license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['SlovakBERT'] | false | How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='gerulata/slovakbert')
unmasker("Deti sa <mask> na ihrisku.")
[{'sequence': 'Deti sa hrali na ihrisku.',
'score': 0.6355380415916443,
'token': 5949,
'token_str': ' hrali'},
{'sequence': 'Deti sa hrajú na ihrisku.',
'score': 0.14731724560260773,
'token': 9081,
'token_str': ' hrajú'},
{'sequence': 'Deti sa zahrali na ihrisku.',
'score': 0.05016357824206352,
'token': 32553,
'token_str': ' zahrali'},
{'sequence': 'Deti sa stretli na ihrisku.',
'score': 0.041727423667907715,
'token': 5964,
'token_str': ' stretli'},
{'sequence': 'Deti sa učia na ihrisku.',
'score': 0.01886524073779583,
'token': 18099,
'token_str': ' učia'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert')
model = RobertaModel.from_pretrained('gerulata/slovakbert')
text = "Text ktorý sa má embedovať."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('gerulata/slovakbert')
model = TFRobertaModel.from_pretrained('gerulata/slovakbert')
text = "Text ktorý sa má embedovať."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
Or extract information from the model like this:
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='gerulata/slovakbert')
unmasker("Slovenské národne povstanie sa uskutočnilo v roku <mask>.")
[{'sequence': 'Slovenske narodne povstanie sa uskutočnilo v roku 1944.',
'score': 0.7383289933204651,
'token': 16621,
'token_str': ' 1944'},...]
```
| c6e5f75b1c9df83deb3e7a8bd2b46c9c |
mit | ['SlovakBERT'] | false | Training data
The SlovakBERT model was pretrained on these datasets:
- Wikipedia (326MB of text),
- OpenSubtitles (415MB of text),
- Oscar (4.6GB of text),
- Gerulata WebCrawl (12.7GB of text) ,
- Gerulata Monitoring (214 MB of text),
- blbec.online (4.5GB of text)
The text was then processed with the following steps:
- URL and email addresses were replaced with special tokens ("url", "email").
- Elongated interpunction was reduced (e.g. -- to -).
- Markdown syntax was deleted.
- All text content in braces f.g was eliminated to reduce the amount of markup and programming language text.
We segmented the resulting corpus into sentences and removed duplicates to get 181.6M unique sentences. In total, the final corpus has 19.35GB of text.
| 65a33157f97136c4e1470d3416873815 |
mit | ['SlovakBERT'] | false | Pretraining
The model was trained in **fairseq** on 4 x Nvidia A100 GPUs for 300K steps with a batch size of 512 and a sequence length of 512. The optimizer used is Adam with a learning rate of 5e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), a weight decay of 0.01, dropout rate 0.1, learning rate warmup for 10k steps and linear decay of the learning rate after. We used 16-bit float precision.
| e43afed95598561110d4583f4669d733 |
mit | ['SlovakBERT'] | false | About us
<a href="https://www.gerulata.com/">
<img width="300px" src="https://www.gerulata.com/images/gerulata-logo-blue.png">
</a>
Gerulata uses near real-time monitoring, advanced analytics and machine learning to help create a safer, more productive and enjoyable online environment for everyone.
| f3faaf2975c53a8bd8811665ba061cd0 |
mit | ['SlovakBERT'] | false | BibTeX entry and citation info
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2109.15254
```
@misc{pikuliak2021slovakbert,
title={SlovakBERT: Slovak Masked Language Model},
author={Matúš Pikuliak and Štefan Grivalský and Martin Konôpka and Miroslav Blšták and Martin Tamajka and Viktor Bachratý and Marián Šimko and Pavol Balážik and Michal Trnka and Filip Uhlárik},
year={2021},
eprint={2109.15254},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 30947812e9099551d00e51d137384d8c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | gLWoman This is my second Stable Diffusion custom model that bring to you a generic woman generated with non-licenced images. The magic word is: gLWoman If you enjoy my work, please consider supporting me: [](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/tKpiVEE.png width=30% height=30%> <img src=https://imgur.com/GAOJzps.png width=30% height=30%> <img src=https://imgur.com/oxI9ZQv.png width=30% height=30%> | e52fea54e3e4832a560ea857a6c00523 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'game'] | false | BreathOfTheWild Diffusion This model was trained on screenshots from Botw and a few from Hyrule warrior, but because I don't actually play both of them, it was pretty hard to get decent training images. The portraits still contain resemblance of link and zelda, this can sometimes be avoided by prompting "portrait of", and the model isn't exactly easily modifiable through prompting. The landscapes are pretty good though. To reference the art style, use the token: botw style You can also check out the higher quality, but trickier V2 on [Civitai](https://civitai.com/models/4676/breathofthewild-diffusion). | d522fb21f7cbeeb14a9b908adbc2a5d8 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'diffusers', 'game'] | false | Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run BreathOfTheWild_Diffusion: [](https://huggingface.co/spaces/ItsJayQz/BreathOfTheWild_Diffusion?logs=build) Here are some samples. **Portraits**   Prompt used: botw style A stunning intricate full color portrait of anad, epic character composition, wearing a white dress, brown hair, by ilya kuvshinov, alessio albi, nina masic, sharp focus, natural lighting, subsurface scattering, f2, 35mm, film grain Negative: ((disfigured)), ((bad art)), ((deformed)), ((poorly drawn)), ((extra limbs)), ((close up)), ((b&w)), weird colors, blurry Guidance: 10 Steps: 30 using DPM++ 2M Karras I'm not a prompt wizard so you can probably get better results with some tuning. **Landscape**    **Disclaimers** - I'm in no way affliated with Nintendo, or any entities relating to the ownership of the game. - The phrase Breath Of The Wild is simply a reference for accessibility. - This was created entirely for research, and entertainment purpose. - I did not plan, or is planning on turning this model into a commercial product, or use for commercial purposes. - I do not condone the usage of the model for making counterfeit products that might infringe on Nintendo's copyrights/trademarks. **License** - This model is under Creative OpenRAIL-M. - This means the model can be used royalty-free, and flexible with the model usage, such as redistribution of the model, or of any derivatives of the model. - However, there are restrictions on the openess of the license. More info into the restrictions can be found [here](https://huggingface.co/spaces/CompVis/stable-diffusion-license). **Responsibilities** - By using/downloading the model, you are responsible for: - All outputs/usage of the model. - Understanding the Disclaimers. - Upholding the terms of the license. Thanks for checking out the model! | 75dd7b6fdd8e701a7678e14fb8a5605a |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ID dataset. It achieves the following results on the evaluation set: - Loss: 0.3975 - Wer: 0.2633 | 19a5a6b343bce1806aba6dfe932e9bf4 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_training: Native AMP | 95824b7be5e8b2ed8bf94d7058aead15 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.78 | 100 | 4.5645 | 1.0 | | No log | 1.55 | 200 | 2.9016 | 1.0 | | No log | 2.33 | 300 | 2.2666 | 1.0982 | | No log | 3.1 | 400 | 0.6079 | 0.6376 | | 3.2188 | 3.88 | 500 | 0.4985 | 0.5008 | | 3.2188 | 4.65 | 600 | 0.4477 | 0.4469 | | 3.2188 | 5.43 | 700 | 0.3953 | 0.3915 | | 3.2188 | 6.2 | 800 | 0.4319 | 0.3921 | | 3.2188 | 6.98 | 900 | 0.4171 | 0.3698 | | 0.2193 | 7.75 | 1000 | 0.3957 | 0.3600 | | 0.2193 | 8.53 | 1100 | 0.3730 | 0.3493 | | 0.2193 | 9.3 | 1200 | 0.3780 | 0.3348 | | 0.2193 | 10.08 | 1300 | 0.4133 | 0.3568 | | 0.2193 | 10.85 | 1400 | 0.3984 | 0.3193 | | 0.1129 | 11.63 | 1500 | 0.3845 | 0.3174 | | 0.1129 | 12.4 | 1600 | 0.3882 | 0.3162 | | 0.1129 | 13.18 | 1700 | 0.3982 | 0.3008 | | 0.1129 | 13.95 | 1800 | 0.3902 | 0.3198 | | 0.1129 | 14.73 | 1900 | 0.4082 | 0.3237 | | 0.0765 | 15.5 | 2000 | 0.3732 | 0.3126 | | 0.0765 | 16.28 | 2100 | 0.3893 | 0.3001 | | 0.0765 | 17.05 | 2200 | 0.4168 | 0.3083 | | 0.0765 | 17.83 | 2300 | 0.4193 | 0.3044 | | 0.0765 | 18.6 | 2400 | 0.4006 | 0.3013 | | 0.0588 | 19.38 | 2500 | 0.3836 | 0.2892 | | 0.0588 | 20.16 | 2600 | 0.3761 | 0.2903 | | 0.0588 | 20.93 | 2700 | 0.3895 | 0.2930 | | 0.0588 | 21.71 | 2800 | 0.3885 | 0.2791 | | 0.0588 | 22.48 | 2900 | 0.3902 | 0.2891 | | 0.0448 | 23.26 | 3000 | 0.4200 | 0.2849 | | 0.0448 | 24.03 | 3100 | 0.4013 | 0.2799 | | 0.0448 | 24.81 | 3200 | 0.4039 | 0.2731 | | 0.0448 | 25.58 | 3300 | 0.3970 | 0.2647 | | 0.0448 | 26.36 | 3400 | 0.4081 | 0.2690 | | 0.0351 | 27.13 | 3500 | 0.4090 | 0.2674 | | 0.0351 | 27.91 | 3600 | 0.3953 | 0.2663 | | 0.0351 | 28.68 | 3700 | 0.4044 | 0.2650 | | 0.0351 | 29.46 | 3800 | 0.3969 | 0.2646 | | 76d06985e681c5f8947881bbb1d8af7c |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for convnext_small.in12k A ConvNeXt image classification model. Trained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) by Ross Wightman. ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | b4d620375836c82f0d2dcc7cda34703b |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 58.5 - GMACs: 8.7 - Activations (M): 21.6 - Image size: 224 x 224 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/rwightman/pytorch-image-models - **Dataset:** ImageNet-12k | 8a446424aa84b7341eff6ab941ba668d |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_small.in12k', pretrained=True) model = model.eval() | e505560fd9063012e0f117ced292a000 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_small.in12k', pretrained=True, features_only=True, ) model = model.eval() | 97b4caa3319bb24f1e6b7b1b25507e23 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_small.in12k', pretrained=True, num_classes=0, | 03c59939a47b3cc0a7f92493e270b7f7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.7029 - Accuracy: 0.6540 - F1: 0.1254 - Combined Score: 0.3897 | 679bf3e11b4f11e3ac1d797bb27c12d3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:| | 0.8495 | 1.0 | 29671 | 0.7150 | 0.6333 | 0.0086 | 0.3210 | | 0.7654 | 2.0 | 59342 | 0.7273 | 0.6339 | 0.0121 | 0.3230 | | 0.7305 | 3.0 | 89013 | 0.7241 | 0.6400 | 0.0479 | 0.3440 | | 0.7108 | 4.0 | 118684 | 0.7147 | 0.6381 | 0.0380 | 0.3381 | | 0.698 | 5.0 | 148355 | 0.7192 | 0.6414 | 0.0564 | 0.3489 | | 0.6891 | 6.0 | 178026 | 0.7239 | 0.6357 | 0.0232 | 0.3295 | | 0.6823 | 7.0 | 207697 | 0.7141 | 0.6442 | 0.0723 | 0.3583 | | 0.6771 | 8.0 | 237368 | 0.7112 | 0.6491 | 0.1004 | 0.3748 | | 0.6729 | 9.0 | 267039 | 0.7156 | 0.6494 | 0.1022 | 0.3758 | | 0.6694 | 10.0 | 296710 | 0.7185 | 0.6502 | 0.1053 | 0.3777 | | 0.6664 | 11.0 | 326381 | 0.7129 | 0.6508 | 0.1085 | 0.3796 | | 0.6639 | 12.0 | 356052 | 0.7112 | 0.6508 | 0.1080 | 0.3794 | | 0.6617 | 13.0 | 385723 | 0.7105 | 0.6542 | 0.1260 | 0.3901 | | 0.6597 | 14.0 | 415394 | 0.7029 | 0.6540 | 0.1254 | 0.3897 | | 0.658 | 15.0 | 445065 | 0.7094 | 0.6486 | 0.0964 | 0.3725 | | 0.6564 | 16.0 | 474736 | 0.7072 | 0.6510 | 0.1084 | 0.3797 | | 0.655 | 17.0 | 504407 | 0.7049 | 0.6557 | 0.1333 | 0.3945 | | 0.6537 | 18.0 | 534078 | 0.7051 | 0.6542 | 0.1269 | 0.3905 | | 0.6526 | 19.0 | 563749 | 0.7096 | 0.6601 | 0.1573 | 0.4087 | | 8021890f97023d8cdff76caaecc34070 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr6_2e-05_all_26_02_2022-04_31_13 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy: 0.8299 - F1: 0.8892 | cd745c17ac06181d6f6b3bc010c91b1c |
mit | [] | false | DIALogue-level Commonsense Transformer (DIALeCT) The pretrained checkpoint for the paper [Multiview Contextual Commonsense Inference: A New Dataset and Task](https://arxiv.org/abs/2210.02890). The model is trained based on the [T5-large](https://huggingface.co/t5-large) checkpoint.  | a2939f18b6635cb9a3c356dae1aad43f |
mit | [] | false | Datasets The dataset used to pretrain the model can be obtained from the [CICERO repo](https://github.com/declare-lab/CICERO) following instructions. The Contextualized Commonsense Inference in Dialogues v2 (CICEROv2) consists of annotated commonsense inferences including cause and emotional reaction, etc. The dialogues are from multiple datasets. | Dataset | | 03af41b1c955bc7278b0dc79d997d936 |
mit | [] | false | Examples Some examples of generated results from the pretrained model (the zero-shot setting). **Subsequent Event** ``` What is or could be the subsequent event of the target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it ``` Predicted subsequent event: ``` David's girlfriend apologized to david for her mistake. ``` **Cause** ``` What is or could be the cause of target? <sep> target: Thanks. Will I be able to take a retest ? <sep> context: A: Did I do well on my test ?, <utt> B: Do you want to know the honest answer ?, <utt> A: Why wouldn't I want to know ?, <utt> B: You had pretty bad scores ., <utt> A: Exactly what do you mean by bad ?, <utt> B: You failed ., <utt> A: How'd I fail it ?, <utt> B: There are a couple of reasons why you didn't pass ., <utt> A: What did I do wrong ?, <utt> B: To sum it all up , you really just don't know how to drive ., <utt> A: Thanks. Will I be able to take a retest ?, <utt> B: Sure you can , in about two and a half weeks . ``` Predicted cause: ``` The speaker has failed the driving test. ``` **Emotional Reaction** ``` What is the possible emotional reaction of the listener in response to target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it ``` Predicted emotional reaction: ``` The listener is hopeful that david will forgive his girlfriend for her mistake. ``` | a10e4e18f70fc1ab039461cb725b3d28 |
mit | [] | false | Inference: The input text should be formatted as follows: ``` Question <sep> target: target_utt <sep> context: A: utterance 1 <utt> B: utterance 2 <utt> A: utterance 3 <utt> B: utterance 4 ``` Question: The question against which we want to make the inference. A, B are speaker identifiers The ```target_utt``` should be anyone between ```utterance 1, utterance 2, utterance 3, or utterance 4```. Do not use the speaker identifier in the ```target_utt``` Some samples are provided in the Hosted inference API box examples. | 06f650fc0c97b5f9daf8c4fe866fe071 |
mit | [] | false | BibTeX entry and citation info If you use the model, you can cite: ```bibtex @article{Shen2022MultiviewCC, title={Multiview Contextual Commonsense Inference: A New Dataset and Task}, author={Siqi Shen and Deepanway Ghosal and Navonil Majumder and Henry Lim and Rada Mihalcea and Soujanya Poria}, journal={ArXiv}, year={2022}, volume={abs/2210.02890} } ``` | c2b1d5632221b450cff4303c2db1d609 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1600k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 1600k 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 | 747a3e8766be3ea2610f8ab291d0a816 |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_1600k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_1600k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_1600k") 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_2-step_1600k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_1600k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | b18bc9fcc52d280d5794f8a77423885f |
mit | [] | false | Table Transformer (fine-tuned for Table Structure Recognition) Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. | 87395f148d5f94f8b0ecd68d93e978a4 |
mit | [] | false | Model description The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. | d860070b10291d2b88e09715b13e2479 |
mit | [] | false | Usage You can use the raw model for detecting the structure (like rows, columns) in tables. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. | da7751ce2d3928a88223b4600a8f1baa |
mit | ['generated_from_trainer'] | false | mbti-classification-xlnet-base-cased-augment This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2045 - Accuracy: 0.2829 | 5ef165b78e096a7f434f8431a6525e9d |
mit | ['generated_from_trainer'] | false | 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: cosine - num_epochs: 3 | 9c72170a5394ee1980d4da4ad1391128 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 2.1055 | 1.0 | 29900 | 0.2884 | 2.1344 | | 1.8127 | 2.0 | 59800 | 0.2830 | 2.1479 | | 1.6953 | 3.0 | 89700 | 2.2045 | 0.2829 | | e4dddcba1e0ba3f5936c2a2b5d74f0a6 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a Game Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Neku Sakuraba from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script). Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-medium-neku") model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-medium-neku") | 735254a9712871a22463189ac8b662d4 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-base-bat-zle Neural machine translation model for translating from Baltic languages (bat) to East Slavic languages (zle). 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", } ``` | 165c7fb38a9ab9dbd15448a19bbf255b |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-13 * source language(s): lav lit * target language(s): rus * model: transformer-align * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-align_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-zle/opusTCv20210807_transformer-align_2022-03-13.zip) * more information released models: [OPUS-MT bat-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/bat-zle/README.md) | 98bd4d176e36891c08eb92543b6f7673 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>rus<< Āfrika ir cilvēces šūpulis.", ">>ukr<< Tomas yra mūsų kapitonas." ] model_name = "pytorch-models/opus-mt-tc-base-bat-zle" 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) ) | 811ac389c170112e0ddec09f440d541d |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Томас - наш капітан. ``` 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-base-bat-zle") print(pipe(">>rus<< Āfrika ir cilvēces šūpulis.")) | 3da6f410b8deaae4a5f39567c19808d3 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-align_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-zle/opusTCv20210807_transformer-align_2022-03-13.test.txt) * test set scores: [opusTCv20210807_transformer-align_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/bat-zle/opusTCv20210807_transformer-align_2022-03-13.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | | 73800f798eb40b3aa43c7d7e5f8a2aad |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | lav-rus | tatoeba-test-v2021-08-07 | 0.75918 | 60.5 | 274 | 1541 | | lit-rus | tatoeba-test-v2021-08-07 | 0.72796 | 54.9 | 3598 | 21908 | | lav-rus | flores101-devtest | 0.49210 | 21.1 | 1012 | 23295 | | lav-ukr | flores101-devtest | 0.48185 | 19.2 | 1012 | 22810 | | lit-rus | flores101-devtest | 0.49850 | 21.3 | 1012 | 23295 | | lit-ukr | flores101-devtest | 0.49114 | 19.5 | 1012 | 22810 | | 0c2fc127b2eaf4a1b2d14add7dc74a46 |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-5_female-5_s719 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | a5fba4b12b8c6400a2589faa4e098346 |
apache-2.0 | ['summarization', 't5', 'ar', 'abstractive summarization', 'xlsum', 'generated_from_trainer'] | false | t5-arabic-base-finetuned-xlsum-ar This model is a fine-tuned version of [bakrianoo/t5-arabic-base](https://huggingface.co/bakrianoo/t5-arabic-base) on the xlsum dataset. It achieves the following results on the evaluation set: - Loss: 3.0328 - Rouge-1: 23.72 - Rouge-2: 10.95 - Rouge-l: 21.59 - Gen Len: 19.0 - Bertscore: 71.81 | 59d9d3e0604dd7d021061a7930911212 |
apache-2.0 | ['summarization', 't5', 'ar', 'abstractive summarization', 'xlsum', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 250 - num_epochs: 10 - label_smoothing_factor: 0.1 | 43886c234f00478070178ff66b2a859b |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8558 - Bleu: 52.9454 | 4a9d07ea7da6578d24870fcbf4b37c80 |
apache-2.0 | ['generated_from_keras_callback'] | false | reqbert-tapt-epoch30 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: | a4f2bbb195fcb1ce7c431f390501ba6f |
apache-2.0 | ['generated_from_keras_callback'] | false | 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': 21740, '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 | 97d193dc19c7eb5dee16ef579a636851 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-1-0.25 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: 6.8599 - Bleu: 4.0871 - Gen Len: 35.3267 | 8ece531615e3b38f5f369e4aa716a776 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | **100Memories2.1E** Hi guys! Do you remember my SD 1.5 model about photos with a little bit of vintage style? I resurrect the project as a SD 2.1 embedding Some recomendations: the magic word for your prompts is 100Memories. If you enjoy my work, please consider supporting me: [](https://www.buymeacoffee.com/elrivx) Examples: <img src=https://imgur.com/3EjRdsJ.png width=30% height=30%> <img src=https://imgur.com/YPcD8wd.png width=30% height=30%> <img src=https://imgur.com/XzoTc2l.png width=30% height=30%> <img src=https://imgur.com/7DfSVIT.png width=30% height=30%> | d79e3d62fdf12b062a98aba54eb9071a |
apache-2.0 | ['vision', 'gear-segmentation', 'generated_from_trainer'] | false | segformer-b0-finetuned-segments-gear2 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the marcomameli01/gear dataset. It achieves the following results on the evaluation set: - Loss: 0.1268 - Mean Iou: 0.1254 - Mean Accuracy: 0.2509 - Overall Accuracy: 0.2509 - Per Category Iou: [0.0, 0.2508641975308642] - Per Category Accuracy: [nan, 0.2508641975308642] | 91abdaed2cf7970037ebf5d04adc7e71 |
apache-2.0 | ['vision', 'gear-segmentation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------:|:--------------------------:| | 0.4614 | 5.0 | 20 | 0.4427 | 0.0741 | 0.1481 | 0.1481 | [0.0, 0.14814814814814814] | [nan, 0.14814814814814814] | | 0.3327 | 10.0 | 40 | 0.2933 | 0.1726 | 0.3453 | 0.3453 | [0.0, 0.34528395061728395] | [nan, 0.34528395061728395] | | 0.2305 | 15.0 | 60 | 0.2244 | 0.0382 | 0.0763 | 0.0763 | [0.0, 0.07634567901234568] | [nan, 0.07634567901234568] | | 0.2011 | 20.0 | 80 | 0.2130 | 0.0374 | 0.0748 | 0.0748 | [0.0, 0.07476543209876543] | [nan, 0.07476543209876543] | | 0.1846 | 25.0 | 100 | 0.1672 | 0.1037 | 0.2073 | 0.2073 | [0.0, 0.20730864197530866] | [nan, 0.20730864197530866] | | 0.1622 | 30.0 | 120 | 0.1532 | 0.0805 | 0.1611 | 0.1611 | [0.0, 0.1610864197530864] | [nan, 0.1610864197530864] | | 0.139 | 35.0 | 140 | 0.1396 | 0.0971 | 0.1942 | 0.1942 | [0.0, 0.19417283950617284] | [nan, 0.19417283950617284] | | 0.1342 | 40.0 | 160 | 0.1283 | 0.0748 | 0.1496 | 0.1496 | [0.0, 0.14962962962962964] | [nan, 0.14962962962962964] | | 0.128 | 45.0 | 180 | 0.1224 | 0.1128 | 0.2256 | 0.2256 | [0.0, 0.22558024691358025] | [nan, 0.22558024691358025] | | 0.1243 | 50.0 | 200 | 0.1268 | 0.1254 | 0.2509 | 0.2509 | [0.0, 0.2508641975308642] | [nan, 0.2508641975308642] | | 551227ef68c40b4b04c5282359b484f3 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-kitchen_and_dining-7-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3560 - Accuracy: 0.2692 | 9a93dd2a66de1c66dcf9a182cd5826ad |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper_0015 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3281 - Train Accuracy: 0.0322 - Validation Loss: 0.5841 - Validation Accuracy: 0.0311 - Epoch: 14 | 2bdf60677116ece2be9dbd948cf40435 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0856 | 0.0116 | 4.4440 | 0.0123 | 0 | | 4.3149 | 0.0131 | 4.0521 | 0.0142 | 1 | | 3.9260 | 0.0146 | 3.7264 | 0.0153 | 2 | | 3.5418 | 0.0160 | 3.3026 | 0.0174 | 3 | | 2.7510 | 0.0198 | 2.0157 | 0.0241 | 4 | | 1.6782 | 0.0250 | 1.3567 | 0.0273 | 5 | | 1.1705 | 0.0274 | 1.0678 | 0.0286 | 6 | | 0.9126 | 0.0287 | 0.9152 | 0.0294 | 7 | | 0.7514 | 0.0296 | 0.8057 | 0.0299 | 8 | | 0.6371 | 0.0302 | 0.7409 | 0.0302 | 9 | | 0.5498 | 0.0307 | 0.6854 | 0.0306 | 10 | | 0.4804 | 0.0312 | 0.6518 | 0.0307 | 11 | | 0.4214 | 0.0316 | 0.6200 | 0.0310 | 12 | | 0.3713 | 0.0319 | 0.5947 | 0.0311 | 13 | | 0.3281 | 0.0322 | 0.5841 | 0.0311 | 14 | | 5c70b3804bf9e7c50ed079214e48d3f0 |
apache-2.0 | ['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event'] | false | Benchmark WER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 16.97 | 17.95 | |with 4-grams LM| 11.77 | 12.23| | c29f7faa83d8f6c52cad6f4462414174 |
apache-2.0 | ['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event'] | false | Benchmark CER result: | | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |---|---|---| |without LM| 6.82 | 7.05 | |with 4-grams LM| 5.22 | 5.33 | | 24be62b0b5d30728d9f514f9cc9fceae |
apache-2.0 | ['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event'] | false | Evaluation Please use the eval.py file to run the evaluation: ```python pip install mecab-python3 unidic-lite pykakasi python eval.py --model_id vutankiet2901/wav2vec2-xls-r-1b-ja --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs ``` | 8b3123e3f663ae1b79bc1af3fa676e7c |
apache-2.0 | ['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP | 8b60226a8a6d8677ec4cb16dad3bb818 |
apache-2.0 | ['automatic-speech-recognition', 'common-voice', 'hf-asr-leaderboard', 'ja', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 3.484 | 9.49 | 1500 | 1.1849 | 0.7543 | 0.4099 | | 1.3582 | 18.98 | 3000 | 0.4320 | 0.3489 | 0.1591 | | 1.1716 | 28.48 | 4500 | 0.3835 | 0.3175 | 0.1454 | | 1.0951 | 37.97 | 6000 | 0.3732 | 0.3033 | 0.1405 | | 1.04 | 47.47 | 7500 | 0.3485 | 0.2898 | 0.1360 | | 0.9768 | 56.96 | 9000 | 0.3386 | 0.2787 | 0.1309 | | 0.9129 | 66.45 | 10500 | 0.3363 | 0.2711 | 0.1272 | | 0.8614 | 75.94 | 12000 | 0.3386 | 0.2676 | 0.1260 | | 0.8092 | 85.44 | 13500 | 0.3356 | 0.2610 | 0.1240 | | 0.7658 | 94.93 | 15000 | 0.3316 | 0.2564 | 0.1218 | | c254922f8f53aa4dcef9701523e49767 |
cc | ['text classification'] | false | Model information: This model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased) model that has been finetuned using radiology report texts from the MIMIC-III database. The task performed was text classification in order to benchmark this model with a selection of other variants of BERT for the classifcation of MIMIC-III radiology report texts into two classes. Labels of [0,1] were assigned to radiology reports in MIMIC-III that were linked to an ICD9 diagnosis code for lung cancer = 1 and a random sample of reports which were not linked to any type of cancer diagnosis code at all = 0. | 41c9f49a86a389574d02fb1334a1cbb9 |
cc | ['text classification'] | false | Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before use - - [MIMIC-III](https://www.nature.com/articles/sdata201635.pdf) - [bert-base-uncased](https://huggingface.co/bert-base-uncased) | cd79ab8fb24d4ba45600ec33153057e5 |
cc | ['text classification'] | false | How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/bert-base-uncased-ft-m3-lc") model = AutoModel.from_pretrained("sarahmiller137/bert-base-uncased-ft-m3-lc") ``` | 7911c3a95b532e1bd8c22f0ecc31e41d |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is/does. --> sm Latin model for spaCy trained on UD treebanks for tagging, parsing and lemmatization - **Developed by:** Patrick J. Burns - **Model type:** spaCy model - **Language(s) (NLP):** la - **License:** mit - **Resources for more information:** - [GitHub Repo](https://github.com/diyclassics/la_dep_cltk_sm) | d54e074df5dda61bf6240d932a314e58 |
mit | [] | false | Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** ``` @misc{burns_la_dep_cltk_sm_2022, title = {la\_dep\_cltk\_sm}, version = 0.2.0, url = {https://github.com/diyclassics/la_dep_cltk_sm}, abstract = {spaCy-compatible sm model for Latin}, urldate = {2022-12-26}, author = {Burns, Patrick J.}, year = {2022}, } ``` | 7a0f76985843ae1891a8cfd010ddb21b |
mit | [] | false | How to Get Started with the Model - Install with... - `pip install https://huggingface.co/diyclassics/la_dep_cltk_sm/resolve/main/la_dep_cltk_sm-0.2.0/dist/la_dep_cltk_sm-0.2.0.tar.gz` - Tested on python 3.10.8, spacy==3.4.2 | c623a96325b6ac5cf7ec2eed35bcf0d8 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Swedish This model is an adapted version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset in Swedish. It achieves the following results on the evaluation set: - Wer: 19.8166 | ffb56461a2ae2be8267bf4ce6e5331ab |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description & uses This model is the openai whisper small transformer adapted for Swedish audio to text transcription. The model is available through its [HuggingFace web app](https://huggingface.co/spaces/torileatherman/whisper_small_sv) | 25a96709c9ff8e3355c5688d108033b4 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training and evaluation data Data used for training is the initial 10% of train and validation of [Swedish Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/sv/train) 11.0 from Mozilla Foundation. The dataset used for evaluation is the initial 10% of test of Swedish Common Voice. The training data has been augmented with random noise, random pitching and change of the speed of the voice. | 9904e31a42fbebf8acd211133efc5a75 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - weight decay: 0 | 9efda614cbd8a78355e67a1bad193171 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1379 | 0.95 | 1000 | 0.295811 | 21.467| | 0.0245 | 2.86 | 3000 | 0.300059 | 20.160 | | 0.0060 | 3.82 | 4000 | 0.320301 | 19.762 | | f029b60dedf5507485b2173df4b825f5 |
apache-2.0 | ['translation'] | false | jpn-nld * source group: Japanese * target group: Dutch * OPUS readme: [jpn-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-nld/README.md) * model: transformer-align * source language(s): jpn jpn_Hani jpn_Hira jpn_Kana jpn_Latn * target language(s): nld * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-nld/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-nld/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-nld/opus-2020-06-17.eval.txt) | 3c508b988fc076e2f2c30d8294807de5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-nld - source_languages: jpn - target_languages: nld - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-nld/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'nl'] - src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'} - tgt_constituents: {'nld'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-nld/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-nld/opus-2020-06-17.test.txt - src_alpha3: jpn - tgt_alpha3: nld - short_pair: ja-nl - chrF2_score: 0.534 - bleu: 34.7 - brevity_penalty: 0.938 - ref_len: 25849.0 - src_name: Japanese - tgt_name: Dutch - train_date: 2020-06-17 - src_alpha2: ja - tgt_alpha2: nl - prefer_old: False - long_pair: jpn-nld - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 85e9348eaea39f3af968fbc0f516b0df |
apache-2.0 | [] | false | distilbert-base-en-fr-es-de-zh-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | 37b0acaac22161c2343703a372ae777e |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-en-fr-es-de-zh-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-en-fr-es-de-zh-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | aa4f91a93261957282b6d9321a2089d4 |
creativeml-openrail-m | [] | false | A DreamBooth finetune of Stable Diffusion v1.5 model trained on a bunch of stills from [Kurzgesagt](https://www.youtube.com/c/inanutshell) videos. Use the tokens **_kurzgesagt style_** in your prompts for the effect. **Sample 1:**  **Sample 2:**  | 80067bc5557fc5b250e681a65a19bb44 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_age_teens-10_sixties-0_s818 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (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. | a882fef6a4775fe9d02362f72d698d29 |
mit | ['automatic-speech-recognition', 'generated_from_trainer'] | false | Model description We pre-trained a wav2vec 2.0 base model on 842h of unlabelled Luxembourgish speech collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 4h of labelled Luxembourgish Speech from the same domain. | e4e6fc0580264a066c57718789d795ff |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-spam This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0325 - F1: 0.9910 - Accuracy: 0.9910 | 339ebefb493dd3e5d3f11dc90e174eed |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 0.1523 | 1.0 | 79 | 0.0369 | 0.9892 | 0.9892 | | 0.0303 | 2.0 | 158 | 0.0325 | 0.9910 | 0.9910 | | 542f569c788bce8ca87e5d7700dda9f4 |
apache-2.0 | ['generated_from_trainer'] | false | TUF_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1832 - Accuracy: 0.96 | eb87bffa997b2ccec1d7c930eb7d435e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5092 | 0.1 | 50 | 0.4385 | 0.7533 | | 0.2807 | 0.2 | 100 | 0.2225 | 0.9 | | 0.1881 | 0.3 | 150 | 0.1531 | 0.94 | | 0.1895 | 0.4 | 200 | 0.1426 | 0.94 | | 0.1995 | 0.5 | 250 | 0.1428 | 0.94 | | 0.1745 | 0.59 | 300 | 0.1538 | 0.9267 | | 0.1679 | 0.69 | 350 | 0.1249 | 0.9533 | | 0.199 | 0.79 | 400 | 0.1327 | 0.9467 | | 0.1703 | 0.89 | 450 | 0.1488 | 0.92 | | 0.1541 | 0.99 | 500 | 0.1772 | 0.9467 | | 0.1436 | 1.09 | 550 | 0.1070 | 0.9667 | | 0.1463 | 1.19 | 600 | 0.1165 | 0.9467 | | 0.1309 | 1.29 | 650 | 0.1054 | 0.9733 | | 0.097 | 1.39 | 700 | 0.1346 | 0.94 | | 0.1307 | 1.49 | 750 | 0.1477 | 0.9467 | | 0.1506 | 1.58 | 800 | 0.1311 | 0.9533 | | 0.1386 | 1.68 | 850 | 0.1165 | 0.9667 | | 0.1463 | 1.78 | 900 | 0.4207 | 0.9067 | | 0.1202 | 1.88 | 950 | 0.1528 | 0.9667 | | 0.1403 | 1.98 | 1000 | 0.1262 | 0.96 | | 0.073 | 2.08 | 1050 | 0.1459 | 0.96 | | 0.0713 | 2.18 | 1100 | 0.1747 | 0.9533 | | 0.0814 | 2.28 | 1150 | 0.1953 | 0.9667 | | 0.0935 | 2.38 | 1200 | 0.1888 | 0.9533 | | 0.0685 | 2.48 | 1250 | 0.1562 | 0.9467 | | 0.1154 | 2.57 | 1300 | 0.1806 | 0.96 | | 0.1239 | 2.67 | 1350 | 0.1322 | 0.9533 | | 0.1011 | 2.77 | 1400 | 0.2148 | 0.94 | | 0.0718 | 2.87 | 1450 | 0.1686 | 0.96 | | 0.1159 | 2.97 | 1500 | 0.1532 | 0.9533 | | 0.0516 | 3.07 | 1550 | 0.1888 | 0.96 | | 0.063 | 3.17 | 1600 | 0.1851 | 0.9467 | | 0.068 | 3.27 | 1650 | 0.2775 | 0.94 | | 0.0946 | 3.37 | 1700 | 0.1853 | 0.96 | | 0.0606 | 3.47 | 1750 | 0.2148 | 0.9467 | | 0.0663 | 3.56 | 1800 | 0.2091 | 0.9533 | | 0.0474 | 3.66 | 1850 | 0.1702 | 0.9533 | | 0.0585 | 3.76 | 1900 | 0.1660 | 0.96 | | 0.0439 | 3.86 | 1950 | 0.2220 | 0.9533 | | 0.0758 | 3.96 | 2000 | 0.1834 | 0.96 | | 0.0497 | 4.06 | 2050 | 0.1707 | 0.9533 | | 0.0412 | 4.16 | 2100 | 0.1948 | 0.9533 | | 0.0338 | 4.26 | 2150 | 0.2039 | 0.9533 | | 0.0796 | 4.36 | 2200 | 0.1797 | 0.9533 | | 0.0727 | 4.46 | 2250 | 0.1986 | 0.9533 | | 0.032 | 4.55 | 2300 | 0.1947 | 0.9467 | | 0.0436 | 4.65 | 2350 | 0.1908 | 0.9467 | | 0.0205 | 4.75 | 2400 | 0.1806 | 0.96 | | 0.0326 | 4.85 | 2450 | 0.1835 | 0.96 | | 0.0404 | 4.95 | 2500 | 0.1832 | 0.96 | | 83a62ae4ec51704a38b3de2989d0759b |
apache-2.0 | ['automatic-speech-recognition', 'et'] | false | exp_w2v2t_et_wav2vec2_s112 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (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. | 82b5aefb6780b05af2ee98045796a7a4 |
apache-2.0 | [] | false | Training Data Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task. | 5ef21a5ef466dd343b3c7fbdf7e3e861 |
apache-2.0 | [] | false | Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')]) | 51a7ad876f02037ac2687fb0dd00c19d |
apache-2.0 | [] | false | e.g. scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')]) ``` | 675871f385d06873a1d2ad11cd95dafe |
apache-2.0 | [] | false | Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = torch.nn.functional.sigmoid(model(**features).logits) print(scores) ``` | 42bdbc866f77027cbf6d71ff5efc19d1 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_xls-r_s691 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 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 98fc04c888d6f89d9b15a5f39a79acb5 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.001_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.8767 | 954ebc6ba10f3972df83473c90c2b6f2 |
unlicense | [] | false | Use the token SamDoesArt to trigger the effect. It should work anywhere in the prompt. I usually put it right at the beginning of the prompt, which has a mildly different effect than putting it at the end of the prompt. Up to you though to do some test and find where is the prompt is best for your personal tastes. I don't recommend putting the word style directly after the keyword like most custom models do. On this model it will cause unpredictable, wierd results that probably aren't what you truly want. The effect of a comma after SamDoesArt or no comma is difficult to determine. "SamDoesArt, portrait of a pretty girl" "SamDoesArt, a man working in a factory, manly, machines" "SamDoesArt, an african lion, mane, magestic You get the idea. A much more thorough and much more visual guide to the model can be seen here: https://www.reddit.com/r/StableDiffusion/comments/za1d7p/samdoesart_v3_intended_to_improve_both_human/ Still don't like this website and don't understand why it is so user unfriendly... | 7ba50c9ae1e176bd964fb526c58db966 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Medium Galician This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs gl_es dataset. It achieves the following results on the evaluation set: - Loss: 0.4553 - Wer: 14.6741 | 06ed36e01ed8526f405b5f94628aad93 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0002 | 39.01 | 1000 | 0.4553 | 14.6741 | | 0.0001 | 79.0 | 2000 | 0.5023 | 14.9400 | | 0.0 | 119.0 | 3000 | 0.5317 | 15.1609 | | 0.0 | 159.0 | 4000 | 0.5513 | 15.2015 | | 0.0 | 199.0 | 5000 | 0.5593 | 15.2060 | | ad2854ecb173e3140bf1ee548efb3247 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2214 - Accuracy: 0.9294 | 62db770e3f542d943dae9edd5dcc8681 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2435 | 1.0 | 1250 | 0.2186 | 0.917 | | 0.1495 | 2.0 | 2500 | 0.2214 | 0.9294 | | 0.0829 | 3.0 | 3750 | 0.4892 | 0.8918 | | 0.0472 | 4.0 | 5000 | 0.5189 | 0.8976 | | 0.0268 | 5.0 | 6250 | 0.5478 | 0.8996 | | 9ffe5b5df99b4551e19bff2f34717edf |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0964 | ff0f87a11294082cd8fd64b7dffba0f6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.0664 | 1.0 | 5533 | 1.0170 | | 0.7946 | 2.0 | 11066 | 1.0367 | | 0.5758 | 3.0 | 16599 | 1.0964 | | aed549c00480643041ddd3635df16d32 |
apache-2.0 | ['translation'] | false | opus-mt-sv-chk * source languages: sv * target languages: chk * OPUS readme: [sv-chk](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-chk/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-chk/opus-2020-01-16.zip) * test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-chk/opus-2020-01-16.test.txt) * test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-chk/opus-2020-01-16.eval.txt) | eec0b7161a6eb18a879b99f80d0cfc39 |
mit | ['automatic-speech-recognition', 'generated_from_trainer'] | false | Model description We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 11h of labelled Luxembourgish speech from the same domain. | 1fb9730bbe5895fbb800ef917c2a5e70 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-32-5 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: 0.6248 - Accuracy: 0.6826 | f41210d25d8c7581b3a25252db920c11 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7136 | 1.0 | 13 | 0.6850 | 0.5385 | | 0.6496 | 2.0 | 26 | 0.6670 | 0.6154 | | 0.5895 | 3.0 | 39 | 0.6464 | 0.7692 | | 0.4271 | 4.0 | 52 | 0.6478 | 0.7692 | | 0.2182 | 5.0 | 65 | 0.6809 | 0.6923 | | 0.103 | 6.0 | 78 | 0.9119 | 0.6923 | | 0.0326 | 7.0 | 91 | 1.0718 | 0.6923 | | 0.0154 | 8.0 | 104 | 1.0721 | 0.7692 | | 0.0087 | 9.0 | 117 | 1.1416 | 0.7692 | | 0.0067 | 10.0 | 130 | 1.2088 | 0.7692 | | 0.005 | 11.0 | 143 | 1.2656 | 0.7692 | | 0.0037 | 12.0 | 156 | 1.3104 | 0.7692 | | 0.0032 | 13.0 | 169 | 1.3428 | 0.6923 | | 937ec36df0f96ee6b969bbeebcd418b3 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8456 - Matthews Correlation: 0.5500 | fb3bde144e7094baeaf09636a241a53e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5197 | 1.0 | 535 | 0.5477 | 0.4130 | | 0.3456 | 2.0 | 1070 | 0.5035 | 0.5239 | | 0.2342 | 3.0 | 1605 | 0.6100 | 0.5285 | | 0.1698 | 4.0 | 2140 | 0.7556 | 0.5456 | | 0.1295 | 5.0 | 2675 | 0.8456 | 0.5500 | | 58f4d3005450d944bc5726895a5c9027 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Medium Czech 2 CV11 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set: - Loss: 0.2417 - Wer: 11.4086 | e6dc0d66771a46fb1686ab15a1a714b1 |
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