478 | 0.5429 | | 0.5906 | 1.55 | 157500 | 0.5944 | 0.5944 | 0.5854 | 0.5454 | 0.5373 | | 0.5847 | 1.57 | 160000 | 0.5989 | 0.5989 | 0.5845 | 0.5419 | 0.5398 | | 0.5837 | 1.6 | 162500 | 0.5914 | 0.5914 | 0.5822 | 0.5477 | 0.5394 | | 0.5928 | 1.62 | 165000 | 0.5888 | 0.5888 | 0.5798 | 0.5497 | 0.5424 | | 0.585 | 1.64 | 167500 | 0.5952 | 0.5952 | 0.5829 | 0.5448 | 0.5391 | | 0.5929 | 1.67 | 170000 | 0.5829 | 0.5829 | 0.5768 | 0.5542 | 0.5440 | | 0.5886 | 1.69 | 172500 | 0.5831 | 0.5831 | 0.5783 | 0.5540 | 0.5428 | | 0.5793 | 1.72 | 175000 | 0.5857 | 0.5857 | 0.5776 | 0.5520 | 0.5453 | | 0.5805 | 1.74 | 177500 | 0.5746 | 0.5746 | 0.5727 | 0.5606 | 0.5489 | | 0.5875 | 1.77 | 180000 | 0.5798 | 0.5798 | 0.5739 | 0.5566 | 0.5487 | | 0.5898 | 1.79 | 182500 | 0.5818 | 0.5818 | 0.5746 | 0.5550 | 0.5475 | | 0.5884 | 1.82 | 185000 | 0.5736 | 0.5736 | 0.5722 | 0.5613 | 0.5496 | | 0.5757 | 1.84 | 187500 | 0.5816 | 0.5816 | 0.5756 | 0.5552 | 0.5464 | | 0.5789 | 1.87 | 190000 | 0.5846 | 0.5846 | 0.5774 | 0.5529 | 0.5448 | | 0.575 | 1.89 | 192500 | 0.5866 | 0.5866 | 0.5779 | 0.5513 | 0.5443 | | 0.5836 | 1.91 | 195000 | 0.5815 | 0.5815 | 0.5764 | 0.5552 | 0.5470 | | 0.573 | 1.94 | 197500 | 0.5805 | 0.5805 | 0.5749 | 0.5561 | 0.5493 | | 0.5728 | 1.96 | 200000 | 0.5808 | 0.5808 | 0.5757 | 0.5558 | 0.5474 | | 0.5711 | 1.99 | 202500 | 0.5790 | 0.5790 | 0.5751 | 0.5572 | 0.5465 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.12.1 - Datasets 2.8.0 - Tokenizers 0.13.2

bc7aa86ab5f9d3466c0b9a88c8230049

nila-yuki/final_lab

nila-yuki

bert

8

6

transformers

0

token-classification

false

true

false

apache-2.0

null

0

['generated_from_keras_callback']

true

1,416

false

# nila-yuki/final_lab This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0240 - Validation Loss: 0.0593 - 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': 1017, '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: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1059 | 0.0572 | 0 | | 0.0391 | 0.0542 | 1 | | 0.0240 | 0.0593 | 2 | ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Datasets 2.1.0 - Tokenizers 0.12.1

88334cc29205e927fe1bf15ee6611c0e