license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | ❎-negative-prompt-template) _Steps: 90, Sampler: DPM++ 2M Karras, CFG scale: 8.5, Seed: 2821955656, Size: 512x512, Model hash: b7ba5b22_ --- **The drag of the kingdom** ```Wide shot of a grand kingdom, lifelike, super highly detailed, professional digital painting, artstation, concept art, Unreal Engine 5, HD quali... | 5faa9fbe7f05f08208511549ea002791 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | ❎-negative-prompt-template) _Steps: 90, Sampler: DDIM, CFG scale: 13.5, Seed: 2625868484, Size: 512x512, Model hash: b7ba5b22_ --- **The steamy momoa** ```Perfectly-centered portrait of a effeff9 MAN jason momoa with shining scales descending from heaven, concept art, ART STATION, BEAUTIFUL PERFECT detailed MANGA E... | 3377f45c4a62e0167762edc9bab4512c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | ❎ Negative Prompt Template This model offers a unique style where characters typically have larger, exaggerated sleeves and hands. To supress this style, add more variants to adjust the hand style. All images were rendered with the negative prompt below: ```Negative prompt: ((((ugly)))), (((duplicate))), ((morbid)... | a8ffc1f4b218f58e53081836fee57378 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). Export the model: - [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx) - [MPS](https:/... | c51115d32be6a6d7384b2a72608d3fd0 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'final fantasy'] | false | 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: - You can't use the model to deliberately produce nor share illegal or harmful outputs or content - The authors claims no rights on the outp... | 55db7a601b5495bc761db031d4d20d17 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_qnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 1.1777 - Accuracy: 0.5881 | b71e7847726b66f80d64b97058fe7915 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.6984 | 1.0 | 33208 | 1.1777 | 0.5881 | | 0.5294 | 2.0 | 66416 | 1.2095 | 0.6011 | | 0.4577 | 3.0 | 99624 | 1.2274 ... | e1e46c85da09f14abced8941a686e47f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__hate_speech_offensive__train-16-3 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: 1.0675 - Accuracy: 0.44 | 6baef212ef86ba7536c262ca8c4a015b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0951 | 1.0 | 10 | 1.1346 | 0.1 | | 1.0424 | 2.0 | 20 | 1.1120 | 0.2 | | 0.957 | 3.0 | 30 | 1.1002 | 0.... | e5fc2400d0f1843d93afe52bfc10e725 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Steampunk-Diffusion Dreambooth model, based on Stablediffusion 2.1 (728px version) Trained 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... | dd6a157814eba18b6cdea2e1397cb1d9 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-finetuned-chest-xray-pneumonia This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) dataset. It achieves the following results on the evaluati... | 7ef72351b69c9f5f1ddb67018d294c05 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 60a9857f14af36f4fbf8f980d6e811d8 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 326 | 0.2739 | 0.9167 | | 0.2238 | 2.0 | 652 | 0.2892 | 0.9071 | | 0.2238 | 3.0 | 978 | 0.2077 | 0.... | d3f746cd52baac205d5ace0b8637775b |
apache-2.0 | [] | false | doc2query/all-t5-base-v1
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
It can be used for:
- **Document expansion**: You generate for your paragraphs 20-40 queries and... | c768d04c3933ca31cbacd31eaffb9ab9 |
apache-2.0 | [] | false | Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'doc2query/all-t5-base-v1'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
text = "Python is an interpreted, high-level and general-purpose programm... | c6d517744437dce7df1a5cfd26d85268 |
apache-2.0 | [] | false | Training
This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 570k training steps. For the training script, see the `train_script.py` in this repository.
The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces.
This model was train... | 31bcf796b7164c5fb12553d5b8431581 |
apache-2.0 | [] | false | Prefix
This model was trained **without a prefix**. In contrast to [doc2query/all-with_prefix-t5-base-v1](https://huggingface.co/doc2query/all-with_prefix-t5-base-v1) you cannot specify what type of transformation (answer2question, review2title) etc. you will have. This can lead to a mixture of output values.
| c92dd21e5b9a3ea208921a95cbaff52c |
apache-2.0 | ['translation'] | false | opus-mt-tum-fr * source languages: tum * target languages: fr * OPUS readme: [tum-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tum-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 131d09c42f8bc566f56d788dc9443f2f |
mit | ['generated_from_trainer'] | false | Goodreads_Books_Reviews_Roberta_52 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: 0.8592 - F1: 0.5986 - Accuracy: 0.6349 | 5bec0fb17ce5f1a6f29cf3badeafc81d |
mit | ['generated_from_trainer'] | false | 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 2 | d59ca6c0c9f184294a9d54f31a7b84f8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| | 0.8824 | 1.0 | 25313 | 0.8754 | 0.5792 | 0.6254 | | 0.8127 | 2.0 | 50626 | 0.8592 | 0.5986 | 0.6349 | | 98e9d6a8530d45c1cc574a5b1d47f4ce |
mit | [] | false | giygas on Stable Diffusion This is the `<giygas>` 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 y... | 7bcc4d381101270c42a0b6cf620d7926 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2500 | 43b8b5c75f8891f354c2c3cffed4c810 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0975 | 1.0 | 291 | 1.7060 | | 1.648 | 2.0 | 582 | 1.4280 | | 1.4837 | 3.0 | 873 | 1.3980 | | 1.3978 | 4.0 | 1164 | 1.4040 ... | df650f84f17b60d18285db6292365501 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-mlm-finetuned-emotion This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5597 | 429e8f79aef820e35a93a1a90fa57f06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 4.1612 | 22.73 | 500 | 3.6903 | | 3.9137 | 45.45 | 1000 | 3.6206 | | 3.819 | 68.18 | 1500 | 3.5811 | | 3.7498 | 90.91 | 2000 | 3.5975 ... | 3235b49fea0ad437071bbc1053db3893 |
mit | ['torch'] | false | How to use Here is how to use this model in PyTorch: ```python >>> from transformers import PegasusForConditionalGeneration, AlbertTokenizer >>> >>> model_id = "rmihaylov/pegasus-base-qag-bg" >>> model = PegasusForConditionalGeneration.from_pretrained(model_id) >>> tokenizer = AlbertTokenizer.from_pretrained(model_i... | 58df64c492e3b0da2a950ad920d8c303 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3701 - Wer: 0.2946 | cc8e953b4e91bfefaf422f2a849ab902 |
apache-2.0 | ['generated_from_trainer'] | false | 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_sch... | 64978f46098619812e8ae60004ad4bfb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.8287 | 3.67 | 400 | 0.6628 | 0.6928 | | 0.3926 | 7.34 | 800 | 0.4257 | 0.4716 | | 0.1847 | 11.01 | 1200 | 0.4034 | 0.3931 | |... | 19906b53426a91dbb62ff80795703b5d |
mit | ['aspect-based-sentiment-analysis', 'lcf-bert'] | false | Note
This model is training with 180k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
... | 23e51e537e5090394f5f84784df714c2 |
mit | ['aspect-based-sentiment-analysis', 'lcf-bert'] | false | DeBERTa for aspect-based sentiment analysis
The `deberta-v3-large-absa` model for aspect-based sentiment analysis, trained with English datasets from [ABSADatasets](https://github.com/yangheng95/ABSADatasets).
| 84df180d396a4c94f5cb7250160c47e6 |
mit | ['aspect-based-sentiment-analysis', 'lcf-bert'] | false | Training Model
This model is trained based on the FAST-LSA-T model with `microsoft/deberta-v3-large`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA).
To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA).
| 5ad447d4e645afdd470b6239daabbd25 |
mit | ['aspect-based-sentiment-analysis', 'lcf-bert'] | false | Usage
```python3
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-large-absa")
model = AutoModel.from_pretrained("yangheng/deberta-v3-large-absa")
inputs = tokenizer("good product especially video and audio quality fantastic.", return_tensors="p... | f69de2875374e1b0e5625cbe51908fb3 |
mit | ['aspect-based-sentiment-analysis', 'lcf-bert'] | false | Datasets
This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
```
loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg
loading: integrated_datasets/apc_datasets/SemEval/laptop14/0.cross_boost.fast_lcf_bert_Laptop14_deberta-... | 6fa58a164a4bdbf95379cf020d156667 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-rte-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.4372 | 18acc4f46c7a40b1d6a325e13b4a4d7d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.249 | 1.6 | 500 | 5.5560 | | 5.5772 | 3.21 | 1000 | 5.2693 | | 5.3637 | 4.81 | 1500 | 5.0609 | | 5.1969 | 6.41 | 2000 | 4.9263 ... | 9f748e27ad20281741280ee22405f845 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'ilpost', 'summarization'] | false | IT5 Large for News Summarization ✂️🗞️ 🇮🇹 This repository contains the checkpoint for the [IT5 Large](https://huggingface.co/gsarti/it5-large) model fine-tuned on news summarization on the [Fanpage](https://huggingface.co/datasets/ARTeLab/fanpage) and [Il Post](https://huggingface.co/datasets/ARTeLab/ilpost) corpor... | 52111c10472a5351db525f6c7b676ee9 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'ilpost', 'summarization'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines newsum = pipeline("summarization", model='it5/it5-large-news-summarization') newsum("Dal 31 maggio è infine partita la piattaforma ITsAR... | a81c39c9ad2beb99e619e2782d1c99e4 |
apache-2.0 | ['generated_from_trainer'] | 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: 0.4175 - Accuracy: 0.9368 | f8f77f6176005f8de3cb25056197cf30 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 142a619e45b206ae42bd2bd183d2749b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 159 | 3.3516 | 0.6652 | | 3.4274 | 2.0 | 318 | 2.2866 | 0.7848 | | 3.4274 | 3.0 | 477 | 1.5064 | 0.... | ab81790fbd36a2be9db907decd80b0b3 |
gpl-3.0 | ['spacy', 'token-classification'] | false | Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). | Feature | Description | | --- | --- | | **Name** | `es_spacy_ner_cds_trf` ... | eae341b244ab18a0ec8456b464aa93ea |
gpl-3.0 | ['spacy', 'token-classification'] | false | Usage You can use this model with the spaCy *pipeline* for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("es_spacy_ner_cds_trf") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Indust... | b5ce47dfa6057a659deaa9b3b787032f |
other | ['generated_from_trainer'] | false | segformer-b0-finetuned-segments-rowbody-4cats This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1486 - Mean Iou: 0.6527 - Mean Accuracy: 0.9381 - Overall Accuracy: 0.9558 - Accuracy Sleeve... | 4dc83b2b8eff44f0ebf9caa5a92ce6a2 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | d4b41785b6b21388daaee01fb83824bb |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Sleeve-right | Accuracy Sleeve-left | Accuracy Neck | Accuracy Body | Iou Sleeve-right | Iou Sleeve-left | Iou Neck | Iou Body | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----... | 63fc466a708c182ffbf6c5ce5ab7a98f |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-mnli-rte-wnli-10 This model is a fine-tuned version of [yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5](https://huggingface.co/yy642/bert-base-uncased-finetuned-mnli-rte-wnli-5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5876 - Accuracy: 0.92... | 3156617e7ac75edca4ab3a342f704cc7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0641 | 1.0 | 16558 | 0.4528 | 0.9138 | | 0.0479 | 2.0 | 33116 | 0.5116 | 0.9153 | | 0.0363 | 3.0 | 49674 | 0.5660 ... | 90df44edacf9bee67bae611b69664fd6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-klay-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0060 - Wer: 0.1791 | e6ab597683b4d649a3b4be2b62744352 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 500 - num_epochs: 300 - mixed_precision_tr... | 086b52736aebcecff2ed171a1df739ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 15.0 | 300 | 2.4020 | 0.9889 | | 2.4596 | 30.0 | 600 | 1.3773 | 0.9833 | | 2.4596 | 45.0 | 900 | 0.5241 | 0.7253 | |... | 4a372587c620f4b09d8ff3003057fd86 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_qqp_96 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: 0.7423 - Accuracy: 0.6329 - F1: 0.0062 - Combined Score: 0.3195 | 180d16b30efdd6de1453098d73f8938b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.8963 | 1.0 | 1422 | 0.7832 | 0.6318 | 0.0 | 0.3159 | | 0.7734 | 2.0 | 2844 | ... | b1827506682bf6f836519c9f1f30564c |
apache-2.0 | [] | false | Funnel Transformer large model (B8-8-8 with decoder) Pretrained model on English language using a similar objective as [ELECTRA](https://huggingface.co/transformers/model_doc/electra.html). It was introduced in [this paper](https://arxiv.org/pdf/2006.03236.pdf) and first released in [this repository](https://github.c... | 8d6a071eaf3d520a526517e8e0f7dee0 |
apache-2.0 | [] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import FunnelTokenizer, FunnelModel tokenizer = FunnelTokenizer.from_pretrained("funnel-transformer/large") model = FunneModel.from_pretrained("funnel-transformer/large") text = "Replace me by any te... | d6c44f386cb52e148788ee171378fdf7 |
apache-2.0 | ['generated_from_trainer'] | false | Article_250v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article250v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2337 - Precision: 0.6301 - Recall: 0.6385 - F1: 0.6342 - Accuracy: 0.... | 93db617ebc52ccd5b189bb86c2460ce6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 96 | 0.2343 | 0.5944 | 0.6046 | 0.5994 | 0.9191 | | No log | 2.0 |... | 438b14a281a0e759d865cf513912d202 |
apache-2.0 | ['translation'] | false | opus-mt-uk-es * source languages: uk * target languages: es * OPUS readme: [uk-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/uk-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 7591174f6ba49ff7af23e566bff6eafc |
creativeml-openrail-m | [] | false | Xynthii Diffusion v1 * **Dataset**: 159 images of Xynthii (credits to Hanna) * **Prior-preservation** loss[1] * **Train text encoder** * **1000 steps** * **Model**: "runwayml/stable-diffusion-v1-5" Environmental impact: * **Hardware Type**: 3090 24GB * **Hours used**: 1 * **Cloud Provider**: runpod community cloud ... | 9e82a1b2cb1f566e6fe509f2b7ec8c9d |
creativeml-openrail-m | [] | false | Xynthii Diffusion epoch48 * **Dataset**: 159 images of Xynthii (credits to Hanna) * **Prior-preservation** loss[1] * **Train text encoder** * **48 epochs (7632 steps)** * **Model**: "runwayml/stable-diffusion-v1-5" Environmental impact: * **Hardware Type**: 3090 24GB * **Hours used**: 3 * **Cloud Provider**: runpod... | f37e6d7fa89309d6d7a10de17cf0f2ea |
creativeml-openrail-m | [] | false | Example generations epoch 48 `a classical preraphaelite painting of taylor swift as a xynthii by john william waterhouse and William-Adolphe Bouguereau`  `xynthii named Hanna`  and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 | 81403d0c452dbbbc0360c16d9cfd4772 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7294 | 1.0 | 39 | 3.5148 | | 2.6148 | 2.0 | 78 | 3.5284 | | 2.157 | 3.0 | 117 | 3.6368 | | 2.1294 | 4.0 | 156 | 3.6644 ... | ce10d452621117956383fc9fa54795f4 |
apache-2.0 | ['generated_from_trainer'] | false | bart-finetuned-cnn-3 This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-3](https://huggingface.co/sshleifer/distilbart-xsum-12-3) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 2.0751 - Rouge1: 40.201 - Rouge2: 18.8482 - Rougel: 29.4439 - Rougelsum: 37.... | b44223144751e66907941c66bf46946c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - mixed_precision_training: Native AMP | ffa78b0cec5537b3adf3836c5af264a6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.276 | 1.0 | 8883 | 2.1762 | 39.6581 | 18.3333 | 28.7765 | 36.7688 |... | 04c15608ea653ab55ed7c0f141a26375 |
apache-2.0 | ['distilbart', 'summarization'] | false | MLQ-distilbart-bbc This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the BBC News Summary dataset (https://www.kaggle.com/pariza/bbc-news-summary). The model has been generated as part of the in-lab practice of **Deep NLP course** currently... | b46efe05144ed910b884c01597bcf9b9 |
mit | ['text-to-image'] | false | Mann-E 4 Revision 0.1 __Mann-E__ is a _text to image_ model which has been developed by [Muhammadreza Haghiri](https://haghiri75.com/en) in order to be part of the [Cognitive Web](https://opencognitives.com) movement and projects. This is revision 0.1 of the 4th version of the model. | b1536880c27ac6e8908bc886d0c9cf1a |
mit | ['text-to-image'] | false | What does _Mann-E_ mean? It's a play with the name [Mani](https://en.wikipedia.org/wiki/Mani_(prophet)), who was a Persian religious leader at the early Sassanian era and also a painter and he's famous for both his religious and artistic works. His artistic side was more considered for naming the model of course. | bd39f0c9973bd5c22221f0c69221643f |
mit | ['text-to-image'] | false | Code The following code is written for _CUDA_ supported devices. If you use UI's or inference tools on other devices, you may need to tweak them in order to get them to the work. Otherwise, it will be fine. First, you need to install required libraries: ``` pip3 install diffusers transformers scipy ftfy accelerate ... | b546d28309b69ef314bf30bab998222b |
mit | ['generated_from_trainer'] | false | berturk-uncased-keyword-extractor This model is a fine-tuned version of [dbmdz/bert-base-turkish-uncased](https://huggingface.co/dbmdz/bert-base-turkish-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3931 - Precision: 0.6631 - Recall: 0.6728 - Accuracy: 0.9188 - F1:... | 49564aeefaa5d58c93a3b888747a89a9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:| | 0.1779 | 1.0 | 1875 | 0.1862 | 0.6199 | 0.6356 | 0.9192 | 0.6276 | | 0.1327 | 2.0 ... | 788e223e21b012dbf0e322c7ea7c468d |
apache-2.0 | ['text-classification', 'pytorch'] | false | ```python from transformers import pipeline model_id = "suvrobaner/distilbert-base-uncased-finetuned-emotion-en-tweets" classifier = pipeline("text-classification", model = model_id) custom_tweet = "I saw a movie today and it was really good." preds = classifier(custom_tweet, return_all_scores=True) labels = ['sad... | 74278001abd6a796367aa8ea2f4f0e44 |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov7', 'pypi'] | false | Model Description [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) [YOLOv7-Pip: Packaged version of the Yolov7 repository](https://github.com/kadirnar/yolov7-pip) [Paper Repo: Implementation of paper - YOLOv7](https://github.com/WongKinYiu... | fddcf3d1d3b21e70cc832fc514d3cfee |
gpl-3.0 | ['object-detection', 'computer-vision', 'yolov7', 'pypi'] | false | BibTeX Entry and Citation Info ``` @article{wang2022yolov7, title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, journal={arXiv preprint arXiv:2207.02696}, year={2022} } ``` | c2acb9784d8ad534e85a0f0353c65d81 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Wav2Vec2 XLS-R Adult/Child Speech Classifier Wav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a private ... | 027f2d4dd90225b404b58855b53b2399 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | params | Arch. | Training/Validation data (text) | | -------------------------------- | ------- | ----- | ----------------------------------------- | | `wav2vec2-xls-r-adult-child-cls` | 300M | XLS-R | Adult/Child Speech Classification Dataset | | 201583a00e91309345566bdb2605cc64 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Evaluation Results The model achieves the following results on evaluation: | Dataset | Loss | Accuracy | F1 | | --------------------------------- | ------ | -------- | ------ | | Adult/Child Speech Classification | 0.1851 | 94.69% | 0.9508 | | 13c85c19b15d3c198a9f16ed3c176d01 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - `learning_rate`: 3e-05 - `train_batch_size`: 8 - `eval_batch_size`: 8 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 32 - `optimizer`: Adam with `betas=(0.9,0.999)` and `epsilon=1e-08` - `lr_scheduler_typ... | 4f46987e24224b19352111cff93c0f68 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | :-----------: | :---: | :--: | :-------------: | :------: | :----: | | 0.2906 | 1.0 | 383 | 0.1856 | 0.9372 | 0.9421 | | 0.1749 | 2.0 | 766 | 0.1925 | 0.9418 | 0.9465 | | 0.1681 |... | d306020053ed7f6187a3eed04fa1f9f2 |
mit | ['generated_from_trainer'] | false | wikitext_roberta-base This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the wikitext wikitext-2-raw-v1 dataset. It achieves the following results on the evaluation set: - Loss: 1.2143 - Accuracy: 0.7371 | 75afc17dc7e5cb0c121af68ee34ebda9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | bb11e51f1b0d27f9c8adedcd10fb4fcc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4175 | 0.99 | 37 | 1.3355 | 0.7194 | | 1.438 | 1.99 | 74 | 1.2953 | 0.7249 | | 1.4363 | 2.99 | 111 | 1.2759 | 0.... | d3a787930aa2bc0a14a2b66e54ea21d4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad 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: 1.8545 | 1f9136fdd5cf752288ab8fafe25c53bd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0122 | 1.0 | 2312 | 1.8973 | | 1.7666 | 2.0 | 4624 | 1.8320 | | 1.5729 | 3.0 | 6936 | 1.8545 | | 0cbdc5de0780aa2077d63dfefaa21b6c |
apache-2.0 | ['text2text-generation'] | false | TL;DR Switch Transformers is a Mixture of Experts (MoE) model trained on Masked Language Modeling (MLM) task. The model architecture is similar to the classic T5, but with the Feed Forward layers replaced by the Sparse MLP layers containing "experts" MLP. According to the [original paper](https://arxiv.org/pdf/2101.0... | 375d9410f6af7c712842f648b71de8c4 |
apache-2.0 | ['text2text-generation'] | false | Model Description - **Model type:** Language model - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Related Models:** [All Switch Transformers Checkpoints](https://huggingface.co/models?search=switch) - **Original Checkpoints:** [All Original Switch Transformers Checkpoints](https://github.com/google-r... | 4ccbcf7399e36d660fe0540968ac06b6 |
apache-2.0 | ['text2text-generation'] | false | mixture-of-experts-moe-checkpoints) - **Resources for more information:** - [Research paper](https://arxiv.org/pdf/2101.03961.pdf) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face Switch Transformers Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/switch_transforme... | 7486cbee26a220d3a5c7c89ef79424e8 |
apache-2.0 | ['text2text-generation'] | false | Usage Note that these checkpoints has been trained on Masked-Language Modeling (MLM) task. Therefore the checkpoints are not "ready-to-use" for downstream tasks. You may want to check `FLAN-T5` for running fine-tuned weights or fine-tune your own MoE following [this notebook](https://colab.research.google.com/drive/1... | 4aea0bcfcc3f665a58bdda6f905d296b |
apache-2.0 | ['text2text-generation'] | false | Running the model on a CPU <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-128") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switc... | 10b2c79ae1e249ea80f3a7992c426ff6 |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-128") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-128", device_map="auto") input_text = "A <extra_id_0> wa... | d99a5b55e2fe6d50a0ac621d190bceaf |
apache-2.0 | ['text2text-generation'] | false | pip install accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-128") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-128", device_map="auto", torch_dtype=torch.float16) inpu... | 16353879675d1a1a12adfbfe659d90e7 |
apache-2.0 | ['text2text-generation'] | false | pip install bitsandbytes accelerate from transformers import AutoTokenizer, SwitchTransformersForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("google/switch-base-128") model = SwitchTransformersForConditionalGeneration.from_pretrained("google/switch-base-128", device_map="auto") input_text = "A <e... | c1cafcf04d9081d8fefdb34a9fcf7f15 |
apache-2.0 | ['text2text-generation'] | false | Training Procedure According to the model card from the [original paper](https://arxiv.org/pdf/2101.03961.pdf) the model has been trained on TPU v3 or TPU v4 pods, using [`t5x`](https://github.com/google-research/t5x) codebase together with [`jax`](https://github.com/google/jax). | 7ac28630add0769b9b705f944ad621b0 |
apache-2.0 | ['text2text-generation'] | false | Testing Data, Factors & Metrics The authors evaluated the model on various tasks and compared the results against T5. See the table below for some quantitative evaluation:  For full details, please check the [re... | 3496fc0de4d089c36a964df2fa20d5d4 |
apache-2.0 | ['text2text-generation'] | false | Citation **BibTeX:** ```bibtex @misc{https://doi.org/10.48550/arxiv.2101.03961, doi = {10.48550/ARXIV.2101.03961}, url = {https://arxiv.org/abs/2101.03961}, author = {Fedus, William and Zoph, Barret and Shazeer, Noam}, keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Compu... | afc6fe94ab22315176bf235b0c134a2a |
mit | [] | false | tudisco on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train... | 4e979855a6af67de24921902938b658b |
apache-2.0 | ['generated_from_trainer'] | 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: 1.1567 - Precision: 0.7234 - Recall: 0.7301 - Fscore: 0.7253 | 2599bfe3ceac9bf1062ba5253527ce01 |
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