librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-24 12:28:46	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 493 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-24 12:27:57	card stringlengths 11 1.01M
mekondjo/distilbert-base-uncased-finetuned-emotion	mekondjo	2022-04-12T15:53:40Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-12T15:39:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9248167911304236 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2219 - Accuracy: 0.9245 - F1: 0.9248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.848 \| 1.0 \| 250 \| 0.3157 \| 0.9075 \| 0.9059 \| \| 0.253 \| 2.0 \| 500 \| 0.2219 \| 0.9245 \| 0.9248 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
surdan/LaBSE_ner_nerel	surdan	2022-04-12T13:17:34Z	1,192	10	transformers	[ "transformers", "pytorch", "bert", "token-classification", "ru", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-11T14:45:16Z	--- language: ["ru", "en"] tasks: - token-classification --- ## About model This model based on [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru). And trained on [surdan/nerel_short](https://huggingface.co/datasets/surdan/nerel_short) dataset You can find more info: - How the model was trained [Train_model.ipynb](https://huggingface.co/surdan/LaBSE_ner_nerel/blob/main/Train_model.ipynb) - Example of usage model [Inference.ipynb](https://huggingface.co/surdan/LaBSE_ner_nerel/blob/main/Inference.ipynb)
luckydog/distilbert-base-uncased-finetuned-emotion	luckydog	2022-04-12T12:36:17Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-12T02:41:30Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9 - name: F1 type: f1 value: 0.8980758869010411 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 - Accuracy: 0.9 - F1: 0.8981 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.2761 \| 1.0 \| 250 \| 0.6036 \| 0.814 \| 0.7881 \| \| 0.4081 \| 2.0 \| 500 \| 0.3298 \| 0.9 \| 0.8981 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
TovaHasi/Toyota_material_calculator_reach_trucks	TovaHasi	2022-04-12T12:33:49Z	0	0	null	[ "license:unlicense", "region:us" ]	null	2022-04-12T12:22:50Z	--- app_file: app.py license: unlicense ---
lewtun/roberta-large-finetuned-clinc-123	lewtun	2022-04-12T12:05:51Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-12T12:00:35Z	--- license: mit tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: roberta-large-finetuned-clinc-123 results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.925483870967742 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-finetuned-clinc-123 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7226 - Accuracy: 0.9255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 5.0576 \| 1.0 \| 120 \| 5.0269 \| 0.0068 \| \| 4.5101 \| 2.0 \| 240 \| 2.9324 \| 0.7158 \| \| 1.9757 \| 3.0 \| 360 \| 0.7226 \| 0.9255 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
Chris1/real2sim	Chris1	2022-04-12T11:33:32Z	0	0	null	[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]	null	2022-04-12T11:33:27Z	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
Chris1/ape2punk_epoch80	Chris1	2022-04-12T11:21:48Z	0	0	null	[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]	null	2022-04-12T11:21:43Z	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```
Kuray107/ls-timit-wsj0-100percent-supervised-meta	Kuray107	2022-04-12T11:19:25Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-11T22:24:57Z	--- tags: - generated_from_trainer model-index: - name: ls-timit-wsj0-100percent-supervised-meta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ls-timit-wsj0-100percent-supervised-meta This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0531 - Wer: 0.0214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.1618 \| 4.57 \| 1000 \| 0.0500 \| 0.0432 \| \| 0.0489 \| 9.13 \| 2000 \| 0.0535 \| 0.0291 \| \| 0.0306 \| 13.7 \| 3000 \| 0.0478 \| 0.0275 \| \| 0.0231 \| 18.26 \| 4000 \| 0.0531 \| 0.0214 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
Eterna2/LayoutParser	Eterna2	2022-04-12T08:58:12Z	0	2	null	[ "detectron2", "layout_parser", "license:apache-2.0", "region:us" ]	null	2022-04-12T08:13:51Z	--- license: apache-2.0 tags: - detectron2 - layout_parser --- Model binaries downloaded from https://github.com/Layout-Parser/layout-parser/blob/c0044a08da7a630e2241348e597a08ba6aa87ba1/src/layoutparser/models/detectron2/catalog.py
adache/tf-distilbert-base-uncased-finetuned-emotion	adache	2022-04-12T08:20:01Z	12	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-12T08:19:50Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tf-distilbert-base-uncased-finetuned-emotion 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Tokenizers 0.11.6
nntadotzip/bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022	nntadotzip	2022-04-12T08:14:00Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-12T07:53:07Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022 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: - Loss: 0.3856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 357 \| 0.4760 \| \| 0.6305 \| 2.0 \| 714 \| 0.3957 \| \| 0.4345 \| 3.0 \| 1071 \| 0.3856 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
adache/distilbert-base-uncased-finetuned-emotion	adache	2022-04-12T07:48:16Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-12T05:43:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion 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: 0.2270 - Accuracy: 0.9245 - F1: 0.9249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8398 \| 1.0 \| 250 \| 0.3276 \| 0.9005 \| 0.8966 \| \| 0.2541 \| 2.0 \| 500 \| 0.2270 \| 0.9245 \| 0.9249 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.6
ID56/FF-Vision-CIFAR	ID56	2022-04-12T07:06:43Z	0	0	null	[ "pytorch", "image-classification", "dataset:cifar10", "license:cc-by-sa-4.0", "region:us" ]	image-classification	2022-04-06T22:02:53Z	--- thumbnail: "https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" license: cc-by-sa-4.0 tags: - image-classification datasets: - cifar10 metrics: - accuracy inference: false --- # CIFAR-10 Upside Down Classifier For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track. <a href="https://wandb.ai/dealer56/cifar-updown-classifier/reports/CIFAR-10-Upside-Down-Classifier-Fatima-Fellowship-2022-Coding-Challenge-Vision---VmlldzoxODA2MDE4" target="_parent"><img src="https://img.shields.io/badge/weights-%26biases-ffcf40" alt="W&B Report"/></a> <img src="https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" alt="Cover Image" width="800"/> ## Usage ### Model Definition ```python from torch import nn import timm from huggingface_hub import PyTorchModelHubMixin class UpDownEfficientNetB0(nn.Module, PyTorchModelHubMixin): """A simple Hub Mixin wrapper for timm EfficientNet-B0. Used to classify whether an image is upright or flipped down, on CIFAR-10.""" def __init__(self, **kwargs): super().__init__() self.base_model = timm.create_model('efficientnet_b0', num_classes=1, drop_rate=0.2, drop_path_rate=0.2) self.config = kwargs.pop("config", None) def forward(self, input): return self.base_model(input) ``` ### Loading the Model from Hub ```python net = UpDownEfficientNetB0.from_pretrained("ID56/FF-Vision-CIFAR") ``` ### Running Inference ```python from torchvision import transforms CIFAR_MEAN = (0.4914, 0.4822, 0.4465) CIFAR_STD = (0.247, 0.243, 0.261) transform = transforms.Compose([ transforms.Resize(40, 40), transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD) ]) image = load_some_image() # Load some PIL Image or uint8 HWC image array image = transform(image) # Convert to CHW image tensor image = image.unsqueeze(0) # Add batch dimension net.eval() pred = net(image) ```
tartuNLP/m2m100_418M_smugri	tartuNLP	2022-04-12T06:38:16Z	5	0	transformers	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-04T11:24:56Z	--- license: mit language: - en widget: - text: "Let us translate some text from Livonian to Võro!" --- # NMT for Finno-Ugric Languages This is an NMT system for translating between Võro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets. ## Tokenizer Four language codes were added to the tokenizer: __liv__, __vro__, __sma__ and __sme__. Currently the m2m100 tokenizer loads the list of languages from a hard-coded list, so it has to be updated after loading; see the code example below. ## Usage example Install the transformers and sentencepiece libraries: `pip install sentencepiece transformers` ```from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tartuNLP/m2m100_418M_smugri") #Fix the language codes in the tokenizer tokenizer.id_to_lang_token = dict(list(tokenizer.id_to_lang_token.items()) + list(tokenizer.added_tokens_decoder.items())) tokenizer.lang_token_to_id = dict(list(tokenizer.lang_token_to_id.items()) + list(tokenizer.added_tokens_encoder.items())) tokenizer.lang_code_to_token = { k.replace("_", ""): k for k in tokenizer.additional_special_tokens } tokenizer.lang_code_to_id = { k.replace("_", ""): v for k, v in tokenizer.lang_token_to_id.items() } model = AutoModelForSeq2SeqLM.from_pretrained("tartuNLP/m2m100_418M_smugri") tokenizer.src_lang = 'liv' encoded_src = tokenizer("Līvõ kēļ jelāb!", return_tensors="pt") encoded_out = model.generate(**encoded_src, forced_bos_token_id = tokenizer.get_lang_id("sme")) print(tokenizer.batch_decode(encoded_out, skip_special_tokens=True)) ``` The output is `Livčča giella eallá.`
gary109/wav2vec2-base-mirst500	gary109	2022-04-12T05:52:24Z	2	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:mir_st500", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2022-04-11T06:13:13Z	--- license: apache-2.0 tags: - audio-classification - generated_from_trainer datasets: - mir_st500 metrics: - accuracy model-index: - name: wav2vec2-base-mirst500 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-mirst500 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /workspace/datasets/datasets/MIR_ST500/MIR_ST500_AUDIO_CLASSIFICATION.py dataset. It achieves the following results on the evaluation set: - Loss: 0.8678 - Accuracy: 0.7017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 1 - seed: 0 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1999 \| 1.0 \| 1304 \| 1.1029 \| 0.5877 \| \| 1.0779 \| 2.0 \| 2608 \| 0.9455 \| 0.6555 \| \| 0.9775 \| 3.0 \| 3912 \| 0.9670 \| 0.6523 \| \| 0.9542 \| 4.0 \| 5216 \| 0.8810 \| 0.6946 \| \| 0.9403 \| 5.0 \| 6520 \| 0.8678 \| 0.7017 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1+cu102 - Datasets 2.0.0 - Tokenizers 0.10.3
kmasiak/FraudDetection	kmasiak	2022-04-12T03:07:59Z	0	0	null	[ "region:us" ]	null	2022-04-06T23:36:56Z	The files in this repository were used for detecting accounting fraud using VAE-GAN and other models. Here is a breakdown of the files: 20220409-21_35_52_ep_3_decoder_model.pth - Decoder I trained that has the best results. 20220409-21_35_52_ep_3_discriminator_model.pth - Discriminator I trained that has the best results. 20220409-21_35_52_ep_3_encoder_model.pth - Encoder I trained that has the best results. Dataset.csv - The dataset used for train/testing, contains 9 features, 532909 regular, 70 global, and 30 local transactions. Fraud_Detection_AutoML.ipynb - AutoSklearnClassifier (an implementation of automl) is used on the fraud detection dataset. Fraud_Detection_Supervised.ipynb - KNN classifier is used on the fraud detection dataset. Gradio_Demo.ipynb - Note this is just for demo purposes. The actual implementation of the VAE-GAN model is not used in the gradio demo due to time constraints. SMOTE_VAE_GAN.ipynb - Use SMOTE to help mitigate the issue of an unbalanced dataset while training. VAE_GAN_Test.ipynb - Evaluates a VAE-GAN model. VAE_GAN_Train.ipynb - Trains the VAE-GAN model on the fraud detection dataset. ep_100_decoder_model.pth - Pre-trained decoder from a previous paper I used to improve results. ep_100_discriminator_model.pth - Pre-trained discriminator from a previous paper I used to improve results. ep_100_encoder_model.pth - Pre-trained encoder from a previous paper I used to improve results. Note: Credit for the above 3 files goes to Credit goes to Jie Dai, Chenjian Wang, and Shuoyi Wei. Accounting Fraud Detection with VAE-GAN, 2020.
NoCaptain/DistilRoBERTa-C19-Vax-Fine-tuned	NoCaptain	2022-04-12T00:34:14Z	2	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-10T00:51:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - accuracy - f1 model-index: - name: DistilRoberta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DistilRoberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1246 - Precision: 0.9633 - Accuracy: 0.9697 - F1: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:--------:\|:------:\| \| 0.5894 \| 0.4 \| 500 \| 0.4710 \| 0.8381 \| 0.7747 \| 0.7584 \| \| 0.3863 \| 0.8 \| 1000 \| 0.3000 \| 0.8226 \| 0.8737 \| 0.8858 \| \| 0.2272 \| 1.2 \| 1500 \| 0.1973 \| 0.9593 \| 0.9333 \| 0.9329 \| \| 0.1639 \| 1.6 \| 2000 \| 0.1694 \| 0.9067 \| 0.9367 \| 0.9403 \| \| 0.1263 \| 2.0 \| 2500 \| 0.1128 \| 0.9657 \| 0.9597 \| 0.9603 \| \| 0.0753 \| 2.4 \| 3000 \| 0.1305 \| 0.9614 \| 0.967 \| 0.9679 \| \| 0.0619 \| 2.8 \| 3500 \| 0.1246 \| 0.9633 \| 0.9697 \| 0.9705 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
modhp/wav2vec2-model2-torgo	modhp	2022-04-11T23:31:56Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-08T19:47:36Z	--- tags: - generated_from_trainer model-index: - name: wav2vec2-model2-torgo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-model2-torgo This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9975 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---:\| \| 12.5453 \| 0.76 \| 500 \| 14.6490 \| 1.0 \| \| 4.8036 \| 1.53 \| 1000 \| 8.4523 \| 1.0 \| \| 5.0421 \| 2.29 \| 1500 \| 5.4114 \| 1.0 \| \| 5.2055 \| 3.05 \| 2000 \| 11.0507 \| 1.0 \| \| 4.6389 \| 3.82 \| 2500 \| 4.6792 \| 1.0 \| \| 4.5523 \| 4.58 \| 3000 \| 4.7855 \| 1.0 \| \| 4.7843 \| 5.34 \| 3500 \| 11.2783 \| 1.0 \| \| 4.6066 \| 6.11 \| 4000 \| 8.7807 \| 1.0 \| \| 4.7382 \| 6.87 \| 4500 \| 2942.0220 \| 1.0 \| \| 130.5733 \| 7.63 \| 5000 \| 5.8412 \| 1.0 \| \| 4.4972 \| 8.4 \| 5500 \| 17.7038 \| 1.0 \| \| 4.5196 \| 9.16 \| 6000 \| 11.4548 \| 1.0 \| \| 4.3198 \| 9.92 \| 6500 \| 6.0885 \| 1.0 \| \| 4.4273 \| 10.69 \| 7000 \| 6.7374 \| 1.0 \| \| 4.2783 \| 11.45 \| 7500 \| 4.7276 \| 1.0 \| \| 4.2985 \| 12.21 \| 8000 \| 6.1412 \| 1.0 \| \| 4.3262 \| 12.98 \| 8500 \| 5.2621 \| 1.0 \| \| 4.1705 \| 13.74 \| 9000 \| 5.2214 \| 1.0 \| \| 4.3176 \| 14.5 \| 9500 \| 5.5359 \| 1.0 \| \| 3.9808 \| 15.27 \| 10000 \| 4.1537 \| 1.0 \| \| 4.0228 \| 16.03 \| 10500 \| 4.2962 \| 1.0 \| \| 4.0595 \| 16.79 \| 11000 \| 7.6361 \| 1.0 \| \| 4.0088 \| 17.56 \| 11500 \| 6.8715 \| 1.0 \| \| 3.8727 \| 18.32 \| 12000 \| 8.8657 \| 1.0 \| \| 4.0073 \| 19.08 \| 12500 \| 5.8170 \| 1.0 \| \| 3.8511 \| 19.85 \| 13000 \| 13.9836 \| 1.0 \| \| 4.0899 \| 20.61 \| 13500 \| 5.3287 \| 1.0 \| \| 3.8782 \| 21.37 \| 14000 \| 8.0635 \| 1.0 \| \| 3.9235 \| 22.14 \| 14500 \| 5.5129 \| 1.0 \| \| 3.7276 \| 22.9 \| 15000 \| 5.0819 \| 1.0 \| \| 3.7908 \| 23.66 \| 15500 \| 6.1458 \| 1.0 \| \| 3.9176 \| 24.43 \| 16000 \| 4.6094 \| 1.0 \| \| 3.8477 \| 25.19 \| 16500 \| 5.1406 \| 1.0 \| \| 3.6917 \| 25.95 \| 17000 \| 4.5684 \| 1.0 \| \| 3.8568 \| 26.72 \| 17500 \| 4.0306 \| 1.0 \| \| 3.7231 \| 27.48 \| 18000 \| 5.6331 \| 1.0 \| \| 3.8145 \| 28.24 \| 18500 \| 8.2997 \| 1.0 \| \| 3.7809 \| 29.01 \| 19000 \| 5.7468 \| 1.0 \| \| 3.5995 \| 29.77 \| 19500 \| 4.9975 \| 1.0 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 1.18.3 - Tokenizers 0.11.6
huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings	huggingtweets	2022-04-11T22:44:40Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-11T22:43:38Z	--- language: en thumbnail: http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506323689456947207/xBvvxyQr_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000610482331/8808c2f408b97fe3646f2dca86441506_400x400.jpeg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">makeouthill & VacuumF & Jason Hendricks & Angry Memories</div> <div style="text-align: center; font-size: 14px;">@angrymemorys-oldandtoothless-sadboi666_-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from makeouthill & VacuumF & Jason Hendricks & Angry Memories. \| Data \| makeouthill \| VacuumF \| Jason Hendricks \| Angry Memories \| \| --- \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 321 \| 425 \| 3250 \| 3199 \| \| Retweets \| 34 \| 0 \| 0 \| 941 \| \| Short tweets \| 49 \| 31 \| 0 \| 1110 \| \| Tweets kept \| 238 \| 394 \| 3250 \| 1148 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nh2rd94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @angrymemorys-oldandtoothless-sadboi666_-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
adasnew/t5-small-xsum	adasnew	2022-04-11T22:35:12Z	18	1	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-11T18:45:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.3953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.8641 \| 0.04 \| 500 \| 2.6202 \| \| 2.7466 \| 0.08 \| 1000 \| 2.5660 \| \| 2.8767 \| 0.12 \| 1500 \| 2.5319 \| \| 2.7099 \| 0.16 \| 2000 \| 2.5107 \| \| 2.7752 \| 0.2 \| 2500 \| 2.4922 \| \| 2.6037 \| 0.24 \| 3000 \| 2.4800 \| \| 2.8236 \| 0.27 \| 3500 \| 2.4677 \| \| 2.7089 \| 0.31 \| 4000 \| 2.4581 \| \| 2.7299 \| 0.35 \| 4500 \| 2.4498 \| \| 2.7498 \| 0.39 \| 5000 \| 2.4420 \| \| 2.6186 \| 0.43 \| 5500 \| 2.4346 \| \| 2.7817 \| 0.47 \| 6000 \| 2.4288 \| \| 2.5559 \| 0.51 \| 6500 \| 2.4239 \| \| 2.6725 \| 0.55 \| 7000 \| 2.4186 \| \| 2.6316 \| 0.59 \| 7500 \| 2.4149 \| \| 2.5561 \| 0.63 \| 8000 \| 2.4115 \| \| 2.5708 \| 0.67 \| 8500 \| 2.4097 \| \| 2.5861 \| 0.71 \| 9000 \| 2.4052 \| \| 2.6363 \| 0.74 \| 9500 \| 2.4024 \| \| 2.7435 \| 0.78 \| 10000 \| 2.4003 \| \| 2.7258 \| 0.82 \| 10500 \| 2.3992 \| \| 2.6113 \| 0.86 \| 11000 \| 2.3983 \| \| 2.6006 \| 0.9 \| 11500 \| 2.3972 \| \| 2.5684 \| 0.94 \| 12000 \| 2.3960 \| \| 2.6181 \| 0.98 \| 12500 \| 2.3953 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
tonyalves/ft-pt-br-local-2	tonyalves	2022-04-11T20:57:03Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-11T20:46:13Z	--- license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer model-index: - name: ft-pt-br-local-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ft-pt-br-local-2 This model is a fine-tuned version of [tonyalves/output](https://huggingface.co/tonyalves/output) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6
akoksal/bounti	akoksal	2022-04-11T20:12:25Z	304	6	transformers	[ "transformers", "pytorch", "bert", "text-classification", "sentiment", "twitter", "turkish", "tr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-11T19:55:36Z	--- language: "tr" tags: - sentiment - twitter - turkish --- This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained [BERTurk model 128k uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) with [BounTi dataset](https://ieeexplore.ieee.org/document/9477814). ## Usage in Hugging Face Pipeline ``` from transformers import pipeline bounti = pipeline("sentiment-analysis",model="akoksal/bounti") print(bounti("Bu yemeği pek sevmedim")) >> [{'label': 'negative', 'score': 0.8012508153915405}] ``` ## Results The scores of the finetuned model with BERTurk: \|\|Accuracy\|Precision\|Recall\|F1\| \|-------------\|:---------:\|:---------:\|:------:\|:-----:\| \|Validation\|0.745\|0.706\|0.730\|0.715\| \|Test\|0.723\|0.692\|0.729\|0.701\| ## Dataset You can find the dataset in [our Github repo](https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis) with the training, validation, and test splits. Due to Twitter copyright, we cannot release the full text of the tweets. We share the tweet IDs, and the full text can be downloaded through official Twitter API. \| \| Training \| Validation \| Test \| \|----------\|:--------:\|:----------:\|:----:\| \| Positive \| 1691 \| 188 \| 469 \| \| Neutral \| 3034 \| 338 \| 843 \| \| Negative \| 1008 \| 113 \| 280 \| \| Total \| 5733 \| 639 \| 1592 \| ## Citation You can cite the following paper if you use our work: ``` @INPROCEEDINGS{BounTi, author={Köksal, Abdullatif and Özgür, Arzucan}, booktitle={2021 29th Signal Processing and Communications Applications Conference (SIU)}, title={Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis}, year={2021}, volume={}, number={} } ``` ---
Kuray107/ls-timit-100percent-supervised-meta	Kuray107	2022-04-11T19:44:56Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-11T14:57:43Z	--- tags: - generated_from_trainer model-index: - name: ls-timit-100percent-supervised-meta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ls-timit-100percent-supervised-meta This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0649 - Wer: 0.0253 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0964 \| 7.04 \| 1000 \| 0.0706 \| 0.0342 \| \| 0.0445 \| 14.08 \| 2000 \| 0.0649 \| 0.0253 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
nateraw/fastai-dummy-learner	nateraw	2022-04-11T19:23:08Z	0	0	fastai	[ "fastai", "region:us" ]	null	2022-04-11T19:15:53Z	--- tags: - fastai --- # Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join our fastai community on the Hugging Face Discord! Greetings fellow fastlearner 🤝! --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
jegormeister/robbert-v2-dutch-base-mqa-finetuned	jegormeister	2022-04-11T19:09:29Z	1,058	4	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "robbert", "nl", "dataset:clips/mqa", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-04-11T13:40:02Z	--- language: nl pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - robbert datasets: - clips/mqa --- # jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base). It was fine-tuned on 1,000,000 rows of Dutch FAQ question-answer pairs from [clips/mqa](https://huggingface.co/datasets/clips/mqa). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') model = AutoModel.from_pretrained('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 12500 with parameters: ``` {'batch_size': 80, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
TrabajoAprendizajeProfundo/Trabajo	TrabajoAprendizajeProfundo	2022-04-11T17:27:15Z	5	0	stable-baselines3	[ "stable-baselines3", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]	reinforcement-learning	2022-04-09T11:48:09Z	--- tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 --- # TODO: Fill this model card This is a pre-trained model of agent playing Asteroids-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. ### Usage (with Stable-baselines3) Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: ``` pip install stable-baselines3 pip install huggingface_sb3 ``` Then, you can use the model like this: ```python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="TrabajoAprendizajeProfundo/Trabajo", filename="Asteroids-v0.zip") model = PPO.load(checkpoint) # Evaluate the agent eval_env = gym.make('Asteroids-v0') mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") # Watch the agent play directory = './video' env = Recorder(env, directory) obs = env.reset() done = False while not done: action, _state = model2.predict(obs) obs, reward, done, info = env.step(action) env.play() ``` ### Evaluation Results mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
tbosse/bert-base-german-cased-finetuned-subj_v5_11Epoch	tbosse	2022-04-11T17:08:55Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-11T15:51:15Z	--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_v5_11Epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-german-cased-finetuned-subj_v5_11Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3467 - Precision: 0.8240 - Recall: 0.8287 - F1: 0.8263 - Accuracy: 0.9198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 32 \| 0.3485 \| 0.6992 \| 0.7051 \| 0.7021 \| 0.8639 \| \| No log \| 2.0 \| 64 \| 0.2679 \| 0.7947 \| 0.7612 \| 0.7776 \| 0.8994 \| \| No log \| 3.0 \| 96 \| 0.2555 \| 0.8073 \| 0.8118 \| 0.8095 \| 0.9112 \| \| No log \| 4.0 \| 128 \| 0.2591 \| 0.8290 \| 0.8034 \| 0.8160 \| 0.9132 \| \| No log \| 5.0 \| 160 \| 0.2808 \| 0.8450 \| 0.8118 \| 0.8281 \| 0.9158 \| \| No log \| 6.0 \| 192 \| 0.2953 \| 0.8386 \| 0.8174 \| 0.8279 \| 0.9172 \| \| No log \| 7.0 \| 224 \| 0.3164 \| 0.8347 \| 0.8371 \| 0.8359 \| 0.9204 \| \| No log \| 8.0 \| 256 \| 0.3267 \| 0.8329 \| 0.8258 \| 0.8293 \| 0.9178 \| \| No log \| 9.0 \| 288 \| 0.3373 \| 0.8268 \| 0.8315 \| 0.8291 \| 0.9198 \| \| No log \| 10.0 \| 320 \| 0.3450 \| 0.8324 \| 0.8230 \| 0.8277 \| 0.9211 \| \| No log \| 11.0 \| 352 \| 0.3467 \| 0.8240 \| 0.8287 \| 0.8263 \| 0.9198 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment	Giyaseddin	2022-04-11T15:17:08Z	6	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-11T09:03:04Z	--- license: apache-2.0 language: en library: transformers other: distilbert datasets: - Short Question Answer Assessment Dataset --- # DistilBERT base uncased model for Short Question Answer Assessment ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. This is a classification model that solves Short Question Answer Assessment task, finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-uncased) on [Question Answer Assessment dataset](#) ## Intended uses & limitations This can only be used for the kind of questions and answers provided by that are similar to the ones in the dataset of [Banjade et al.](https://aclanthology.org/W16-0520.pdf). ### How to use You can use this model directly with a : ```python >>> from transformers import pipeline >>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment", return_all_scores=True) >>> context = "To rescue a child who has fallen down a well, rescue workers fasten him to a rope, the other end of which is then reeled in by a machine. The rope pulls the child straight upward at steady speed." >>> question = "How does the amount of tension in the rope compare to the downward force of gravity acting on the child?" >>> ref_answer = "Since the child is being raised straight upward at a constant speed, the net force on the child is zero and all the forces balance. That means that the tension in the rope balances the downward force of gravity." >>> student_answer = "The tension force is higher than the force of gravity." >>> >>> body = " [SEP] ".join([context, question, ref_answer, student_answer]) >>> raw_results = classifier([body]) >>> raw_results [[{'label': 'LABEL_0', 'score': 0.0004029414849355817}, {'label': 'LABEL_1', 'score': 0.0005476847873069346}, {'label': 'LABEL_2', 'score': 0.998059093952179}, {'label': 'LABEL_3', 'score': 0.0009902542224153876}]] >>> _LABELS_ID2NAME = {0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"} >>> results = [] >>> for result in raw_results: for score in result: results.append([ {_LABELS_ID2NAME[int(score["label"][-1:])]: "%.2f" % score["score"]} ]) >>> results [[{'correct': '0.00'}], [{'correct_but_incomplete': '0.00'}], [{'contradictory': '1.00'}], [{'incorrect': '0.00'}]] ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). This bias will also affect all fine-tuned versions of this model. Also one of the limiations of this model is the length, longer sequences would lead to wrong predictions, due to the pre-processing phase (after concatentating the input sequences, the important student answer might be pruned!) ## Pre-training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Fine-tuning data The annotated dataset consists of 900 students’ short constructed answers and their correctness in the given context. Four qualitative levels of correctness are defined, correct, correct-but-incomplete, contradictory and Incorrect. ## Training procedure ### Preprocessing In the preprocessing phase, the following parts are concatenated: _question context_, _question_, _reference_answer_, and _student_answer_ using the separator `[SEP]`. This makes the full text as: ``` [CLS] Context Sentence [SEP] Question Sentence [SEP] Reference Answer Sentence [SEP] Student Answer Sentence [CLS] ``` The data are splitted according to the following ratio: - Training set 80%. - Test set 20%. Lables are mapped as: `{0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"}` ### Fine-tuning The model was finetuned on GeForce GTX 960M for 20 minuts. The parameters are: \| Parameter \| Value \| \|:-------------------:\|:-----:\| \| Learning rate \| 5e-5 \| \| Weight decay \| 0.01 \| \| Training batch size \| 8 \| \| Epochs \| 4 \| Here is the scores during the training: \| Epoch \| Training Loss \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:----------:\|:-------------:\|:-----------------:\|:----------:\|:---------:\|:----------:\|:--------:\| \| 1 \| No log \| 0.665765 \| 0.755330 \| 0.743574 \| 0.781210 \| 0.755330 \| \| 2 \| 0.932100 \| 0.362124 \| 0.890355 \| 0.889875 \| 0.891407 \| 0.890355 \| \| 3 \| 0.364900 \| 0.226225 \| 0.942132 \| 0.941802 \| 0.942458 \| 0.942132 \| \| 3 \| 0.176900 \| 0.193660 \| 0.954315 \| 0.954175 \| 0.954985 \| 0.954315 \| ## Evaluation results When fine-tuned on downstream task of Question Answer Assessment, 4 class classification, this model achieved the following results: (scores are rounded to 2 floating points) \| \| precision \| recall \| f1-score \| support \| \|:------------------------:\|:----------:\|:-------:\|:--------:\|:-------:\| \| _correct_ \| 0.938 \| 0.989 \| 0.963 \| 366 \| \| _correct_but_incomplete_ \| 0.975 \| 0.922 \| 0.948 \| 257 \| \| _contradictory_ \| 0.946 \| 0.938 \| 0.942 \| 113 \| \| _incorrect_ \| 0.963 \| 0.944 \| 0.953 \| 249 \| \| accuracy \| - \| - \| 0.954 \| 985 \| \| macro avg \| 0.956 \| 0.948 \| 0.952 \| 985 \| \| weighted avg \| 0.955 \| 0.954 \| 0.954 \| 985 \| Confusion matrix: \| Actual \ Predicted \| _correct_ \| _correct_but_incomplete_ \| _contradictory_ \| _incorrect_ \| \|:------------------------:\|:---------:\|:------------------------:\|:---------------:\|:-----------:\| \| _correct_ \| 362 \| 4 \| 0 \| 0 \| \| _correct_but_incomplete_ \| 13 \| 237 \| 0 \| 7 \| \| _contradictory_ \| 4 \| 1 \| 106 \| 2 \| \| _incorrect_ \| 7 \| 1 \| 6 \| 235 \| The AUC score is: 'micro'= 0.9695 and 'macro': 0.9659
optimum/neuron-MiniLMv2-L12-H384-distilled-finetuned-clinc	optimum	2022-04-11T13:44:33Z	5	1	transformers	[ "transformers", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-11T13:38:35Z	--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Neuron conversation # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9389999 ## Deploy/use Model If you want to use this model checkout the following notenbook: [sagemaker/18_inferentia_inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb) ```python from sagemaker.huggingface.model import HuggingFaceModel # create Hugging Face Model Class huggingface_model = HuggingFaceModel( model_data=s3_model_uri, # path to your model and script role=role, # iam role with permissions to create an Endpoint transformers_version="4.12", # transformers version used pytorch_version="1.9", # pytorch version used py_version='py37', # python version used ) # Let SageMaker know that we've already compiled the model via neuron-cc huggingface_model._is_compiled_model = True # deploy the endpoint endpoint predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type="ml.inf1.xlarge" # AWS Inferentia Instance ) ```
aleksavega/t5-efficient-base-finetuned-1.2	aleksavega	2022-04-11T12:04:08Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-04-11T09:53:00Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-efficient-base-finetuned-1.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-efficient-base-finetuned-1.2 This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5294 - Rouge1: 62.691 - Rouge2: 55.9731 - Rougel: 60.9097 - Rougelsum: 61.4393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4662 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\| \| 2.2424 \| 1.0 \| 1217 \| 1.7042 \| 34.2215 \| 24.2754 \| 31.7289 \| 32.4237 \| \| 1.7716 \| 2.0 \| 2434 \| 1.6184 \| 43.4774 \| 34.0476 \| 41.3691 \| 41.9132 \| \| 1.6324 \| 3.0 \| 3651 \| 1.5811 \| 49.1441 \| 40.7935 \| 47.0077 \| 47.6388 \| \| 1.5226 \| 4.0 \| 4868 \| 1.5243 \| 54.4769 \| 46.3387 \| 52.3289 \| 52.9555 \| \| 1.4121 \| 5.0 \| 6085 \| 1.5040 \| 56.8792 \| 49.1963 \| 54.7327 \| 55.2805 \| \| 1.331 \| 6.0 \| 7302 \| 1.4930 \| 58.6896 \| 51.1683 \| 56.7096 \| 57.3605 \| \| 1.2677 \| 7.0 \| 8519 \| 1.4785 \| 59.9285 \| 52.4631 \| 57.8575 \| 58.4203 \| \| 1.2175 \| 8.0 \| 9736 \| 1.4839 \| 60.0299 \| 52.8806 \| 58.0099 \| 58.6348 \| \| 1.1782 \| 9.0 \| 10953 \| 1.4908 \| 61.247 \| 54.0887 \| 59.2175 \| 59.7658 \| \| 1.1442 \| 10.0 \| 12170 \| 1.4882 \| 61.9895 \| 54.9455 \| 60.0728 \| 60.5786 \| \| 1.1118 \| 11.0 \| 13387 \| 1.5061 \| 62.1077 \| 55.1276 \| 60.2218 \| 60.7475 \| \| 1.081 \| 12.0 \| 14604 \| 1.5078 \| 61.6083 \| 54.6805 \| 59.7912 \| 60.2489 \| \| 1.0668 \| 13.0 \| 15821 \| 1.5200 \| 62.3075 \| 55.5201 \| 60.5192 \| 60.9557 \| \| 1.0488 \| 14.0 \| 17038 \| 1.5344 \| 62.5144 \| 55.6332 \| 60.6845 \| 61.1715 \| \| 1.0324 \| 15.0 \| 18255 \| 1.5313 \| 62.7697 \| 56.0313 \| 60.9298 \| 61.4739 \| \| 1.0302 \| 16.0 \| 19472 \| 1.5294 \| 62.691 \| 55.9731 \| 60.9097 \| 61.4393 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6
optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc	optimum	2022-04-11T11:21:21Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-11T11:18:49Z	--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3479 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 60 \| 0.8171 \| 0.2490 \| \| No log \| 2.0 \| 120 \| 0.7039 \| 0.6568 \| \| No log \| 3.0 \| 180 \| 0.6067 \| 0.7932 \| \| 0.7269 \| 4.0 \| 240 \| 0.5270 \| 0.8674 \| \| 0.7269 \| 5.0 \| 300 \| 0.4659 \| 0.9010 \| \| 0.7269 \| 6.0 \| 360 \| 0.4201 \| 0.9194 \| \| 0.7269 \| 7.0 \| 420 \| 0.3867 \| 0.9352 \| \| 0.4426 \| 8.0 \| 480 \| 0.3649 \| 0.9352 \| \| 0.4426 \| 9.0 \| 540 \| 0.3520 \| 0.9403 \| \| 0.4426 \| 10.0 \| 600 \| 0.3479 \| 0.94 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3
vocab-transformers/distilbert-tokenizer_256k-MLM_best	vocab-transformers	2022-04-11T11:16:06Z	5	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-11T11:14:12Z	# DistilBERT with 256k token embeddings This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs. Then the model was trained on this dataset with MLM for 1.55M steps (batch size 64). The token embeddings were updated during MLM.
vocab-transformers/distilbert-word2vec_256k-MLM_best	vocab-transformers	2022-04-11T11:13:13Z	27	4	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-11T11:10:05Z	# DistilBERT with word2vec token embeddings This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs. Then the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddings were NOT updated. For the initial word2vec weights with Gensim see: [https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec](https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec)
Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-3	Chikashi	2022-04-11T08:17:07Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-10T23:51:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 27.1711 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3163 - Rouge1: 27.1711 - Rouge2: 10.6296 - Rougel: 23.206 - Rougelsum: 26.4801 - Gen Len: 18.5433 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.0734 \| 0.25 \| 5000 \| 2.7884 \| 22.4825 \| 7.2492 \| 19.243 \| 21.9167 \| 18.0616 \| \| 2.9201 \| 0.51 \| 10000 \| 2.7089 \| 24.0869 \| 8.0348 \| 20.4814 \| 23.4541 \| 18.5994 \| \| 2.8403 \| 0.76 \| 15000 \| 2.6390 \| 24.62 \| 8.3776 \| 20.8736 \| 23.9784 \| 18.4676 \| \| 2.7764 \| 1.02 \| 20000 \| 2.5943 \| 24.1504 \| 8.3933 \| 20.8271 \| 23.5382 \| 18.4078 \| \| 2.6641 \| 1.27 \| 25000 \| 2.5428 \| 25.6574 \| 9.2371 \| 21.8576 \| 24.9558 \| 18.4249 \| \| 2.6369 \| 1.53 \| 30000 \| 2.5042 \| 25.5208 \| 9.254 \| 21.6673 \| 24.8589 \| 18.6467 \| \| 2.6 \| 1.78 \| 35000 \| 2.4637 \| 26.094 \| 9.7003 \| 22.3097 \| 25.4695 \| 18.5065 \| \| 2.5562 \| 2.03 \| 40000 \| 2.4285 \| 26.5374 \| 9.9222 \| 22.5291 \| 25.8836 \| 18.5553 \| \| 2.4322 \| 2.29 \| 45000 \| 2.3858 \| 26.939 \| 10.3555 \| 23.0211 \| 26.2834 \| 18.5614 \| \| 2.4106 \| 2.54 \| 50000 \| 2.3537 \| 26.7423 \| 10.2816 \| 22.7986 \| 26.083 \| 18.5792 \| \| 2.3731 \| 2.8 \| 55000 \| 2.3163 \| 27.1711 \| 10.6296 \| 23.206 \| 26.4801 \| 18.5433 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggan/pix2pix-facades-demo	huggan	2022-04-11T08:09:26Z	0	0	null	[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]	null	2022-04-09T13:16:10Z	--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- This was run from this implementation: https://github.com/NielsRogge/community-events-1/blob/improve_pix2pix/huggan/pytorch/pix2pix/train.py The command to run was: ```bash accelerate launch train.py --checkpoint_interval 1 --push_to_hub --output_dir pix2pix-facades --hub_model_id huggan/pix2pix-facades-demo --wandb ```
Mandela/DialoGPT-small-DEADPOOL	Mandela	2022-04-10T23:43:06Z	0	0	null	[ "conversation", "region:us" ]	null	2022-04-10T16:49:29Z	--- language: - python tags: - conversation ---
Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-5	Chikashi	2022-04-10T23:42:14Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-09T19:16:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 26.1071 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.4351 - Rouge1: 26.1071 - Rouge2: 9.3627 - Rougel: 22.0825 - Rougelsum: 25.4514 - Gen Len: 18.474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.9216 \| 0.13 \| 5000 \| 2.6385 \| 23.8039 \| 7.8863 \| 20.0109 \| 23.0802 \| 18.3481 \| \| 2.8158 \| 0.25 \| 10000 \| 2.5884 \| 24.2567 \| 8.2003 \| 20.438 \| 23.5325 \| 18.3833 \| \| 2.7743 \| 0.38 \| 15000 \| 2.5623 \| 24.8471 \| 8.3768 \| 20.8711 \| 24.1114 \| 18.2901 \| \| 2.7598 \| 0.51 \| 20000 \| 2.5368 \| 25.1566 \| 8.6721 \| 21.1896 \| 24.4558 \| 18.3561 \| \| 2.7192 \| 0.64 \| 25000 \| 2.5220 \| 25.3477 \| 8.8106 \| 21.3799 \| 24.6742 \| 18.3108 \| \| 2.7207 \| 0.76 \| 30000 \| 2.5114 \| 25.5912 \| 8.998 \| 21.5508 \| 24.9344 \| 18.3445 \| \| 2.7041 \| 0.89 \| 35000 \| 2.4993 \| 25.457 \| 8.8644 \| 21.4516 \| 24.7965 \| 18.4354 \| \| 2.687 \| 1.02 \| 40000 \| 2.4879 \| 25.5886 \| 8.9766 \| 21.6794 \| 24.9512 \| 18.4035 \| \| 2.6652 \| 1.14 \| 45000 \| 2.4848 \| 25.7367 \| 9.078 \| 21.7096 \| 25.0924 \| 18.4328 \| \| 2.6536 \| 1.27 \| 50000 \| 2.4761 \| 25.7368 \| 9.1609 \| 21.729 \| 25.0866 \| 18.3117 \| \| 2.6589 \| 1.4 \| 55000 \| 2.4702 \| 25.7738 \| 9.1413 \| 21.7492 \| 25.114 \| 18.4862 \| \| 2.6384 \| 1.53 \| 60000 \| 2.4620 \| 25.7433 \| 9.1356 \| 21.8198 \| 25.0896 \| 18.489 \| \| 2.6337 \| 1.65 \| 65000 \| 2.4595 \| 26.0919 \| 9.2605 \| 21.9447 \| 25.4065 \| 18.4083 \| \| 2.6375 \| 1.78 \| 70000 \| 2.4557 \| 26.0912 \| 9.3469 \| 22.0182 \| 25.4428 \| 18.4133 \| \| 2.6441 \| 1.91 \| 75000 \| 2.4502 \| 26.1366 \| 9.3143 \| 22.058 \| 25.4673 \| 18.4972 \| \| 2.6276 \| 2.03 \| 80000 \| 2.4478 \| 25.9929 \| 9.2464 \| 21.9271 \| 25.3263 \| 18.469 \| \| 2.6062 \| 2.16 \| 85000 \| 2.4467 \| 26.0465 \| 9.3166 \| 22.0342 \| 25.3998 \| 18.3777 \| \| 2.6126 \| 2.29 \| 90000 \| 2.4407 \| 26.1953 \| 9.3848 \| 22.1148 \| 25.5161 \| 18.467 \| \| 2.6182 \| 2.42 \| 95000 \| 2.4397 \| 26.1331 \| 9.3626 \| 22.1076 \| 25.4627 \| 18.4413 \| \| 2.6041 \| 2.54 \| 100000 \| 2.4375 \| 26.1301 \| 9.3567 \| 22.0869 \| 25.465 \| 18.4929 \| \| 2.5996 \| 2.67 \| 105000 \| 2.4367 \| 26.0956 \| 9.3314 \| 22.063 \| 25.4242 \| 18.5074 \| \| 2.6144 \| 2.8 \| 110000 \| 2.4355 \| 26.1764 \| 9.4157 \| 22.1231 \| 25.5175 \| 18.4729 \| \| 2.608 \| 2.93 \| 115000 \| 2.4351 \| 26.1071 \| 9.3627 \| 22.0825 \| 25.4514 \| 18.474 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/s_m_frank	huggingtweets	2022-04-10T22:28:09Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-10T22:27:04Z	--- language: en thumbnail: http://www.huggingtweets.com/s_m_frank/1649629685555/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1480658144833515525/DS0AOK_d_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">cute junco observer</div> <div style="text-align: center; font-size: 14px;">@s_m_frank</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from cute junco observer. \| Data \| cute junco observer \| \| --- \| --- \| \| Tweets downloaded \| 1253 \| \| Retweets \| 482 \| \| Short tweets \| 184 \| \| Tweets kept \| 587 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s2slp94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @s_m_frank's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bjkzwlr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bjkzwlr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/s_m_frank') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
tonyalves/local_dataset	tonyalves	2022-04-10T22:23:55Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-10T22:05:01Z	--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer model-index: - name: local_dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # local_dataset 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 - PT dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/nordicshrew	huggingtweets	2022-04-10T22:04:13Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-10T22:02:07Z	--- language: en thumbnail: http://www.huggingtweets.com/nordicshrew/1649628249290/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1129935220260704256/RSmw3S0E_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">guelph’s finest poster</div> <div style="text-align: center; font-size: 14px;">@nordicshrew</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from guelph’s finest poster. \| Data \| guelph’s finest poster \| \| --- \| --- \| \| Tweets downloaded \| 3219 \| \| Retweets \| 429 \| \| Short tweets \| 145 \| \| Tweets kept \| 2645 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ywrep7o1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nordicshrew's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jti1kl9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jti1kl9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nordicshrew') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
keerthisaran/distilbert-base-uncased-finetuned-emotion	keerthisaran	2022-04-10T21:58:34Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-14T18:45:20Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.920435758296201 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.92 - F1: 0.9204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8464 \| 1.0 \| 250 \| 0.3125 \| 0.9085 \| 0.9061 \| \| 0.2476 \| 2.0 \| 500 \| 0.2183 \| 0.92 \| 0.9204 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Ayobami/UpsideDownDetector	Ayobami	2022-04-10T20:42:37Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-04-10T06:26:56Z	--- license: mit --- An image rotation detector trained to detect if an image is upside down or not
BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal	BigSalmon	2022-04-10T20:04:26Z	3	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-04-04T05:04:06Z	It works worse than the GPT-2 Large & Medium models I have been training, because I don't have the compute needed to train the entire dataset I have. I had to resort to using bits. ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal") model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. * - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. * - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. * informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. * informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` Points and keywords. Informal to formal.
huggingtweets/graveyard_plots-hel_ql-witheredstrings	huggingtweets	2022-04-10T19:16:31Z	2	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-10T19:15:53Z	--- language: en thumbnail: http://www.huggingtweets.com/graveyard_plots-hel_ql-witheredstrings/1649618186549/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1457045233783701504/fnjAg6lH_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1332861091119046661/7ZD3Nqqg_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">GHANEM & Anthropos & darth hattie</div> <div style="text-align: center; font-size: 14px;">@graveyard_plots-hel_ql-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from GHANEM & Anthropos & darth hattie. \| Data \| GHANEM \| Anthropos \| darth hattie \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 413 \| 1175 \| 1288 \| \| Retweets \| 1 \| 354 \| 9 \| \| Short tweets \| 18 \| 92 \| 146 \| \| Tweets kept \| 394 \| 729 \| 1133 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26q7h6ze/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @graveyard_plots-hel_ql-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vrvcbh4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vrvcbh4/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/graveyard_plots-hel_ql-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nepp1d0/SingleBertSmilesTargetInteraction	nepp1d0	2022-04-10T18:55:03Z	5	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-09T14:05:51Z	Prot_bert finetuned on GPCR_train dataset of Drug Target prediction Trainig paramenters: overwrite_output_dir=True, evaluation_strategy="epoch", learning_rate=1e-3, weight_decay=0.001, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, push_to_hub=True, fp16=True, logging_steps=logging_steps, save_strategy='epoch', num_train_epochs=2
danhsf/xlm-roberta-base-finetuned-panx-de-fr	danhsf	2022-04-10T18:21:26Z	4	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T18:01:12Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - F1: 0.8582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2885 \| 1.0 \| 715 \| 0.1817 \| 0.8287 \| \| 0.1497 \| 2.0 \| 1430 \| 0.1618 \| 0.8442 \| \| 0.0944 \| 3.0 \| 2145 \| 0.1667 \| 0.8582 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
V3RX2000/xlm-roberta-base-finetuned-panx-it	V3RX2000	2022-04-10T15:46:48Z	2	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T13:26:51Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.822805578342904 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2323 - F1: 0.8228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8126 \| 1.0 \| 70 \| 0.3361 \| 0.7231 \| \| 0.2995 \| 2.0 \| 140 \| 0.2526 \| 0.8079 \| \| 0.1865 \| 3.0 \| 210 \| 0.2323 \| 0.8228 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
V3RX2000/xlm-roberta-base-finetuned-panx-fr	V3RX2000	2022-04-10T15:39:32Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T13:08:53Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8354854938789199 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2651 - F1: 0.8355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5954 \| 1.0 \| 191 \| 0.3346 \| 0.7975 \| \| 0.2689 \| 2.0 \| 382 \| 0.2900 \| 0.8347 \| \| 0.1821 \| 3.0 \| 573 \| 0.2651 \| 0.8355 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
abdusah/ft-tatoeba-ar-en	abdusah	2022-04-10T15:34:36Z	15	1	transformers	[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "dataset:open_subtitles", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-04-08T11:49:17Z	--- tags: - translation - generated_from_trainer datasets: - open_subtitles model-index: - name: ft-tatoeba-ar-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ft-tatoeba-ar-en This model was trained from scratch on the open_subtitles dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
V3RX2000/xlm-roberta-base-finetuned-panx-de-fr	V3RX2000	2022-04-10T15:31:08Z	2	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T12:45:09Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - F1: 0.8582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2885 \| 1.0 \| 715 \| 0.1817 \| 0.8287 \| \| 0.1497 \| 2.0 \| 1430 \| 0.1618 \| 0.8442 \| \| 0.0944 \| 3.0 \| 2145 \| 0.1667 \| 0.8582 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
V3RX2000/xlm-roberta-base-finetuned-panx-de	V3RX2000	2022-04-10T15:13:04Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T10:46:21Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8590909090909091 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1380 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2642 \| 1.0 \| 525 \| 0.1624 \| 0.8251 \| \| 0.1315 \| 2.0 \| 1050 \| 0.1445 \| 0.8508 \| \| 0.0832 \| 3.0 \| 1575 \| 0.1380 \| 0.8591 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
brad1141/baseline_gptv1	brad1141	2022-04-10T13:25:55Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T13:18:14Z	--- license: mit tags: - generated_from_trainer model-index: - name: baseline_gptv1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # baseline_gptv1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
brad1141/baseline_bertv3	brad1141	2022-04-10T13:16:14Z	8	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T13:09:44Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: baseline_bertv3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # baseline_bertv3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
laampt/distilbert-base-uncased-finetuned-squad	laampt	2022-04-10T13:15:06Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-10T13:05:51Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
brad1141/baseline_longformerv1	brad1141	2022-04-10T13:01:30Z	4	1	transformers	[ "transformers", "pytorch", "tensorboard", "longformer", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-10T12:37:35Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: baseline_longformerv1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # baseline_longformerv1 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7596 - Precision: 0.1333 - Recall: 0.15 - F1: 0.1400 - Accuracy: 0.1400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 1.8469 \| 0.89 \| 1 \| 1.7596 \| 0.1333 \| 0.15 \| 0.1400 \| 0.1400 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
V3RX2000/distilbert-base-uncased-finetuned-emotion	V3RX2000	2022-04-10T12:32:05Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-10T12:24:41Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9247142990809298 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2285 - Accuracy: 0.9245 - F1: 0.9247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8812 \| 1.0 \| 250 \| 0.3301 \| 0.906 \| 0.9035 \| \| 0.2547 \| 2.0 \| 500 \| 0.2285 \| 0.9245 \| 0.9247 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
malcolm/TSC_SentimentA_IMDBAmznTSC_2	malcolm	2022-04-10T09:43:32Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-10T07:59:27Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: TSC_SentimentA_IMDBAmznTSC_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TSC_SentimentA_IMDBAmznTSC_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1985 - Accuracy: 0.9365 - F1: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Pavithra/codeparrot-ds-sample-gpt-small-10epoch	Pavithra	2022-04-10T07:49:47Z	2	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-08T17:43:51Z	--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-gpt-small-10epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codeparrot-ds-sample-gpt-small-10epoch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 4.29 \| 0.94 \| 1000 \| 2.8452 \| \| 2.3155 \| 1.88 \| 2000 \| 2.3659 \| \| 1.8817 \| 2.82 \| 3000 \| 2.2085 \| \| 1.6245 \| 3.77 \| 4000 \| 2.1260 \| \| 1.4314 \| 4.71 \| 5000 \| 2.0705 \| \| 1.2698 \| 5.65 \| 6000 \| 2.0603 \| \| 1.1281 \| 6.59 \| 7000 \| 2.0599 \| \| 1.0108 \| 7.53 \| 8000 \| 2.0769 \| \| 0.9167 \| 8.47 \| 9000 \| 2.0870 \| \| 0.8551 \| 9.42 \| 10000 \| 2.0943 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
jaeyeon/wav2vec2-child-en-tokenizer-4	jaeyeon	2022-04-10T05:28:49Z	6	1	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-04-08T07:33:44Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-child-en-tokenizer-4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-child-en-tokenizer-4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4709 - Wer: 0.3769 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.0334 \| 1.72 \| 100 \| 1.4709 \| 0.3769 \| \| 0.0332 \| 3.45 \| 200 \| 1.4709 \| 0.3769 \| \| 0.0343 \| 5.17 \| 300 \| 1.4709 \| 0.3769 \| \| 0.032 \| 6.9 \| 400 \| 1.4709 \| 0.3769 \| \| 0.0332 \| 8.62 \| 500 \| 1.4709 \| 0.3769 \| \| 0.0327 \| 10.34 \| 600 \| 1.4709 \| 0.3769 \| \| 0.0331 \| 12.07 \| 700 \| 1.4709 \| 0.3769 \| \| 0.0334 \| 13.79 \| 800 \| 1.4709 \| 0.3769 \| \| 0.0319 \| 15.52 \| 900 \| 1.4709 \| 0.3769 \| \| 0.0338 \| 17.24 \| 1000 \| 1.4709 \| 0.3769 \| \| 0.0321 \| 18.97 \| 1100 \| 1.4709 \| 0.3769 \| \| 0.0367 \| 20.69 \| 1200 \| 1.4709 \| 0.3769 \| \| 0.0331 \| 22.41 \| 1300 \| 1.4709 \| 0.3769 \| \| 0.0332 \| 24.14 \| 1400 \| 1.4709 \| 0.3769 \| \| 0.0347 \| 25.86 \| 1500 \| 1.4709 \| 0.3769 \| \| 0.0319 \| 27.59 \| 1600 \| 1.4709 \| 0.3769 \| \| 0.0302 \| 29.31 \| 1700 \| 1.4709 \| 0.3769 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
mojians/E2E-QA-Mining	mojians	2022-04-10T02:34:53Z	22	3	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "question-generation", "question-answer mining", "dataset:squad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-01T16:03:34Z	--- datasets: - squad tags: - question-generation - question-answer mining widget: - text: "context: The English name 'Normans' comes from the French words Normans/Normanz, plural of Normant, modern French normand, which is itself borrowed from Old Low Franconian Nortmann 'Northman' or directly from Old Norse Norðmaðr, Latinized variously as Nortmannus, Normannus, or Nordmannus (recorded in Medieval Latin, 9th century) to mean 'Norseman, Viking'. generate questions and answers:" inference: parameters: min_length: 50 license: mit --- # Model name ## Model description This model mines the question-answer pairs from a given context in an end2end fashion. It takes a context as an input and generates a list of questions and answers as an output. It is based on a pre-trained `t5-small` model and uses a prompt enigneering technique to train. #### How to use The model takes the context (with prompt) as an input sequence and will generate question-answer pairs as an output sequence. The max sequence length is 512 tokens. Inputs should be organized into the following format: ``` context: context text here. generate questions and answers: ``` The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method. You can try out the demo in the [E2E-QA-mining space app](https://huggingface.co/spaces/mojians/E2E-QA-mining) #### Limitations and bias The model is limited to generating questions in the same style as those found in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), The generated questions can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context and answer do not match, the generated question is likely to be incoherent. ## Training data The model was fine-tuned on a dataset made up of several well-known QA datasets ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)） ## Source and Citation Please find our code and cite us in this repo [https://github.com/jian-mo/E2E-QA-Mining](https://github.com/jian-mo/E2E-QA-Mining)
huggingtweets/rusticgendarme	huggingtweets	2022-04-09T20:23:24Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/rusticgendarme/1649535793480/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1477404220685008896/bEbHFn3g_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">merz▫️▫️▫️▫️</div> <div style="text-align: center; font-size: 14px;">@rusticgendarme</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from merz▫️▫️▫️▫️. \| Data \| merz▫️▫️▫️▫️ \| \| --- \| --- \| \| Tweets downloaded \| 3220 \| \| Retweets \| 527 \| \| Short tweets \| 613 \| \| Tweets kept \| 2080 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yxv7eg1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rusticgendarme's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2eajj2bh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2eajj2bh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rusticgendarme') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Hodiden/autotrain-TestProj-722121991	Hodiden	2022-04-09T19:21:44Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "unk", "dataset:Hodiden/autotrain-data-TestProj", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-09T04:53:23Z	--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - Hodiden/autotrain-data-TestProj co2_eq_emissions: 8.052949236815056 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 722121991 - CO2 Emissions (in grams): 8.052949236815056 ## Validation Metrics - Loss: 1.123626708984375 - Rouge1: 56.1275 - Rouge2: 33.5648 - RougeL: 51.986 - RougeLsum: 51.9943 - Gen Len: 13.2823 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Hodiden/autotrain-TestProj-722121991 ```
alexjercan/codebert-base-buggy-token-classification	alexjercan	2022-04-09T16:00:35Z	7	2	transformers	[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-04T07:02:54Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: codebert-base-buggy-token-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codebert-base-buggy-token-classification This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5217 - Precision: 0.6942 - Recall: 0.0940 - F1: 0.1656 - Accuracy: 0.7714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
damlab/GO-language	damlab	2022-04-09T14:28:07Z	7	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "dataset:damlab/uniprot", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-08T18:26:38Z	--- license: mit datasets: - damlab/uniprot metrics: - accuracy widget: - text: 'involved_in GO:0006468 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372' example_title: 'Function' --- # GO-Language model ## Table of Contents - [Summary](#model-summary) - [Model Description](#model-description) - [Intended Uses & Limitations](#intended-uses-&-limitations) - [How to Use](#how-to-use) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Preprocessing](#preprocessing) - [Training](#training) - [Evaluation Results](#evaluation-results) - [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info) ## Summary This model was built as a way to encode the Gene Ontology definition of a protein as vector representation. It was trained on a collection of gene-ontology terms from model organisms. Each function was sorted by the ID number and combined with its annotation description ie (`is_a`, `enables`, `located_in`, etc). The model is tokenized such that each description and GO term is its own token. This is intended to be used as a translation model between PROT-BERT and GO-Language. That type of translation model will be useful for predicting the function of novel genes. ## Model Description This model was trained using the damlab/uniprot dataset on the `go` field with 256 token chunks and a 15% mask rate. ## Intended Uses & Limitations This model is a useful encapsulation of gene ontology functions. It allows both an exploration of gene-level similarities as well as comparisons between functional terms. ## How to use As this is a BERT-style Masked Language learner, it can be used to determine the most likely token a masked position. ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="damlab/GO-language") unmasker("involved_in [MASK] involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372") [{'score': 0.1040298342704773, 'token': 103, 'token_str': 'GO:0002250', 'sequence': 'involved_in GO:0002250 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.018045395612716675, 'token': 21, 'token_str': 'GO:0005576', 'sequence': 'involved_in GO:0005576 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.015035462565720081, 'token': 50, 'token_str': 'GO:0000139', 'sequence': 'involved_in GO:0000139 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.01181247178465128, 'token': 37, 'token_str': 'GO:0007165', 'sequence': 'involved_in GO:0007165 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.01000668853521347, 'token': 14, 'token_str': 'GO:0005737', 'sequence': 'involved_in GO:0005737 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'} ] ``` ## Training Data The dataset was trained using [damlab/uniprot](https://huggingface.co/datasets/damlab/uniprot) from a random initial model. The Gene Ontology functions were sorted (by ID number) along with annotating term. ## Training Procedure ### Preprocessing All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation. ### Training Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. ## BibTeX Entry and Citation Info [More Information Needed]
Saitomar/Fellowship-Challenge-CV	Saitomar	2022-04-09T14:01:46Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-04-09T10:36:13Z	# Fatima's Fellowship Challenge This card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program. - Epochs : 30 - Batch size : 32 - Learing rate : 0.0005 - Model : ResNet-50 - Optimizer : Adam - Dataset : CIFAR10
edwardjross/xlm-roberta-base-finetuned-recipe-all	edwardjross	2022-04-09T13:19:55Z	324	14	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "arxiv:2004.12184", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-08T14:01:31Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-recipe-all results: [] widget: - text: "1 sheet of frozen puff pastry (thawed)" - text: "1/2 teaspoon fresh thyme, minced" - text: "2-3 medium tomatoes" - text: "1 petit oignon rouge" --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-recipe-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the recipe ingredient [NER dataset](https://github.com/cosylabiiit/recipe-knowledge-mining) from the paper [A Named Entity Based Approach to Model Recipes](https://arxiv.org/abs/2004.12184) (using both the `gk` and `ar` datasets). It achieves the following results on the evaluation set: - Loss: 0.1169 - F1: 0.9672 On the test set it obtains an F1 of 0.9615, slightly above the CRF used in the paper. ## Model description Predicts tag of each token in an ingredient string. \| Tag \| Significance \| Example \| \| --- \| --- \| --- \| \| NAME \| Name of Ingredient \| salt, pepper \| \| STATE \| Processing State of Ingredient. \| ground, thawed \| \| UNIT \| Measuring unit(s). \| gram, cup \| \| QUANTITY \| Quantity associated with the unit(s). \| 1, 1 1/2 , 2-4 \| \| SIZE \| Portion sizes mentioned. \| small, large \| \| TEMP \| Temperature applied prior to cooking. \| hot, frozen \| \| DF (DRY/FRESH) \| Fresh otherwise as mentioned. \| dry, fresh \| ## Intended uses & limitations * Only trained on ingredient strings. * Tags subtokens; tag should be propagated to whole word * Works best with pre-tokenisation splitting of symbols (such as parentheses) and numbers (e.g. 50g -> 50 g) * Typically only detects the first ingredient if there are multiple. * Only trained on two American English data sources * Tags TEMP and DF have very few training data. ## Training and evaluation data Both the `ar` (AllRecipes.com) and `gk` (FOOD.com) datasets obtained from the TSVs from the authors' [repository](https://github.com/cosylabiiit/recipe-knowledge-mining). ## Training procedure It follows the overall procedure from Chapter 4 of [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098103231/) by Tunstall, von Wera and Wolf. See the [training notebook](https://github.com/EdwardJRoss/nlp_transformers_exercises/blob/master/notebooks/ch4-ner-recipe-stanford-crf.ipynb) for details. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2529 \| 1.0 \| 331 \| 0.1303 \| 0.9592 \| \| 0.1164 \| 2.0 \| 662 \| 0.1224 \| 0.9640 \| \| 0.0904 \| 3.0 \| 993 \| 0.1156 \| 0.9671 \| \| 0.0585 \| 4.0 \| 1324 \| 0.1169 \| 0.9672 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
Yu-Ping/BERT-Fake_News_Classifier	Yu-Ping	2022-04-09T13:06:21Z	0	0	null	[ "bert-base-cased", "text classifier", "PyTorch", "license:apache-2.0", "model-index", "region:us" ]	null	2022-04-09T07:44:23Z	--- language: - en - TW thumbnail: https://colab.research.google.com/drive/1L3PvqNjMF-K_ykztrNEqKhky279EcPaN?usp=sharing tags: - bert-base-cased - text classifier - PyTorch license: apache-2.0 datasets: - True.csv (downloaded from https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) - Fake.csv (downloaded from https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) metrics: - accuracy - auc model-index: - name: bert-base-cased results: - task: type: fake-news-classifier name: Text Classification dataset: type: news name: Fake and real news metrics: - type: accuracy value: 90.92% ---
tau/tavbert-tr	tau	2022-04-09T12:55:55Z	4	1	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "language model", "tr", "dataset:oscar", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-04-09T12:52:34Z	--- language: tr tags: - roberta - language model datasets: - oscar --- # TavBERT base model A Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020). ### How to use ```python import numpy as np import torch from transformers import AutoModelForMaskedLM, AutoTokenizer model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-tr") tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-tr") def mask_sentence(sent, span_len=5): start_pos = np.random.randint(0, len(sent) - span_len) masked_sent = sent[:start_pos] + '[MASK]' * span_len + sent[start_pos + span_len:] print("Masked sentence:", masked_sent) output = model(**tokenizer.encode_plus(masked_sent, return_tensors='pt'))['logits'][0][1:-1] preds = [int(x) for x in torch.argmax(torch.softmax(output, axis=1), axis=1)[start_pos:start_pos + span_len]] pred_sent = sent[:start_pos] + ''.join(tokenizer.convert_ids_to_tokens(preds)) + sent[start_pos + span_len:] print("Model's prediction:", pred_sent) ``` ## Training data OSCAR (Ortiz, 2019) Turkish section (27 GB text, 77 million sentences).
huggingtweets/notsorobot	huggingtweets	2022-04-09T12:41:46Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-08T16:11:26Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbBT6WF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">3bkreno</div> <div style="text-align: center; font-size: 14px;">@notsorob</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 3bkreno. \| Data \| 3bkreno \| \| --- \| --- \| \| Tweets downloaded \| 26419 \| \| Retweets \| 111 \| \| Short tweets \| -8796 \| \| Tweets kept \| 8796 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1l7p1yze/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @notsorob's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ypaq5o5y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ypaq5o5y/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/notsorob') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
jicoc22578/autotrain-livedoor_news-722922024	jicoc22578	2022-04-09T10:47:55Z	3	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "ja", "dataset:jicoc22578/autotrain-data-livedoor_news", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-09T10:33:57Z	--- tags: autotrain language: ja widget: - text: "Windows 11搭載PCを買ったら最低限やっておきたいこと" - text: "3月デスクトップOSシェア、Windowsが増加しMacが減少" - text: "raytrek、Core i7-12700HとRTX 3070 Tiを搭載するノートPC" datasets: - jicoc22578/autotrain-data-livedoor_news co2_eq_emissions: 0.019299491458156143 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 722922024 - CO2 Emissions (in grams): 0.019299491458156143 ## Validation Metrics - Loss: 0.19609540700912476 - Accuracy: 0.9457627118644067 - Macro F1: 0.9404319054946133 - Micro F1: 0.9457627118644067 - Weighted F1: 0.9456037443251943 - Macro Precision: 0.9420917371721244 - Micro Precision: 0.9457627118644067 - Weighted Precision: 0.9457910238180336 - Macro Recall: 0.9391783746329772 - Micro Recall: 0.9457627118644067 - Weighted Recall: 0.9457627118644067 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/jicoc22578/autotrain-livedoor_news-722922024 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
gdwangh/distilbert-base-uncased-finetuned-cola	gdwangh	2022-04-09T10:39:17Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-31T14:34:17Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5197669430092784 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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: 0.6532 - Matthews Correlation: 0.5198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5228 \| 1.0 \| 535 \| 0.5270 \| 0.4212 \| \| 0.3448 \| 2.0 \| 1070 \| 0.5360 \| 0.5073 \| \| 0.2305 \| 3.0 \| 1605 \| 0.6532 \| 0.5198 \| \| 0.1691 \| 4.0 \| 2140 \| 0.7934 \| 0.5171 \| \| 0.128 \| 5.0 \| 2675 \| 0.8732 \| 0.5166 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-3	Chikashi	2022-04-09T08:34:39Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-08T23:02:19Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 26.7383 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-wikihow_3epoch_b4_lr3e-3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3400 - Rouge1: 26.7383 - Rouge2: 10.1981 - Rougel: 22.8642 - Rougelsum: 26.0922 - Gen Len: 18.524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 3.2548 \| 0.13 \| 5000 \| 2.9708 \| 22.0519 \| 6.7142 \| 18.7677 \| 21.4627 \| 17.9546 \| \| 3.1153 \| 0.25 \| 10000 \| 2.9099 \| 20.2838 \| 5.8365 \| 17.5009 \| 19.7112 \| 18.4981 \| \| 3.0478 \| 0.38 \| 15000 \| 2.8763 \| 22.8282 \| 7.3649 \| 19.6843 \| 22.2312 \| 18.1331 \| \| 3.0146 \| 0.51 \| 20000 \| 2.8484 \| 23.2465 \| 7.4295 \| 19.621 \| 22.6246 \| 18.5115 \| \| 2.9572 \| 0.64 \| 25000 \| 2.7902 \| 23.8681 \| 7.9617 \| 20.4984 \| 23.2066 \| 18.5544 \| \| 2.9425 \| 0.76 \| 30000 \| 2.7577 \| 23.4402 \| 7.5289 \| 19.7382 \| 22.7941 \| 18.4613 \| \| 2.9075 \| 0.89 \| 35000 \| 2.7343 \| 23.0082 \| 7.5408 \| 19.8426 \| 22.3832 \| 18.1218 \| \| 2.8705 \| 1.02 \| 40000 \| 2.7136 \| 23.9492 \| 7.8861 \| 20.3675 \| 23.3035 \| 18.4869 \| \| 2.7967 \| 1.14 \| 45000 \| 2.6923 \| 24.2394 \| 8.2895 \| 20.7275 \| 23.6127 \| 18.3486 \| \| 2.7794 \| 1.27 \| 50000 \| 2.6639 \| 24.4062 \| 8.2481 \| 20.8957 \| 23.8077 \| 18.4258 \| \| 2.7776 \| 1.4 \| 55000 \| 2.6321 \| 24.6213 \| 8.4161 \| 21.0528 \| 23.968 \| 18.351 \| \| 2.7397 \| 1.53 \| 60000 \| 2.6116 \| 24.16 \| 8.3605 \| 20.618 \| 23.5037 \| 18.6049 \| \| 2.7199 \| 1.65 \| 65000 \| 2.5846 \| 24.2606 \| 8.3829 \| 20.6274 \| 23.6252 \| 18.4742 \| \| 2.7044 \| 1.78 \| 70000 \| 2.5663 \| 25.0452 \| 8.896 \| 21.4554 \| 24.4748 \| 18.3143 \| \| 2.6928 \| 1.91 \| 75000 \| 2.5365 \| 25.1312 \| 9.008 \| 21.6376 \| 24.4963 \| 18.5605 \| \| 2.6281 \| 2.03 \| 80000 \| 2.5209 \| 25.5311 \| 9.1521 \| 21.729 \| 24.8864 \| 18.2597 \| \| 2.5333 \| 2.16 \| 85000 \| 2.4860 \| 25.4834 \| 9.2969 \| 21.7257 \| 24.8802 \| 18.3831 \| \| 2.5308 \| 2.29 \| 90000 \| 2.4619 \| 26.0526 \| 9.605 \| 22.2178 \| 25.4353 \| 18.4235 \| \| 2.5136 \| 2.42 \| 95000 \| 2.4356 \| 25.9434 \| 9.6537 \| 22.2957 \| 25.312 \| 18.4647 \| \| 2.4801 \| 2.54 \| 100000 \| 2.4098 \| 26.1109 \| 9.7637 \| 22.3844 \| 25.4771 \| 18.5765 \| \| 2.4494 \| 2.67 \| 105000 \| 2.3835 \| 26.332 \| 9.9472 \| 22.4243 \| 25.6933 \| 18.5985 \| \| 2.4393 \| 2.8 \| 110000 \| 2.3590 \| 26.6896 \| 10.2248 \| 22.8743 \| 26.0665 \| 18.4883 \| \| 2.4071 \| 2.93 \| 115000 \| 2.3400 \| 26.7383 \| 10.1981 \| 22.8642 \| 26.0922 \| 18.524 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
nielsr/segformer-test-v6	nielsr	2022-04-09T08:21:01Z	8	0	transformers	[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "dataset:segments/sidewalk-semantic", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-segmentation	2022-04-09T07:53:39Z	--- license: apache-2.0 tags: - vision - image-segmentation datasets: - segments/sidewalk-semantic widget: - src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg example_title: Brugge ---
nikhedward/bart-large-cnn-finetuned-multi-news1	nikhedward	2022-04-09T04:51:07Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:multi_news", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-09T02:56:22Z	--- license: mit tags: - generated_from_trainer datasets: - multi_news metrics: - rouge model-index: - name: bart-large-cnn-finetuned-multi-news1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: multi_news type: multi_news args: default metrics: - name: Rouge1 type: rouge value: 42.1215 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-cnn-finetuned-multi-news1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0858 - Rouge1: 42.1215 - Rouge2: 14.9986 - Rougel: 23.4737 - Rougelsum: 36.4212 - Gen Len: 133.703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.1984 \| 1.0 \| 750 \| 2.0858 \| 42.1215 \| 14.9986 \| 23.4737 \| 36.4212 \| 133.703 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
avialfont/dummy-finetuned-amazon-en-es	avialfont	2022-04-09T03:35:50Z	4	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-08T15:20:54Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: avialfont/dummy-finetuned-amazon-en-es results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # avialfont/dummy-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.6755 - Validation Loss: 3.8033 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 3627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.2942 \| 4.4915 \| 0 \| \| 6.2878 \| 3.9207 \| 1 \| \| 5.6755 \| 3.8033 \| 2 \| ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.6
MeerAnwar/CodingChallengeFatimaFellowship	MeerAnwar	2022-04-09T01:35:28Z	0	0	null	[ "region:us" ]	null	2022-04-08T14:35:45Z	# 1. Deep Learning for Vision </p> Upside down detector: Train a model to detect if images are upside down * Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub) * Synthetically turn some of the images upside down. Create a training and test set. * Build a neural network (using TensorFlow, PyTorch, or any framework you like) * Train it to classify image orientation until a reasonable accuracy is reached * Upload the model to the Hugging Face Hub, and add a link to your model below. * Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model performance on these images in the future (you do not need to implement these suggestions)
nateraw/test-save-keras-sequential	nateraw	2022-04-08T20:15:54Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-04-08T19:07:35Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
lysandre/test-save-keras-sequential	lysandre	2022-04-08T20:01:39Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-04-08T19:32:35Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed
bmichele/poetry-generation-nextline-mbart-gut-en-single	bmichele	2022-04-08T19:13:43Z	4	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-08T18:46:39Z	# poetry-generation-nextline-mbart-gut-en-single * `nextline`: generates a poem line from previous line(s) * `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) * `gut`: trained on Project Gutenberg data * `en`: English language * `single`: uses only last poem line as input for generation
nateraw/autoencoder-keras-mnist-demo-new	nateraw	2022-04-08T18:37:12Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2022-04-08T18:37:04Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
krinal214/augmented_Squad_Translated	krinal214	2022-04-08T18:15:59Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-08T15:58:32Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: augmented_Squad_Translated results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # augmented_Squad_Translated This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.1154 \| 1.0 \| 10835 \| 0.5251 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6
MaRiOrOsSi/t5-base-finetuned-question-answering	MaRiOrOsSi	2022-04-08T18:00:14Z	1,273	32	transformers	[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "Generative Question Answering", "en", "dataset:duorc", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-04-08T07:36:44Z	--- language: en datasets: - duorc widget: - text: "question: Is Giacomo Italian? context: Giacomo is 25 years old and he was born in Tuscany" - text: "question: Where does Christian come from? context: Christian is a student of UNISI but he come from Caserta" - text: "question: Is the dog coat grey? context: You have a beautiful dog with a brown coat" tags: - Generative Question Answering --- # T5 for Generative Question Answering This model is the result produced by Christian Di Maio and Giacomo Nunziati for the Language Processing Technologies exam. Reference for [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [DuoRC](https://huggingface.co/datasets/duorc) for Generative Question Answering by just prepending the question to the context. ## Code The code used for T5 training is available at this [repository](https://github.com/nunziati/bert-vs-t5-for-question-answering/blob/main/train_t5_selfrc.py). ## Results The results are evaluated on: - DuoRC/SelfRC -> Test Subset - DuoRC/ParaphraseRC -> Test Subset - SQUADv1 -> Validation Subset Removing all tokens not related to dictionary words from the evaluation metrics. The model used as reference is BERT finetuned on SQUAD v1. \| Model \| SelfRC \| ParaphraseRC \| SQUAD \|--\|--\|--\|--\| \| T5-BASE-FINETUNED \| F1: 49.00 EM: 31.38 \| F1: 28.75 EM: 15.18 \| F1: 63.28 EM: 37.24 \| \| BERT-BASE-FINETUNED \| F1: 47.18 EM: 30.76 \| F1: 21.20 EM: 12.62 \| F1: 77.19 EM: 57.81 \| ## How to use it 🚀 ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelWithLMHead.from_pretrained(model_name) question = "What is 42?" context = "42 is the answer to life, the universe and everything" input = f"question: {question} context: {context}" encoded_input = tokenizer([input], return_tensors='pt', max_length=512, truncation=True) output = model.generate(input_ids = encoded_input.input_ids, attention_mask = encoded_input.attention_mask) output = tokenizer.decode(output[0], skip_special_tokens=True) print(output) ``` ## Citation Created by [Christian Di Maio](https://it.linkedin.com/in/christiandimaio) and [Giacomo Nunziati](https://it.linkedin.com/in/giacomo-nunziati-b19572185) > Made with <span style="color: #e25555;">&hearts;</span> in Italy
caush/TestMeanFraction2	caush	2022-04-08T17:51:14Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T17:26:33Z	--- license: mit tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: TestMeanFraction2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TestMeanFraction2 This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3967 - Matthews Correlation: 0.2537 ## Model description More information needed ## Intended uses & limitations "La panique totale" Cette femme trouve une énorme araignée suspendue à sa douche. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 0.13 \| 50 \| 1.1126 \| 0.1589 \| \| No log \| 0.25 \| 100 \| 1.0540 \| 0.1884 \| \| No log \| 0.38 \| 150 \| 1.1533 \| 0.0818 \| \| No log \| 0.51 \| 200 \| 1.0676 \| 0.1586 \| \| No log \| 0.64 \| 250 \| 0.9949 \| 0.2280 \| \| No log \| 0.76 \| 300 \| 1.0343 \| 0.2629 \| \| No log \| 0.89 \| 350 \| 1.0203 \| 0.2478 \| \| No log \| 1.02 \| 400 \| 1.0041 \| 0.2752 \| \| No log \| 1.15 \| 450 \| 1.0808 \| 0.2256 \| \| 1.023 \| 1.27 \| 500 \| 1.0029 \| 0.2532 \| \| 1.023 \| 1.4 \| 550 \| 1.0204 \| 0.2508 \| \| 1.023 \| 1.53 \| 600 \| 1.1377 \| 0.1689 \| \| 1.023 \| 1.65 \| 650 \| 1.0499 \| 0.2926 \| \| 1.023 \| 1.78 \| 700 \| 1.0441 \| 0.2474 \| \| 1.023 \| 1.91 \| 750 \| 1.0279 \| 0.2611 \| \| 1.023 \| 2.04 \| 800 \| 1.1511 \| 0.2804 \| \| 1.023 \| 2.16 \| 850 \| 1.2381 \| 0.2512 \| \| 1.023 \| 2.29 \| 900 \| 1.3340 \| 0.2385 \| \| 1.023 \| 2.42 \| 950 \| 1.4372 \| 0.2842 \| \| 0.7325 \| 2.54 \| 1000 \| 1.3967 \| 0.2537 \| \| 0.7325 \| 2.67 \| 1050 \| 1.4272 \| 0.2624 \| \| 0.7325 \| 2.8 \| 1100 \| 1.3869 \| 0.1941 \| \| 0.7325 \| 2.93 \| 1150 \| 1.4983 \| 0.2063 \| \| 0.7325 \| 3.05 \| 1200 \| 1.4959 \| 0.2409 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0a0+0aef44c - Datasets 2.0.0 - Tokenizers 0.11.6
leonadase/bert-base-chinese-finetuned-ner-v1	leonadase	2022-04-08T17:49:01Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fdner", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-08T13:26:02Z	--- tags: - generated_from_trainer datasets: - fdner metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-chinese-finetuned-ner-v1 results: - task: name: Token Classification type: token-classification dataset: name: fdner type: fdner args: fdner metrics: - name: Precision type: precision value: 0.981203007518797 - name: Recall type: recall value: 0.9886363636363636 - name: F1 type: f1 value: 0.9849056603773584 - name: Accuracy type: accuracy value: 0.9909536373916321 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-chinese-finetuned-ner-v1 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset. It achieves the following results on the evaluation set: - Loss: 0.0413 - Precision: 0.9812 - Recall: 0.9886 - F1: 0.9849 - Accuracy: 0.9910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 8 \| 2.0640 \| 0.0 \| 0.0 \| 0.0 \| 0.4323 \| \| No log \| 2.0 \| 16 \| 1.7416 \| 0.0204 \| 0.0227 \| 0.0215 \| 0.5123 \| \| No log \| 3.0 \| 24 \| 1.5228 \| 0.0306 \| 0.0265 \| 0.0284 \| 0.5456 \| \| No log \| 4.0 \| 32 \| 1.2597 \| 0.0961 \| 0.1591 \| 0.1198 \| 0.6491 \| \| No log \| 5.0 \| 40 \| 1.0273 \| 0.1588 \| 0.2159 \| 0.1830 \| 0.7450 \| \| No log \| 6.0 \| 48 \| 0.8026 \| 0.2713 \| 0.3258 \| 0.2960 \| 0.8208 \| \| No log \| 7.0 \| 56 \| 0.6547 \| 0.36 \| 0.4091 \| 0.3830 \| 0.8513 \| \| No log \| 8.0 \| 64 \| 0.5180 \| 0.4650 \| 0.5038 \| 0.4836 \| 0.8873 \| \| No log \| 9.0 \| 72 \| 0.4318 \| 0.5139 \| 0.5606 \| 0.5362 \| 0.9067 \| \| No log \| 10.0 \| 80 \| 0.3511 \| 0.6169 \| 0.6894 \| 0.6512 \| 0.9291 \| \| No log \| 11.0 \| 88 \| 0.2887 \| 0.6691 \| 0.6894 \| 0.6791 \| 0.9414 \| \| No log \| 12.0 \| 96 \| 0.2396 \| 0.7042 \| 0.7576 \| 0.7299 \| 0.9516 \| \| No log \| 13.0 \| 104 \| 0.2052 \| 0.7568 \| 0.8371 \| 0.7950 \| 0.9587 \| \| No log \| 14.0 \| 112 \| 0.1751 \| 0.8303 \| 0.8712 \| 0.8503 \| 0.9610 \| \| No log \| 15.0 \| 120 \| 0.1512 \| 0.8464 \| 0.8977 \| 0.8713 \| 0.9668 \| \| No log \| 16.0 \| 128 \| 0.1338 \| 0.8759 \| 0.9091 \| 0.8922 \| 0.9710 \| \| No log \| 17.0 \| 136 \| 0.1147 \| 0.8959 \| 0.9129 \| 0.9043 \| 0.9746 \| \| No log \| 18.0 \| 144 \| 0.1011 \| 0.9326 \| 0.9432 \| 0.9379 \| 0.9761 \| \| No log \| 19.0 \| 152 \| 0.0902 \| 0.9251 \| 0.9356 \| 0.9303 \| 0.9795 \| \| No log \| 20.0 \| 160 \| 0.0806 \| 0.9440 \| 0.9583 \| 0.9511 \| 0.9804 \| \| No log \| 21.0 \| 168 \| 0.0743 \| 0.9586 \| 0.9659 \| 0.9623 \| 0.9812 \| \| No log \| 22.0 \| 176 \| 0.0649 \| 0.9511 \| 0.9583 \| 0.9547 \| 0.9851 \| \| No log \| 23.0 \| 184 \| 0.0595 \| 0.9591 \| 0.9773 \| 0.9681 \| 0.9876 \| \| No log \| 24.0 \| 192 \| 0.0537 \| 0.9625 \| 0.9735 \| 0.9680 \| 0.9883 \| \| No log \| 25.0 \| 200 \| 0.0505 \| 0.9701 \| 0.9848 \| 0.9774 \| 0.9894 \| \| No log \| 26.0 \| 208 \| 0.0464 \| 0.9737 \| 0.9811 \| 0.9774 \| 0.9904 \| \| No log \| 27.0 \| 216 \| 0.0439 \| 0.9737 \| 0.9811 \| 0.9774 \| 0.9906 \| \| No log \| 28.0 \| 224 \| 0.0428 \| 0.9812 \| 0.9886 \| 0.9849 \| 0.9910 \| \| No log \| 29.0 \| 232 \| 0.0417 \| 0.9812 \| 0.9886 \| 0.9849 \| 0.9910 \| \| No log \| 30.0 \| 240 \| 0.0413 \| 0.9812 \| 0.9886 \| 0.9849 \| 0.9910 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
malcolm/TSC_finetuning-sentiment-movie-model	malcolm	2022-04-08T16:44:47Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T14:33:40Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: TSC_finetuning-sentiment-movie-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # TSC_finetuning-sentiment-movie-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1480 - Accuracy: 0.9578 - F1: 0.9757 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
huggingtweets/lilpeeplyric	huggingtweets	2022-04-08T15:15:13Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-08T15:14:31Z	--- language: en thumbnail: http://www.huggingtweets.com/lilpeeplyric/1649430909105/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1445263525878902787/yW8p2-e__400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">lil peep lyrics bot</div> <div style="text-align: center; font-size: 14px;">@lilpeeplyric</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from lil peep lyrics bot. \| Data \| lil peep lyrics bot \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 0 \| \| Short tweets \| 0 \| \| Tweets kept \| 3250 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jgq3lf6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lilpeeplyric's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1lbjza1d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1lbjza1d/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/lilpeeplyric') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
nielsr/segformer-test-v5	nielsr	2022-04-08T15:05:50Z	6	0	transformers	[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "dataset:segments/sidewalk-semantic", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-segmentation	2022-04-08T14:51:17Z	--- license: apache-2.0 tags: - vision - image-segmentation datasets: - segments/sidewalk-semantic widget: - src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg example_title: Brugge ---
xaqren/sentiment_analysis	xaqren	2022-04-08T14:59:55Z	9	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "exbert", "en", "dataset:Confidential", "arxiv:1810.04805", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-05T13:46:58Z	--- language: en tags: - exbert license: apache-2.0 datasets: - Confidential --- # BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model description [xaqren/sentiment_analysis] This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.
iceboy95/SqueezeNet_VisionQ1_20220512	iceboy95	2022-04-08T14:15:01Z	0	0	null	[ "license:afl-3.0", "region:us" ]	null	2022-04-08T14:09:42Z	--- license: afl-3.0 --- ## Description SqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle https://www.kaggle.com/datasets/arnaud58/landscape-pictures ## Results Trained with 8K samples, tested with 120++ non-overlapping samples. Accuracy: 0.978261 f1-score: 0.978417
philschmid/MiniLMv2-L6-H384-sst2	philschmid	2022-04-08T13:56:53Z	4	1	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T13:54:14Z	--- tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: MiniLMv2-L6-H384-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9197247706422018 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MiniLMv2-L6-H384-sst2 This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2532 - Accuracy: 0.9197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5787 \| 1.0 \| 264 \| 0.3496 \| 0.8624 \| \| 0.3413 \| 2.0 \| 528 \| 0.2599 \| 0.8991 \| \| 0.2716 \| 3.0 \| 792 \| 0.2651 \| 0.9048 \| \| 0.2343 \| 4.0 \| 1056 \| 0.2532 \| 0.9197 \| \| 0.2165 \| 5.0 \| 1320 \| 0.2636 \| 0.9151 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
profoz/mlops-demo	profoz	2022-04-08T13:56:10Z	8	1	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "classification", "sequence-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en tags: - classification - sequence-classification license: apache-2.0 --- Github repository [here](https://github.com/sinanuozdemir/oreilly-transformers-nlp)
gymball/FatimaFellowship-UpsideDown	gymball	2022-04-08T12:18:42Z	0	0	null	[ "Image Classification", "en", "dataset:cifar100", "license:unlicense", "region:us" ]	null	2022-04-07T16:18:53Z	--- language: - en tags: - Image Classification license: unlicense datasets: - cifar100 --- This repo contains a model that is capable of detecting upside images. This is part of my submission for the Fatima Fellowship Selection Task.
srmukundb/distilbert-base-uncased-finetuned-squad	srmukundb	2022-04-08T12:08:02Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-08T00:45:45Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2182 \| 1.0 \| 8235 \| 1.2318 \| \| 0.9451 \| 2.0 \| 16470 \| 1.2693 \| \| 0.7554 \| 3.0 \| 24705 \| 1.4104 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
ybelkada/japanese-roberta-question-answering	ybelkada	2022-04-08T11:38:39Z	171	1	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "extractive-qa", "ja", "dataset:SkelterLabsInc/JaQuAD", "license:cc-by-sa-3.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-08T08:52:22Z	--- license: cc-by-sa-3.0 language: ja tags: - question-answering - extractive-qa pipeline_tag: - None datasets: - SkelterLabsInc/JaQuAD metrics: - Exact match - F1 score --- # RoBERTa base Japanese - JaQuAD ## Description A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD). Please refer [RoBERTa base Japanese](https://huggingface.co/rinna/japanese-roberta-base) for details about the pre-training model. The codes for the fine-tuning are available [on this notebook](https://huggingface.co/ybelkada/japanese-roberta-question-answering/blob/main/roberta_ja_qa.ipynb) ## Usage ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer question = 'アレクサンダー・グラハム・ベルは、どこで生まれたの?' context = 'アレクサンダー・グラハム・ベルは、スコットランド生まれの科学者、発明家、工学者である。世界初の>実用的電話の発明で知られている。' model = AutoModelForQuestionAnswering.from_pretrained( 'ybelkada/japanese-roberta-question-answering') tokenizer = AutoTokenizer.from_pretrained( 'ybelkada/japanese-roberta-question-answering') inputs = tokenizer( question, context, add_special_tokens=True, return_tensors="pt") input_ids = inputs["input_ids"].tolist()[0] outputs = model(**inputs) answer_start_scores = outputs.start_logits answer_end_scores = outputs.end_logits # Get the most likely beginning of answer with the argmax of the score. answer_start = torch.argmax(answer_start_scores) # Get the most likely end of answer with the argmax of the score. # 1 is added to `answer_end` because the index pointed by score is inclusive. answer_end = torch.argmax(answer_end_scores) + 1 answer = tokenizer.convert_tokens_to_string( tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])) # answer = 'スコットランド' ``` ## License The fine-tuned model is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ## Miscellaneous The Q&A widget does not work on that model. Tried also with ```Pipeline``` and I was able to reproduce the error, needs a further investigation
huggingtweets/emarobot	huggingtweets	2022-04-08T11:13:49Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-04-08T11:12:40Z	--- language: en thumbnail: http://www.huggingtweets.com/emarobot/1649416424059/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbBT6WF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">3bkreno</div> <div style="text-align: center; font-size: 14px;">@emarobot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 3bkreno. \| Data \| 3bkreno \| \| --- \| --- \| \| Tweets downloaded \| 970 \| \| Retweets \| 111 \| \| Short tweets \| 129 \| \| Tweets kept \| 841 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mfd65acm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @emarobot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1i5j7avt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1i5j7avt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/emarobot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten	marcosfp	2022-04-08T11:10:02Z	11	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T10:59:03Z	--- license: gpl-3.0 --- Objectivity sentence classification model based on distilbert-base-uncased-finetuned-sst-2-english. It was fine-tuned with Rotten-IMDB movie review [data](http://www.cs.cornell.edu/people/pabo/movie-review-data/) using extracted sentences from film plots as objective examples and review comments as subjective language examples. With a test set of 5%, we obtained an accuracy of 96% and f1 of the same value. Please, feel free to try the demo online with subjective language examples like "I think...", "I believe...", and more objective claims. For any further comments contact me, at [email protected].
afbudiman/distilled-indobert-classification	afbudiman	2022-04-08T09:32:57Z	9	1	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:indonlu", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T06:49:31Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9015873015873016 - name: F1 type: f1 value: 0.9014926755197933 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilled-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.9016 - F1: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0427 \| 1.0 \| 688 \| 0.6306 \| 0.8683 \| 0.8684 \| \| 0.5332 \| 2.0 \| 1376 \| 0.5621 \| 0.8794 \| 0.8779 \| \| 0.3021 \| 3.0 \| 2064 \| 0.6785 \| 0.8905 \| 0.8896 \| \| 0.1851 \| 4.0 \| 2752 \| 0.6085 \| 0.8968 \| 0.8959 \| \| 0.1152 \| 5.0 \| 3440 \| 0.6015 \| 0.9016 \| 0.9015 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
btjiong/robbert-twitter-sentiment-custom	btjiong	2022-04-08T08:17:25Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:dutch_social", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-07T18:07:19Z	--- license: mit tags: - generated_from_trainer datasets: - dutch_social metrics: - accuracy - f1 - precision - recall model-index: - name: robbert-twitter-sentiment-custom results: - task: name: Text Classification type: text-classification dataset: name: dutch_social type: dutch_social args: dutch_social metrics: - name: Accuracy type: accuracy value: 0.788 - name: F1 type: f1 value: 0.7878005279207152 - name: Precision type: precision value: 0.7877102066609215 - name: Recall type: recall value: 0.788 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robbert-twitter-sentiment-custom This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.6656 - Accuracy: 0.788 - F1: 0.7878 - Precision: 0.7877 - Recall: 0.788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.8287 \| 1.0 \| 282 \| 0.7178 \| 0.7007 \| 0.6958 \| 0.6973 \| 0.7007 \| \| 0.4339 \| 2.0 \| 564 \| 0.5873 \| 0.7667 \| 0.7668 \| 0.7681 \| 0.7667 \| \| 0.2045 \| 3.0 \| 846 \| 0.6656 \| 0.788 \| 0.7878 \| 0.7877 \| 0.788 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6
philschmid/roberta-large-sst2	philschmid	2022-04-08T08:03:59Z	148	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-04-08T07:27:49Z	--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: roberta-large-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9644495412844036 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-sst2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.1400 - Accuracy: 0.9644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3688 \| 1.0 \| 264 \| 0.1444 \| 0.9564 \| \| 0.1529 \| 2.0 \| 528 \| 0.1502 \| 0.9518 \| \| 0.107 \| 3.0 \| 792 \| 0.1388 \| 0.9530 \| \| 0.0666 \| 4.0 \| 1056 \| 0.1400 \| 0.9644 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6
sail/poolformer_s24	sail	2022-04-08T07:48:50Z	137	1	transformers	[ "transformers", "pytorch", "poolformer", "image-classification", "vision", "dataset:imagenet", "arxiv:2111.11418", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # PoolFormer (S24 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_s24') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s24') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results \| Model \| ImageNet top-1 accuracy \| # params \| URL \| \|---------------------------------------\|-------------------------\|----------\|------------------------------------------------------------------\| \| PoolFormer-S12 \| 77.2 \| 12M \| https://huggingface.co/sail/poolformer_s12 \| \| PoolFormer-S24 \| 80.3 \| 21M \| https://huggingface.co/sail/poolformer_s24** \| \| PoolFormer-S36 \| 81.4 \| 31M \| https://huggingface.co/sail/poolformer_s36 \| \| PoolFormer-M36 \| 82.1 \| 56M \| https://huggingface.co/sail/poolformer_m36 \| \| PoolFormer-M48 \| 82.5 \| 73M \| https://huggingface.co/sail/poolformer_m48 \| ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```

mekondjo/distilbert-base-uncased-finetuned-emotion

mekondjo

2022-04-12T15:53:40Z

7

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-12T15:39:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9248167911304236 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2219 - Accuracy: 0.9245 - F1: 0.9248 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.848 | 1.0 | 250 | 0.3157 | 0.9075 | 0.9059 | | 0.253 | 2.0 | 500 | 0.2219 | 0.9245 | 0.9248 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

surdan/LaBSE_ner_nerel

surdan

2022-04-12T13:17:34Z

1,192

10

transformers

[ "transformers", "pytorch", "bert", "token-classification", "ru", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-11T14:45:16Z

--- language: ["ru", "en"] tasks: - token-classification --- ## About model This model based on [cointegrated/LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru). And trained on [surdan/nerel_short](https://huggingface.co/datasets/surdan/nerel_short) dataset You can find more info: - How the model was trained [Train_model.ipynb](https://huggingface.co/surdan/LaBSE_ner_nerel/blob/main/Train_model.ipynb) - Example of usage model [Inference.ipynb](https://huggingface.co/surdan/LaBSE_ner_nerel/blob/main/Inference.ipynb)

luckydog/distilbert-base-uncased-finetuned-emotion

luckydog

2022-04-12T12:36:17Z

10

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-12T02:41:30Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9 - name: F1 type: f1 value: 0.8980758869010411 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3298 - Accuracy: 0.9 - F1: 0.8981 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.2761 | 1.0 | 250 | 0.6036 | 0.814 | 0.7881 | | 0.4081 | 2.0 | 500 | 0.3298 | 0.9 | 0.8981 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3

TovaHasi/Toyota_material_calculator_reach_trucks

TovaHasi

2022-04-12T12:33:49Z

0

null

[ "license:unlicense", "region:us" ]

null

2022-04-12T12:22:50Z

--- app_file: app.py license: unlicense ---

lewtun/roberta-large-finetuned-clinc-123

lewtun

2022-04-12T12:05:51Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-12T12:00:35Z

--- license: mit tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: roberta-large-finetuned-clinc-123 results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.925483870967742 ---  # roberta-large-finetuned-clinc-123 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7226 - Accuracy: 0.9255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.0576 | 1.0 | 120 | 5.0269 | 0.0068 | | 4.5101 | 2.0 | 240 | 2.9324 | 0.7158 | | 1.9757 | 3.0 | 360 | 0.7226 | 0.9255 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

Chris1/real2sim

Chris1

2022-04-12T11:33:32Z

0

null

[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]

null

2022-04-12T11:33:27Z

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

Chris1/ape2punk_epoch80

Chris1

2022-04-12T11:21:48Z

0

null

[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]

null

2022-04-12T11:21:43Z

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- # MyModelName ## Model description Describe the model here (what it does, what it's used for, etc.) ## Intended uses & limitations #### How to use ```python # You can include sample code which will be formatted ``` #### Limitations and bias Provide examples of latent issues and potential remediations. ## Training data Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data. ## Training procedure Preprocessing, hardware used, hyperparameters... ## Eval results ## Generated Images You can embed local or remote images using `![](...)` ### BibTeX entry and citation info ```bibtex @inproceedings{..., year={2020} } ```

Kuray107/ls-timit-wsj0-100percent-supervised-meta

Kuray107

2022-04-12T11:19:25Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-11T22:24:57Z

--- tags: - generated_from_trainer model-index: - name: ls-timit-wsj0-100percent-supervised-meta results: [] ---  # ls-timit-wsj0-100percent-supervised-meta This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0531 - Wer: 0.0214 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1618 | 4.57 | 1000 | 0.0500 | 0.0432 | | 0.0489 | 9.13 | 2000 | 0.0535 | 0.0291 | | 0.0306 | 13.7 | 3000 | 0.0478 | 0.0275 | | 0.0231 | 18.26 | 4000 | 0.0531 | 0.0214 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3

Eterna2/LayoutParser

Eterna2

2022-04-12T08:58:12Z

0

2

null

[ "detectron2", "layout_parser", "license:apache-2.0", "region:us" ]

null

2022-04-12T08:13:51Z

--- license: apache-2.0 tags: - detectron2 - layout_parser --- Model binaries downloaded from https://github.com/Layout-Parser/layout-parser/blob/c0044a08da7a630e2241348e597a08ba6aa87ba1/src/layoutparser/models/detectron2/catalog.py

adache/tf-distilbert-base-uncased-finetuned-emotion

adache

2022-04-12T08:20:01Z

12

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-12T08:19:50Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tf-distilbert-base-uncased-finetuned-emotion results: [] ---  # tf-distilbert-base-uncased-finetuned-emotion 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: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.18.0 - TensorFlow 2.8.0 - Tokenizers 0.11.6

nntadotzip/bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022

nntadotzip

2022-04-12T08:14:00Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-12T07:53:07Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022 results: [] ---  # bert-base-cased-IUChatbot-ontologyDts-bertBaseCased-bertTokenizer-12April2022 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: - Loss: 0.3856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 357 | 0.4760 | | 0.6305 | 2.0 | 714 | 0.3957 | | 0.4345 | 3.0 | 1071 | 0.3856 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

adache/distilbert-base-uncased-finetuned-emotion

adache

2022-04-12T07:48:16Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-12T05:43:20Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion 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: 0.2270 - Accuracy: 0.9245 - F1: 0.9249 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8398 | 1.0 | 250 | 0.3276 | 0.9005 | 0.8966 | | 0.2541 | 2.0 | 500 | 0.2270 | 0.9245 | 0.9249 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Tokenizers 0.11.6

ID56/FF-Vision-CIFAR

ID56

2022-04-12T07:06:43Z

0

null

[ "pytorch", "image-classification", "dataset:cifar10", "license:cc-by-sa-4.0", "region:us" ]

image-classification

2022-04-06T22:02:53Z

--- thumbnail: "https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" license: cc-by-sa-4.0 tags: - image-classification datasets: - cifar10 metrics: - accuracy inference: false --- # CIFAR-10 Upside Down Classifier For the Fatima Fellowship 2022 Coding Challenge, DL for Vision track. <a href="https://wandb.ai/dealer56/cifar-updown-classifier/reports/CIFAR-10-Upside-Down-Classifier-Fatima-Fellowship-2022-Coding-Challenge-Vision---VmlldzoxODA2MDE4" target="_parent"><img src="https://img.shields.io/badge/weights-%26biases-ffcf40" alt="W&B Report"/></a> <img src="https://huggingface.co/ID56/FF-Vision-CIFAR/resolve/main/assets/cover_image.png" alt="Cover Image" width="800"/> ## Usage ### Model Definition ```python from torch import nn import timm from huggingface_hub import PyTorchModelHubMixin class UpDownEfficientNetB0(nn.Module, PyTorchModelHubMixin): """A simple Hub Mixin wrapper for timm EfficientNet-B0. Used to classify whether an image is upright or flipped down, on CIFAR-10.""" def __init__(self, **kwargs): super().__init__() self.base_model = timm.create_model('efficientnet_b0', num_classes=1, drop_rate=0.2, drop_path_rate=0.2) self.config = kwargs.pop("config", None) def forward(self, input): return self.base_model(input) ``` ### Loading the Model from Hub ```python net = UpDownEfficientNetB0.from_pretrained("ID56/FF-Vision-CIFAR") ``` ### Running Inference ```python from torchvision import transforms CIFAR_MEAN = (0.4914, 0.4822, 0.4465) CIFAR_STD = (0.247, 0.243, 0.261) transform = transforms.Compose([ transforms.Resize(40, 40), transforms.ToTensor(), transforms.Normalize(CIFAR_MEAN, CIFAR_STD) ]) image = load_some_image() # Load some PIL Image or uint8 HWC image array image = transform(image) # Convert to CHW image tensor image = image.unsqueeze(0) # Add batch dimension net.eval() pred = net(image) ```

tartuNLP/m2m100_418M_smugri

tartuNLP

2022-04-12T06:38:16Z

5

0

transformers

[ "transformers", "pytorch", "m2m_100", "text2text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-04T11:24:56Z

--- license: mit language: - en widget: - text: "Let us translate some text from Livonian to Võro!" --- # NMT for Finno-Ugric Languages This is an NMT system for translating between Võro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets. ## Tokenizer Four language codes were added to the tokenizer: __liv__, __vro__, __sma__ and __sme__. Currently the m2m100 tokenizer loads the list of languages from a hard-coded list, so it has to be updated after loading; see the code example below. ## Usage example Install the transformers and sentencepiece libraries: `pip install sentencepiece transformers` ```from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tartuNLP/m2m100_418M_smugri") #Fix the language codes in the tokenizer tokenizer.id_to_lang_token = dict(list(tokenizer.id_to_lang_token.items()) + list(tokenizer.added_tokens_decoder.items())) tokenizer.lang_token_to_id = dict(list(tokenizer.lang_token_to_id.items()) + list(tokenizer.added_tokens_encoder.items())) tokenizer.lang_code_to_token = { k.replace("_", ""): k for k in tokenizer.additional_special_tokens } tokenizer.lang_code_to_id = { k.replace("_", ""): v for k, v in tokenizer.lang_token_to_id.items() } model = AutoModelForSeq2SeqLM.from_pretrained("tartuNLP/m2m100_418M_smugri") tokenizer.src_lang = 'liv' encoded_src = tokenizer("Līvõ kēļ jelāb!", return_tensors="pt") encoded_out = model.generate(**encoded_src, forced_bos_token_id = tokenizer.get_lang_id("sme")) print(tokenizer.batch_decode(encoded_out, skip_special_tokens=True)) ``` The output is `Livčča giella eallá.`

gary109/wav2vec2-base-mirst500

gary109

2022-04-12T05:52:24Z

2

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:mir_st500", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2022-04-11T06:13:13Z

--- license: apache-2.0 tags: - audio-classification - generated_from_trainer datasets: - mir_st500 metrics: - accuracy model-index: - name: wav2vec2-base-mirst500 results: [] ---  # wav2vec2-base-mirst500 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /workspace/datasets/datasets/MIR_ST500/MIR_ST500_AUDIO_CLASSIFICATION.py dataset. It achieves the following results on the evaluation set: - Loss: 0.8678 - Accuracy: 0.7017 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 1 - seed: 0 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1999 | 1.0 | 1304 | 1.1029 | 0.5877 | | 1.0779 | 2.0 | 2608 | 0.9455 | 0.6555 | | 0.9775 | 3.0 | 3912 | 0.9670 | 0.6523 | | 0.9542 | 4.0 | 5216 | 0.8810 | 0.6946 | | 0.9403 | 5.0 | 6520 | 0.8678 | 0.7017 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.9.1+cu102 - Datasets 2.0.0 - Tokenizers 0.10.3

kmasiak/FraudDetection

kmasiak

2022-04-12T03:07:59Z

0

null

[ "region:us" ]

null

2022-04-06T23:36:56Z

The files in this repository were used for detecting accounting fraud using VAE-GAN and other models. Here is a breakdown of the files: 20220409-21_35_52_ep_3_decoder_model.pth - Decoder I trained that has the best results. 20220409-21_35_52_ep_3_discriminator_model.pth - Discriminator I trained that has the best results. 20220409-21_35_52_ep_3_encoder_model.pth - Encoder I trained that has the best results. Dataset.csv - The dataset used for train/testing, contains 9 features, 532909 regular, 70 global, and 30 local transactions. Fraud_Detection_AutoML.ipynb - AutoSklearnClassifier (an implementation of automl) is used on the fraud detection dataset. Fraud_Detection_Supervised.ipynb - KNN classifier is used on the fraud detection dataset. Gradio_Demo.ipynb - Note this is just for demo purposes. The actual implementation of the VAE-GAN model is not used in the gradio demo due to time constraints. SMOTE_VAE_GAN.ipynb - Use SMOTE to help mitigate the issue of an unbalanced dataset while training. VAE_GAN_Test.ipynb - Evaluates a VAE-GAN model. VAE_GAN_Train.ipynb - Trains the VAE-GAN model on the fraud detection dataset. ep_100_decoder_model.pth - Pre-trained decoder from a previous paper I used to improve results. ep_100_discriminator_model.pth - Pre-trained discriminator from a previous paper I used to improve results. ep_100_encoder_model.pth - Pre-trained encoder from a previous paper I used to improve results. Note: Credit for the above 3 files goes to Credit goes to Jie Dai, Chenjian Wang, and Shuoyi Wei. Accounting Fraud Detection with VAE-GAN, 2020.

NoCaptain/DistilRoBERTa-C19-Vax-Fine-tuned

NoCaptain

2022-04-12T00:34:14Z

2

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-10T00:51:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - accuracy - f1 model-index: - name: DistilRoberta results: [] ---  # DistilRoberta This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1246 - Precision: 0.9633 - Accuracy: 0.9697 - F1: 0.9705 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|:------:| | 0.5894 | 0.4 | 500 | 0.4710 | 0.8381 | 0.7747 | 0.7584 | | 0.3863 | 0.8 | 1000 | 0.3000 | 0.8226 | 0.8737 | 0.8858 | | 0.2272 | 1.2 | 1500 | 0.1973 | 0.9593 | 0.9333 | 0.9329 | | 0.1639 | 1.6 | 2000 | 0.1694 | 0.9067 | 0.9367 | 0.9403 | | 0.1263 | 2.0 | 2500 | 0.1128 | 0.9657 | 0.9597 | 0.9603 | | 0.0753 | 2.4 | 3000 | 0.1305 | 0.9614 | 0.967 | 0.9679 | | 0.0619 | 2.8 | 3500 | 0.1246 | 0.9633 | 0.9697 | 0.9705 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

modhp/wav2vec2-model2-torgo

modhp

2022-04-11T23:31:56Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-08T19:47:36Z

--- tags: - generated_from_trainer model-index: - name: wav2vec2-model2-torgo results: [] ---  # wav2vec2-model2-torgo This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9975 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.1 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:---:| | 12.5453 | 0.76 | 500 | 14.6490 | 1.0 | | 4.8036 | 1.53 | 1000 | 8.4523 | 1.0 | | 5.0421 | 2.29 | 1500 | 5.4114 | 1.0 | | 5.2055 | 3.05 | 2000 | 11.0507 | 1.0 | | 4.6389 | 3.82 | 2500 | 4.6792 | 1.0 | | 4.5523 | 4.58 | 3000 | 4.7855 | 1.0 | | 4.7843 | 5.34 | 3500 | 11.2783 | 1.0 | | 4.6066 | 6.11 | 4000 | 8.7807 | 1.0 | | 4.7382 | 6.87 | 4500 | 2942.0220 | 1.0 | | 130.5733 | 7.63 | 5000 | 5.8412 | 1.0 | | 4.4972 | 8.4 | 5500 | 17.7038 | 1.0 | | 4.5196 | 9.16 | 6000 | 11.4548 | 1.0 | | 4.3198 | 9.92 | 6500 | 6.0885 | 1.0 | | 4.4273 | 10.69 | 7000 | 6.7374 | 1.0 | | 4.2783 | 11.45 | 7500 | 4.7276 | 1.0 | | 4.2985 | 12.21 | 8000 | 6.1412 | 1.0 | | 4.3262 | 12.98 | 8500 | 5.2621 | 1.0 | | 4.1705 | 13.74 | 9000 | 5.2214 | 1.0 | | 4.3176 | 14.5 | 9500 | 5.5359 | 1.0 | | 3.9808 | 15.27 | 10000 | 4.1537 | 1.0 | | 4.0228 | 16.03 | 10500 | 4.2962 | 1.0 | | 4.0595 | 16.79 | 11000 | 7.6361 | 1.0 | | 4.0088 | 17.56 | 11500 | 6.8715 | 1.0 | | 3.8727 | 18.32 | 12000 | 8.8657 | 1.0 | | 4.0073 | 19.08 | 12500 | 5.8170 | 1.0 | | 3.8511 | 19.85 | 13000 | 13.9836 | 1.0 | | 4.0899 | 20.61 | 13500 | 5.3287 | 1.0 | | 3.8782 | 21.37 | 14000 | 8.0635 | 1.0 | | 3.9235 | 22.14 | 14500 | 5.5129 | 1.0 | | 3.7276 | 22.9 | 15000 | 5.0819 | 1.0 | | 3.7908 | 23.66 | 15500 | 6.1458 | 1.0 | | 3.9176 | 24.43 | 16000 | 4.6094 | 1.0 | | 3.8477 | 25.19 | 16500 | 5.1406 | 1.0 | | 3.6917 | 25.95 | 17000 | 4.5684 | 1.0 | | 3.8568 | 26.72 | 17500 | 4.0306 | 1.0 | | 3.7231 | 27.48 | 18000 | 5.6331 | 1.0 | | 3.8145 | 28.24 | 18500 | 8.2997 | 1.0 | | 3.7809 | 29.01 | 19000 | 5.7468 | 1.0 | | 3.5995 | 29.77 | 19500 | 4.9975 | 1.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 1.18.3 - Tokenizers 0.11.6

huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings

huggingtweets

2022-04-11T22:44:40Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-11T22:43:38Z

--- language: en thumbnail: http://www.huggingtweets.com/angrymemorys-oldandtoothless-sadboi666_-witheredstrings/1649717075201/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1506323689456947207/xBvvxyQr_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/378800000610482331/8808c2f408b97fe3646f2dca86441506_400x400.jpeg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">makeouthill & VacuumF & Jason Hendricks & Angry Memories</div> <div style="text-align: center; font-size: 14px;">@angrymemorys-oldandtoothless-sadboi666_-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from makeouthill & VacuumF & Jason Hendricks & Angry Memories. | Data | makeouthill | VacuumF | Jason Hendricks | Angry Memories | | --- | --- | --- | --- | --- | | Tweets downloaded | 321 | 425 | 3250 | 3199 | | Retweets | 34 | 0 | 0 | 941 | | Short tweets | 49 | 31 | 0 | 1110 | | Tweets kept | 238 | 394 | 3250 | 1148 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2nh2rd94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @angrymemorys-oldandtoothless-sadboi666_-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/me7rzksi/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/angrymemorys-oldandtoothless-sadboi666_-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

adasnew/t5-small-xsum

adasnew

2022-04-11T22:35:12Z

18

1

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-11T18:45:17Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-xsum results: [] ---  # t5-small-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.3953 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.8641 | 0.04 | 500 | 2.6202 | | 2.7466 | 0.08 | 1000 | 2.5660 | | 2.8767 | 0.12 | 1500 | 2.5319 | | 2.7099 | 0.16 | 2000 | 2.5107 | | 2.7752 | 0.2 | 2500 | 2.4922 | | 2.6037 | 0.24 | 3000 | 2.4800 | | 2.8236 | 0.27 | 3500 | 2.4677 | | 2.7089 | 0.31 | 4000 | 2.4581 | | 2.7299 | 0.35 | 4500 | 2.4498 | | 2.7498 | 0.39 | 5000 | 2.4420 | | 2.6186 | 0.43 | 5500 | 2.4346 | | 2.7817 | 0.47 | 6000 | 2.4288 | | 2.5559 | 0.51 | 6500 | 2.4239 | | 2.6725 | 0.55 | 7000 | 2.4186 | | 2.6316 | 0.59 | 7500 | 2.4149 | | 2.5561 | 0.63 | 8000 | 2.4115 | | 2.5708 | 0.67 | 8500 | 2.4097 | | 2.5861 | 0.71 | 9000 | 2.4052 | | 2.6363 | 0.74 | 9500 | 2.4024 | | 2.7435 | 0.78 | 10000 | 2.4003 | | 2.7258 | 0.82 | 10500 | 2.3992 | | 2.6113 | 0.86 | 11000 | 2.3983 | | 2.6006 | 0.9 | 11500 | 2.3972 | | 2.5684 | 0.94 | 12000 | 2.3960 | | 2.6181 | 0.98 | 12500 | 2.3953 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

tonyalves/ft-pt-br-local-2

tonyalves

2022-04-11T20:57:03Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-11T20:46:13Z

--- license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer model-index: - name: ft-pt-br-local-2 results: [] ---  # ft-pt-br-local-2 This model is a fine-tuned version of [tonyalves/output](https://huggingface.co/tonyalves/output) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 100 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.9.1+cu102 - Datasets 1.18.4 - Tokenizers 0.11.6

akoksal/bounti

akoksal

2022-04-11T20:12:25Z

304

6

transformers

[ "transformers", "pytorch", "bert", "text-classification", "sentiment", "twitter", "turkish", "tr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-11T19:55:36Z

--- language: "tr" tags: - sentiment - twitter - turkish --- This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained [BERTurk model 128k uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) with [BounTi dataset](https://ieeexplore.ieee.org/document/9477814). ## Usage in Hugging Face Pipeline ``` from transformers import pipeline bounti = pipeline("sentiment-analysis",model="akoksal/bounti") print(bounti("Bu yemeği pek sevmedim")) >> [{'label': 'negative', 'score': 0.8012508153915405}] ``` ## Results The scores of the finetuned model with BERTurk: ||Accuracy|Precision|Recall|F1| |-------------|:---------:|:---------:|:------:|:-----:| |Validation|0.745|0.706|0.730|0.715| |Test|0.723|0.692|0.729|0.701| ## Dataset You can find the dataset in [our Github repo](https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis) with the training, validation, and test splits. Due to Twitter copyright, we cannot release the full text of the tweets. We share the tweet IDs, and the full text can be downloaded through official Twitter API. | | Training | Validation | Test | |----------|:--------:|:----------:|:----:| | Positive | 1691 | 188 | 469 | | Neutral | 3034 | 338 | 843 | | Negative | 1008 | 113 | 280 | | Total | 5733 | 639 | 1592 | ## Citation You can cite the following paper if you use our work: ``` @INPROCEEDINGS{BounTi, author={Köksal, Abdullatif and Özgür, Arzucan}, booktitle={2021 29th Signal Processing and Communications Applications Conference (SIU)}, title={Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis}, year={2021}, volume={}, number={} } ``` ---

Kuray107/ls-timit-100percent-supervised-meta

Kuray107

2022-04-11T19:44:56Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-11T14:57:43Z

--- tags: - generated_from_trainer model-index: - name: ls-timit-100percent-supervised-meta results: [] ---  # ls-timit-100percent-supervised-meta This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0649 - Wer: 0.0253 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0964 | 7.04 | 1000 | 0.0706 | 0.0342 | | 0.0445 | 14.08 | 2000 | 0.0649 | 0.0253 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3

nateraw/fastai-dummy-learner

nateraw

2022-04-11T19:23:08Z

0

fastai

[ "fastai", "region:us" ]

null

2022-04-11T19:15:53Z

--- tags: - fastai --- # Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (template below and [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using the 🤗Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join our fastai community on the Hugging Face Discord! Greetings fellow fastlearner 🤝! --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

jegormeister/robbert-v2-dutch-base-mqa-finetuned

jegormeister

2022-04-11T19:09:29Z

1,058

4

sentence-transformers

[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "robbert", "nl", "dataset:clips/mqa", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-04-11T13:40:02Z

--- language: nl pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - robbert datasets: - clips/mqa --- # jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base). It was fine-tuned on 1,000,000 rows of Dutch FAQ question-answer pairs from [clips/mqa](https://huggingface.co/datasets/clips/mqa). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') model = AutoModel.from_pretrained('jegorkitskerkin/robbert-v2-dutch-base-mqa-finetuned') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 12500 with parameters: ``` {'batch_size': 80, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

TrabajoAprendizajeProfundo/Trabajo

TrabajoAprendizajeProfundo

2022-04-11T17:27:15Z

5

0

stable-baselines3

[ "stable-baselines3", "deep-reinforcement-learning", "reinforcement-learning", "region:us" ]

reinforcement-learning

2022-04-09T11:48:09Z

--- tags: - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 --- # TODO: Fill this model card This is a pre-trained model of agent playing Asteroids-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. ### Usage (with Stable-baselines3) Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed: ``` pip install stable-baselines3 pip install huggingface_sb3 ``` Then, you can use the model like this: ```python import gym from huggingface_sb3 import load_from_hub from stable_baselines3 import PPO from stable_baselines3.common.evaluation import evaluate_policy # Retrieve the model from the hub ## repo_id = id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name}) ## filename = name of the model zip file from the repository checkpoint = load_from_hub(repo_id="TrabajoAprendizajeProfundo/Trabajo", filename="Asteroids-v0.zip") model = PPO.load(checkpoint) # Evaluate the agent eval_env = gym.make('Asteroids-v0') mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}") # Watch the agent play directory = './video' env = Recorder(env, directory) obs = env.reset() done = False while not done: action, _state = model2.predict(obs) obs, reward, done, info = env.step(action) env.play() ``` ### Evaluation Results mean_reward, std_reward = evaluate_policy(model2, eval_env, n_eval_episodes=10) print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

tbosse/bert-base-german-cased-finetuned-subj_v5_11Epoch

tbosse

2022-04-11T17:08:55Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-11T15:51:15Z

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-german-cased-finetuned-subj_v5_11Epoch results: [] ---  # bert-base-german-cased-finetuned-subj_v5_11Epoch This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3467 - Precision: 0.8240 - Recall: 0.8287 - F1: 0.8263 - Accuracy: 0.9198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 32 | 0.3485 | 0.6992 | 0.7051 | 0.7021 | 0.8639 | | No log | 2.0 | 64 | 0.2679 | 0.7947 | 0.7612 | 0.7776 | 0.8994 | | No log | 3.0 | 96 | 0.2555 | 0.8073 | 0.8118 | 0.8095 | 0.9112 | | No log | 4.0 | 128 | 0.2591 | 0.8290 | 0.8034 | 0.8160 | 0.9132 | | No log | 5.0 | 160 | 0.2808 | 0.8450 | 0.8118 | 0.8281 | 0.9158 | | No log | 6.0 | 192 | 0.2953 | 0.8386 | 0.8174 | 0.8279 | 0.9172 | | No log | 7.0 | 224 | 0.3164 | 0.8347 | 0.8371 | 0.8359 | 0.9204 | | No log | 8.0 | 256 | 0.3267 | 0.8329 | 0.8258 | 0.8293 | 0.9178 | | No log | 9.0 | 288 | 0.3373 | 0.8268 | 0.8315 | 0.8291 | 0.9198 | | No log | 10.0 | 320 | 0.3450 | 0.8324 | 0.8230 | 0.8277 | 0.9211 | | No log | 11.0 | 352 | 0.3467 | 0.8240 | 0.8287 | 0.8263 | 0.9198 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment

Giyaseddin

2022-04-11T15:17:08Z

6

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-11T09:03:04Z

--- license: apache-2.0 language: en library: transformers other: distilbert datasets: - Short Question Answer Assessment Dataset --- # DistilBERT base uncased model for Short Question Answer Assessment ## Model description DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model. This is a classification model that solves Short Question Answer Assessment task, finetuned [pretrained DistilBERT model](https://huggingface.co/distilbert-base-uncased) on [Question Answer Assessment dataset](#) ## Intended uses & limitations This can only be used for the kind of questions and answers provided by that are similar to the ones in the dataset of [Banjade et al.](https://aclanthology.org/W16-0520.pdf). ### How to use You can use this model directly with a : ```python >>> from transformers import pipeline >>> classifier = pipeline("text-classification", model="Giyaseddin/distilbert-base-uncased-finetuned-short-answer-assessment", return_all_scores=True) >>> context = "To rescue a child who has fallen down a well, rescue workers fasten him to a rope, the other end of which is then reeled in by a machine. The rope pulls the child straight upward at steady speed." >>> question = "How does the amount of tension in the rope compare to the downward force of gravity acting on the child?" >>> ref_answer = "Since the child is being raised straight upward at a constant speed, the net force on the child is zero and all the forces balance. That means that the tension in the rope balances the downward force of gravity." >>> student_answer = "The tension force is higher than the force of gravity." >>> >>> body = " [SEP] ".join([context, question, ref_answer, student_answer]) >>> raw_results = classifier([body]) >>> raw_results [[{'label': 'LABEL_0', 'score': 0.0004029414849355817}, {'label': 'LABEL_1', 'score': 0.0005476847873069346}, {'label': 'LABEL_2', 'score': 0.998059093952179}, {'label': 'LABEL_3', 'score': 0.0009902542224153876}]] >>> _LABELS_ID2NAME = {0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"} >>> results = [] >>> for result in raw_results: for score in result: results.append([ {_LABELS_ID2NAME[int(score["label"][-1:])]: "%.2f" % score["score"]} ]) >>> results [[{'correct': '0.00'}], [{'correct_but_incomplete': '0.00'}], [{'contradictory': '1.00'}], [{'incorrect': '0.00'}]] ``` ### Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. It also inherits some of [the bias of its teacher model](https://huggingface.co/bert-base-uncased#limitations-and-bias). This bias will also affect all fine-tuned versions of this model. Also one of the limiations of this model is the length, longer sequences would lead to wrong predictions, due to the pre-processing phase (after concatentating the input sequences, the important student answer might be pruned!) ## Pre-training data DistilBERT pretrained on the same data as BERT, which is [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038 unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and headers). ## Fine-tuning data The annotated dataset consists of 900 students’ short constructed answers and their correctness in the given context. Four qualitative levels of correctness are defined, correct, correct-but-incomplete, contradictory and Incorrect. ## Training procedure ### Preprocessing In the preprocessing phase, the following parts are concatenated: _question context_, _question_, _reference_answer_, and _student_answer_ using the separator `[SEP]`. This makes the full text as: ``` [CLS] Context Sentence [SEP] Question Sentence [SEP] Reference Answer Sentence [SEP] Student Answer Sentence [CLS] ``` The data are splitted according to the following ratio: - Training set 80%. - Test set 20%. Lables are mapped as: `{0: "correct", 1: "correct_but_incomplete", 2: "contradictory", 3: "incorrect"}` ### Fine-tuning The model was finetuned on GeForce GTX 960M for 20 minuts. The parameters are: | Parameter | Value | |:-------------------:|:-----:| | Learning rate | 5e-5 | | Weight decay | 0.01 | | Training batch size | 8 | | Epochs | 4 | Here is the scores during the training: | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |:----------:|:-------------:|:-----------------:|:----------:|:---------:|:----------:|:--------:| | 1 | No log | 0.665765 | 0.755330 | 0.743574 | 0.781210 | 0.755330 | | 2 | 0.932100 | 0.362124 | 0.890355 | 0.889875 | 0.891407 | 0.890355 | | 3 | 0.364900 | 0.226225 | 0.942132 | 0.941802 | 0.942458 | 0.942132 | | 3 | 0.176900 | 0.193660 | 0.954315 | 0.954175 | 0.954985 | 0.954315 | ## Evaluation results When fine-tuned on downstream task of Question Answer Assessment, 4 class classification, this model achieved the following results: (scores are rounded to 2 floating points) | | precision | recall | f1-score | support | |:------------------------:|:----------:|:-------:|:--------:|:-------:| | _correct_ | 0.938 | 0.989 | 0.963 | 366 | | _correct_but_incomplete_ | 0.975 | 0.922 | 0.948 | 257 | | _contradictory_ | 0.946 | 0.938 | 0.942 | 113 | | _incorrect_ | 0.963 | 0.944 | 0.953 | 249 | | accuracy | - | - | 0.954 | 985 | | macro avg | 0.956 | 0.948 | 0.952 | 985 | | weighted avg | 0.955 | 0.954 | 0.954 | 985 | Confusion matrix: | Actual \ Predicted | _correct_ | _correct_but_incomplete_ | _contradictory_ | _incorrect_ | |:------------------------:|:---------:|:------------------------:|:---------------:|:-----------:| | _correct_ | 362 | 4 | 0 | 0 | | _correct_but_incomplete_ | 13 | 237 | 0 | 7 | | _contradictory_ | 4 | 1 | 106 | 2 | | _incorrect_ | 7 | 1 | 6 | 235 | The AUC score is: 'micro'= **0.9695** and 'macro': **0.9659**

optimum/neuron-MiniLMv2-L12-H384-distilled-finetuned-clinc

optimum

2022-04-11T13:44:33Z

5

1

transformers

[ "transformers", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-11T13:38:35Z

--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 ---  # Neuron conversation # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Accuracy: 0.9389999 ## Deploy/use Model If you want to use this model checkout the following notenbook: [sagemaker/18_inferentia_inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb) ```python from sagemaker.huggingface.model import HuggingFaceModel # create Hugging Face Model Class huggingface_model = HuggingFaceModel( model_data=s3_model_uri, # path to your model and script role=role, # iam role with permissions to create an Endpoint transformers_version="4.12", # transformers version used pytorch_version="1.9", # pytorch version used py_version='py37', # python version used ) # Let SageMaker know that we've already compiled the model via neuron-cc huggingface_model._is_compiled_model = True # deploy the endpoint endpoint predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type="ml.inf1.xlarge" # AWS Inferentia Instance ) ```

aleksavega/t5-efficient-base-finetuned-1.2

aleksavega

2022-04-11T12:04:08Z

12

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-04-11T09:53:00Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: t5-efficient-base-finetuned-1.2 results: [] ---  # t5-efficient-base-finetuned-1.2 This model is a fine-tuned version of [google/t5-efficient-base](https://huggingface.co/google/t5-efficient-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5294 - Rouge1: 62.691 - Rouge2: 55.9731 - Rougel: 60.9097 - Rougelsum: 61.4393 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 4662 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 2.2424 | 1.0 | 1217 | 1.7042 | 34.2215 | 24.2754 | 31.7289 | 32.4237 | | 1.7716 | 2.0 | 2434 | 1.6184 | 43.4774 | 34.0476 | 41.3691 | 41.9132 | | 1.6324 | 3.0 | 3651 | 1.5811 | 49.1441 | 40.7935 | 47.0077 | 47.6388 | | 1.5226 | 4.0 | 4868 | 1.5243 | 54.4769 | 46.3387 | 52.3289 | 52.9555 | | 1.4121 | 5.0 | 6085 | 1.5040 | 56.8792 | 49.1963 | 54.7327 | 55.2805 | | 1.331 | 6.0 | 7302 | 1.4930 | 58.6896 | 51.1683 | 56.7096 | 57.3605 | | 1.2677 | 7.0 | 8519 | 1.4785 | 59.9285 | 52.4631 | 57.8575 | 58.4203 | | 1.2175 | 8.0 | 9736 | 1.4839 | 60.0299 | 52.8806 | 58.0099 | 58.6348 | | 1.1782 | 9.0 | 10953 | 1.4908 | 61.247 | 54.0887 | 59.2175 | 59.7658 | | 1.1442 | 10.0 | 12170 | 1.4882 | 61.9895 | 54.9455 | 60.0728 | 60.5786 | | 1.1118 | 11.0 | 13387 | 1.5061 | 62.1077 | 55.1276 | 60.2218 | 60.7475 | | 1.081 | 12.0 | 14604 | 1.5078 | 61.6083 | 54.6805 | 59.7912 | 60.2489 | | 1.0668 | 13.0 | 15821 | 1.5200 | 62.3075 | 55.5201 | 60.5192 | 60.9557 | | 1.0488 | 14.0 | 17038 | 1.5344 | 62.5144 | 55.6332 | 60.6845 | 61.1715 | | 1.0324 | 15.0 | 18255 | 1.5313 | 62.7697 | 56.0313 | 60.9298 | 61.4739 | | 1.0302 | 16.0 | 19472 | 1.5294 | 62.691 | 55.9731 | 60.9097 | 61.4393 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.6

optimum/MiniLMv2-L12-H384-distilled-finetuned-clinc

optimum

2022-04-11T11:21:21Z

6

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-11T11:18:49Z

--- tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.94 ---  # MiniLMv2-L12-H384-distilled-from-RoBERTa-Large-distilled-clinc This model is a fine-tuned version of [nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L12-H384-distilled-from-RoBERTa-Large) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.3479 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 60 | 0.8171 | 0.2490 | | No log | 2.0 | 120 | 0.7039 | 0.6568 | | No log | 3.0 | 180 | 0.6067 | 0.7932 | | 0.7269 | 4.0 | 240 | 0.5270 | 0.8674 | | 0.7269 | 5.0 | 300 | 0.4659 | 0.9010 | | 0.7269 | 6.0 | 360 | 0.4201 | 0.9194 | | 0.7269 | 7.0 | 420 | 0.3867 | 0.9352 | | 0.4426 | 8.0 | 480 | 0.3649 | 0.9352 | | 0.4426 | 9.0 | 540 | 0.3520 | 0.9403 | | 0.4426 | 10.0 | 600 | 0.3479 | 0.94 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.11.0 - Datasets 1.16.1 - Tokenizers 0.10.3

vocab-transformers/distilbert-tokenizer_256k-MLM_best

vocab-transformers

2022-04-11T11:16:06Z

5

0

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-04-11T11:14:12Z

# DistilBERT with 256k token embeddings This model was initialized with a word2vec token embedding matrix with 256k entries, but these token embeddings were updated during MLM. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs. Then the model was trained on this dataset with MLM for 1.55M steps (batch size 64). The token embeddings were updated during MLM.

vocab-transformers/distilbert-word2vec_256k-MLM_best

vocab-transformers

2022-04-11T11:13:13Z

27

4

transformers

[ "transformers", "pytorch", "distilbert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-04-11T11:10:05Z

# DistilBERT with word2vec token embeddings This model has a word2vec token embedding matrix with 256k entries. The word2vec was trained on 100GB data from C4, MSMARCO, News, Wikipedia, S2ORC, for 3 epochs. Then the model was trained on this dataset with MLM for 1.37M steps (batch size 64). The token embeddings were NOT updated. For the initial word2vec weights with Gensim see: [https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec](https://huggingface.co/vocab-transformers/distilbert-word2vec_256k-MLM_1M/tree/main/word2vec)

Chikashi/t5-small-finetuned-wikihow_3epoch_b8_lr3e-3

Chikashi

2022-04-11T08:17:07Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-10T23:51:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 27.1711 ---  # t5-small-finetuned-wikihow_3epoch_b8_lr3e-3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3163 - Rouge1: 27.1711 - Rouge2: 10.6296 - Rougel: 23.206 - Rougelsum: 26.4801 - Gen Len: 18.5433 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.0734 | 0.25 | 5000 | 2.7884 | 22.4825 | 7.2492 | 19.243 | 21.9167 | 18.0616 | | 2.9201 | 0.51 | 10000 | 2.7089 | 24.0869 | 8.0348 | 20.4814 | 23.4541 | 18.5994 | | 2.8403 | 0.76 | 15000 | 2.6390 | 24.62 | 8.3776 | 20.8736 | 23.9784 | 18.4676 | | 2.7764 | 1.02 | 20000 | 2.5943 | 24.1504 | 8.3933 | 20.8271 | 23.5382 | 18.4078 | | 2.6641 | 1.27 | 25000 | 2.5428 | 25.6574 | 9.2371 | 21.8576 | 24.9558 | 18.4249 | | 2.6369 | 1.53 | 30000 | 2.5042 | 25.5208 | 9.254 | 21.6673 | 24.8589 | 18.6467 | | 2.6 | 1.78 | 35000 | 2.4637 | 26.094 | 9.7003 | 22.3097 | 25.4695 | 18.5065 | | 2.5562 | 2.03 | 40000 | 2.4285 | 26.5374 | 9.9222 | 22.5291 | 25.8836 | 18.5553 | | 2.4322 | 2.29 | 45000 | 2.3858 | 26.939 | 10.3555 | 23.0211 | 26.2834 | 18.5614 | | 2.4106 | 2.54 | 50000 | 2.3537 | 26.7423 | 10.2816 | 22.7986 | 26.083 | 18.5792 | | 2.3731 | 2.8 | 55000 | 2.3163 | 27.1711 | 10.6296 | 23.206 | 26.4801 | 18.5433 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

huggan/pix2pix-facades-demo

huggan

2022-04-11T08:09:26Z

0

null

[ "pytorch", "huggan", "gan", "license:mit", "region:us" ]

null

2022-04-09T13:16:10Z

--- tags: - huggan - gan # See a list of available tags here: # https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12 # task: unconditional-image-generation or conditional-image-generation or image-to-image license: mit --- This was run from this implementation: https://github.com/NielsRogge/community-events-1/blob/improve_pix2pix/huggan/pytorch/pix2pix/train.py The command to run was: ```bash accelerate launch train.py --checkpoint_interval 1 --push_to_hub --output_dir pix2pix-facades --hub_model_id huggan/pix2pix-facades-demo --wandb ```

Mandela/DialoGPT-small-DEADPOOL

Mandela

2022-04-10T23:43:06Z

0

null

[ "conversation", "region:us" ]

null

2022-04-10T16:49:29Z

--- language: - python tags: - conversation ---

Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-5

Chikashi

2022-04-10T23:42:14Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-09T19:16:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 26.1071 ---  # t5-small-finetuned-wikihow_3epoch_b4_lr3e-5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.4351 - Rouge1: 26.1071 - Rouge2: 9.3627 - Rougel: 22.0825 - Rougelsum: 25.4514 - Gen Len: 18.474 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.9216 | 0.13 | 5000 | 2.6385 | 23.8039 | 7.8863 | 20.0109 | 23.0802 | 18.3481 | | 2.8158 | 0.25 | 10000 | 2.5884 | 24.2567 | 8.2003 | 20.438 | 23.5325 | 18.3833 | | 2.7743 | 0.38 | 15000 | 2.5623 | 24.8471 | 8.3768 | 20.8711 | 24.1114 | 18.2901 | | 2.7598 | 0.51 | 20000 | 2.5368 | 25.1566 | 8.6721 | 21.1896 | 24.4558 | 18.3561 | | 2.7192 | 0.64 | 25000 | 2.5220 | 25.3477 | 8.8106 | 21.3799 | 24.6742 | 18.3108 | | 2.7207 | 0.76 | 30000 | 2.5114 | 25.5912 | 8.998 | 21.5508 | 24.9344 | 18.3445 | | 2.7041 | 0.89 | 35000 | 2.4993 | 25.457 | 8.8644 | 21.4516 | 24.7965 | 18.4354 | | 2.687 | 1.02 | 40000 | 2.4879 | 25.5886 | 8.9766 | 21.6794 | 24.9512 | 18.4035 | | 2.6652 | 1.14 | 45000 | 2.4848 | 25.7367 | 9.078 | 21.7096 | 25.0924 | 18.4328 | | 2.6536 | 1.27 | 50000 | 2.4761 | 25.7368 | 9.1609 | 21.729 | 25.0866 | 18.3117 | | 2.6589 | 1.4 | 55000 | 2.4702 | 25.7738 | 9.1413 | 21.7492 | 25.114 | 18.4862 | | 2.6384 | 1.53 | 60000 | 2.4620 | 25.7433 | 9.1356 | 21.8198 | 25.0896 | 18.489 | | 2.6337 | 1.65 | 65000 | 2.4595 | 26.0919 | 9.2605 | 21.9447 | 25.4065 | 18.4083 | | 2.6375 | 1.78 | 70000 | 2.4557 | 26.0912 | 9.3469 | 22.0182 | 25.4428 | 18.4133 | | 2.6441 | 1.91 | 75000 | 2.4502 | 26.1366 | 9.3143 | 22.058 | 25.4673 | 18.4972 | | 2.6276 | 2.03 | 80000 | 2.4478 | 25.9929 | 9.2464 | 21.9271 | 25.3263 | 18.469 | | 2.6062 | 2.16 | 85000 | 2.4467 | 26.0465 | 9.3166 | 22.0342 | 25.3998 | 18.3777 | | 2.6126 | 2.29 | 90000 | 2.4407 | 26.1953 | 9.3848 | 22.1148 | 25.5161 | 18.467 | | 2.6182 | 2.42 | 95000 | 2.4397 | 26.1331 | 9.3626 | 22.1076 | 25.4627 | 18.4413 | | 2.6041 | 2.54 | 100000 | 2.4375 | 26.1301 | 9.3567 | 22.0869 | 25.465 | 18.4929 | | 2.5996 | 2.67 | 105000 | 2.4367 | 26.0956 | 9.3314 | 22.063 | 25.4242 | 18.5074 | | 2.6144 | 2.8 | 110000 | 2.4355 | 26.1764 | 9.4157 | 22.1231 | 25.5175 | 18.4729 | | 2.608 | 2.93 | 115000 | 2.4351 | 26.1071 | 9.3627 | 22.0825 | 25.4514 | 18.474 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/s_m_frank

huggingtweets

2022-04-10T22:28:09Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-10T22:27:04Z

--- language: en thumbnail: http://www.huggingtweets.com/s_m_frank/1649629685555/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1480658144833515525/DS0AOK_d_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">cute junco observer</div> <div style="text-align: center; font-size: 14px;">@s_m_frank</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from cute junco observer. | Data | cute junco observer | | --- | --- | | Tweets downloaded | 1253 | | Retweets | 482 | | Short tweets | 184 | | Tweets kept | 587 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2s2slp94/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @s_m_frank's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2bjkzwlr) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2bjkzwlr/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/s_m_frank') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

tonyalves/local_dataset

tonyalves

2022-04-10T22:23:55Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "pt", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-10T22:05:01Z

--- language: - pt license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer model-index: - name: local_dataset results: [] ---  # local_dataset 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 - PT dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.17.0 - Pytorch 1.9.1+cu102 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/nordicshrew

huggingtweets

2022-04-10T22:04:13Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-10T22:02:07Z

--- language: en thumbnail: http://www.huggingtweets.com/nordicshrew/1649628249290/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1129935220260704256/RSmw3S0E_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">guelph’s finest poster</div> <div style="text-align: center; font-size: 14px;">@nordicshrew</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from guelph’s finest poster. | Data | guelph’s finest poster | | --- | --- | | Tweets downloaded | 3219 | | Retweets | 429 | | Short tweets | 145 | | Tweets kept | 2645 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ywrep7o1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nordicshrew's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jti1kl9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jti1kl9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/nordicshrew') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

keerthisaran/distilbert-base-uncased-finetuned-emotion

keerthisaran

2022-04-10T21:58:34Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-14T18:45:20Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.920435758296201 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.92 - F1: 0.9204 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8464 | 1.0 | 250 | 0.3125 | 0.9085 | 0.9061 | | 0.2476 | 2.0 | 500 | 0.2183 | 0.92 | 0.9204 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Ayobami/UpsideDownDetector

Ayobami

2022-04-10T20:42:37Z

0

null

[ "license:mit", "region:us" ]

null

2022-04-10T06:26:56Z

--- license: mit --- An image rotation detector trained to detect if an image is upside down or not

BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal

BigSalmon

2022-04-10T20:04:26Z

3

0

transformers

[ "transformers", "pytorch", "gpt_neo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-04-04T05:04:06Z

It works worse than the GPT-2 Large & Medium models I have been training, because I don't have the compute needed to train the entire dataset I have. I had to resort to using bits. ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal") model = AutoModelForCausalLM.from_pretrained("BigSalmon/GPTNeo1.3BPointsLincolnFormalInformal") ``` ``` - moviepass to return - this summer - swooped up by - original co-founder stacy spikes text: the re-launch of moviepass is set to transpire this summer, ( rescued at the hands of / under the stewardship of / spearheaded by ) its founding father, stacy spikes. *** - middle schools do not have recess - should get back to doing it - amazing for communication - and getting kids to move around text: a casualty of the education reform craze, recess has been excised from middle schools. this is tragic, for it is instrumental in honing children's communication skills and encouraging physical activity. *** - ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` Points and keywords. Informal to formal.

huggingtweets/graveyard_plots-hel_ql-witheredstrings

huggingtweets

2022-04-10T19:16:31Z

2

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-10T19:15:53Z

--- language: en thumbnail: http://www.huggingtweets.com/graveyard_plots-hel_ql-witheredstrings/1649618186549/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511852580216967169/b1Aiv2t3_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1457045233783701504/fnjAg6lH_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1332861091119046661/7ZD3Nqqg_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">GHANEM & Anthropos & darth hattie</div> <div style="text-align: center; font-size: 14px;">@graveyard_plots-hel_ql-witheredstrings</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from GHANEM & Anthropos & darth hattie. | Data | GHANEM | Anthropos | darth hattie | | --- | --- | --- | --- | | Tweets downloaded | 413 | 1175 | 1288 | | Retweets | 1 | 354 | 9 | | Short tweets | 18 | 92 | 146 | | Tweets kept | 394 | 729 | 1133 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/26q7h6ze/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @graveyard_plots-hel_ql-witheredstrings's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3vrvcbh4) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3vrvcbh4/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/graveyard_plots-hel_ql-witheredstrings') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nepp1d0/SingleBertSmilesTargetInteraction

nepp1d0

2022-04-10T18:55:03Z

5

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-09T14:05:51Z

Prot_bert finetuned on GPCR_train dataset of Drug Target prediction Trainig paramenters: overwrite_output_dir=True, evaluation_strategy="epoch", learning_rate=1e-3, weight_decay=0.001, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, push_to_hub=True, fp16=True, logging_steps=logging_steps, save_strategy='epoch', num_train_epochs=2

danhsf/xlm-roberta-base-finetuned-panx-de-fr

danhsf

2022-04-10T18:21:26Z

4

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T18:01:12Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - F1: 0.8582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 | | 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 | | 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

V3RX2000/xlm-roberta-base-finetuned-panx-it

V3RX2000

2022-04-10T15:46:48Z

2

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T13:26:51Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.822805578342904 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2323 - F1: 0.8228 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8126 | 1.0 | 70 | 0.3361 | 0.7231 | | 0.2995 | 2.0 | 140 | 0.2526 | 0.8079 | | 0.1865 | 3.0 | 210 | 0.2323 | 0.8228 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

V3RX2000/xlm-roberta-base-finetuned-panx-fr

V3RX2000

2022-04-10T15:39:32Z

3

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T13:08:53Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8354854938789199 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2651 - F1: 0.8355 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5954 | 1.0 | 191 | 0.3346 | 0.7975 | | 0.2689 | 2.0 | 382 | 0.2900 | 0.8347 | | 0.1821 | 3.0 | 573 | 0.2651 | 0.8355 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

abdusah/ft-tatoeba-ar-en

abdusah

2022-04-10T15:34:36Z

15

1

transformers

[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "dataset:open_subtitles", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-04-08T11:49:17Z

--- tags: - translation - generated_from_trainer datasets: - open_subtitles model-index: - name: ft-tatoeba-ar-en results: [] ---  # ft-tatoeba-ar-en This model was trained from scratch on the open_subtitles dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

V3RX2000/xlm-roberta-base-finetuned-panx-de-fr

V3RX2000

2022-04-10T15:31:08Z

2

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T12:45:09Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1667 - F1: 0.8582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2885 | 1.0 | 715 | 0.1817 | 0.8287 | | 0.1497 | 2.0 | 1430 | 0.1618 | 0.8442 | | 0.0944 | 3.0 | 2145 | 0.1667 | 0.8582 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

V3RX2000/xlm-roberta-base-finetuned-panx-de

V3RX2000

2022-04-10T15:13:04Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T10:46:21Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8590909090909091 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1380 - F1: 0.8591 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2642 | 1.0 | 525 | 0.1624 | 0.8251 | | 0.1315 | 2.0 | 1050 | 0.1445 | 0.8508 | | 0.0832 | 3.0 | 1575 | 0.1380 | 0.8591 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

brad1141/baseline_gptv1

brad1141

2022-04-10T13:25:55Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T13:18:14Z

--- license: mit tags: - generated_from_trainer model-index: - name: baseline_gptv1 results: [] ---  # baseline_gptv1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

brad1141/baseline_bertv3

brad1141

2022-04-10T13:16:14Z

8

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T13:09:44Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: baseline_bertv3 results: [] ---  # baseline_bertv3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

laampt/distilbert-base-uncased-finetuned-squad

laampt

2022-04-10T13:15:06Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-10T13:05:51Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

brad1141/baseline_longformerv1

brad1141

2022-04-10T13:01:30Z

4

1

transformers

[ "transformers", "pytorch", "tensorboard", "longformer", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-10T12:37:35Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: baseline_longformerv1 results: [] ---  # baseline_longformerv1 This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7596 - Precision: 0.1333 - Recall: 0.15 - F1: 0.1400 - Accuracy: 0.1400 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 1.8469 | 0.89 | 1 | 1.7596 | 0.1333 | 0.15 | 0.1400 | 0.1400 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

V3RX2000/distilbert-base-uncased-finetuned-emotion

V3RX2000

2022-04-10T12:32:05Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-10T12:24:41Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9245 - name: F1 type: f1 value: 0.9247142990809298 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2285 - Accuracy: 0.9245 - F1: 0.9247 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8812 | 1.0 | 250 | 0.3301 | 0.906 | 0.9035 | | 0.2547 | 2.0 | 500 | 0.2285 | 0.9245 | 0.9247 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

malcolm/TSC_SentimentA_IMDBAmznTSC_2

malcolm

2022-04-10T09:43:32Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-10T07:59:27Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: TSC_SentimentA_IMDBAmznTSC_2 results: [] ---  # TSC_SentimentA_IMDBAmznTSC_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1985 - Accuracy: 0.9365 - F1: 0.9373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Pavithra/codeparrot-ds-sample-gpt-small-10epoch

Pavithra

2022-04-10T07:49:47Z

2

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-08T17:43:51Z

--- license: mit tags: - generated_from_trainer model-index: - name: codeparrot-ds-sample-gpt-small-10epoch results: [] ---  # codeparrot-ds-sample-gpt-small-10epoch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.29 | 0.94 | 1000 | 2.8452 | | 2.3155 | 1.88 | 2000 | 2.3659 | | 1.8817 | 2.82 | 3000 | 2.2085 | | 1.6245 | 3.77 | 4000 | 2.1260 | | 1.4314 | 4.71 | 5000 | 2.0705 | | 1.2698 | 5.65 | 6000 | 2.0603 | | 1.1281 | 6.59 | 7000 | 2.0599 | | 1.0108 | 7.53 | 8000 | 2.0769 | | 0.9167 | 8.47 | 9000 | 2.0870 | | 0.8551 | 9.42 | 10000 | 2.0943 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

jaeyeon/wav2vec2-child-en-tokenizer-4

jaeyeon

2022-04-10T05:28:49Z

6

1

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-04-08T07:33:44Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-child-en-tokenizer-4 results: [] ---  # wav2vec2-child-en-tokenizer-4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4709 - Wer: 0.3769 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0334 | 1.72 | 100 | 1.4709 | 0.3769 | | 0.0332 | 3.45 | 200 | 1.4709 | 0.3769 | | 0.0343 | 5.17 | 300 | 1.4709 | 0.3769 | | 0.032 | 6.9 | 400 | 1.4709 | 0.3769 | | 0.0332 | 8.62 | 500 | 1.4709 | 0.3769 | | 0.0327 | 10.34 | 600 | 1.4709 | 0.3769 | | 0.0331 | 12.07 | 700 | 1.4709 | 0.3769 | | 0.0334 | 13.79 | 800 | 1.4709 | 0.3769 | | 0.0319 | 15.52 | 900 | 1.4709 | 0.3769 | | 0.0338 | 17.24 | 1000 | 1.4709 | 0.3769 | | 0.0321 | 18.97 | 1100 | 1.4709 | 0.3769 | | 0.0367 | 20.69 | 1200 | 1.4709 | 0.3769 | | 0.0331 | 22.41 | 1300 | 1.4709 | 0.3769 | | 0.0332 | 24.14 | 1400 | 1.4709 | 0.3769 | | 0.0347 | 25.86 | 1500 | 1.4709 | 0.3769 | | 0.0319 | 27.59 | 1600 | 1.4709 | 0.3769 | | 0.0302 | 29.31 | 1700 | 1.4709 | 0.3769 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

mojians/E2E-QA-Mining

mojians

2022-04-10T02:34:53Z

22

3

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "question-generation", "question-answer mining", "dataset:squad", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-01T16:03:34Z

--- datasets: - squad tags: - question-generation - question-answer mining widget: - text: "context: The English name 'Normans' comes from the French words Normans/Normanz, plural of Normant, modern French normand, which is itself borrowed from Old Low Franconian Nortmann 'Northman' or directly from Old Norse Norðmaðr, Latinized variously as Nortmannus, Normannus, or Nordmannus (recorded in Medieval Latin, 9th century) to mean 'Norseman, Viking'. generate questions and answers:" inference: parameters: min_length: 50 license: mit --- # Model name ## Model description This model mines the question-answer pairs from a given context in an end2end fashion. It takes a context as an input and generates a list of questions and answers as an output. It is based on a pre-trained `t5-small` model and uses a prompt enigneering technique to train. #### How to use The model takes the context (with prompt) as an input sequence and will generate question-answer pairs as an output sequence. The max sequence length is 512 tokens. Inputs should be organized into the following format: ``` context: context text here. generate questions and answers: ``` The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method. You can try out the demo in the [E2E-QA-mining space app](https://huggingface.co/spaces/mojians/E2E-QA-mining) #### Limitations and bias The model is limited to generating questions in the same style as those found in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), The generated questions can potentially be leading or reflect biases that are present in the context. If the context is too short or completely absent, or if the context and answer do not match, the generated question is likely to be incoherent. ## Training data The model was fine-tuned on a dataset made up of several well-known QA datasets ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)） ## Source and Citation Please find our code and cite us in this repo [https://github.com/jian-mo/E2E-QA-Mining](https://github.com/jian-mo/E2E-QA-Mining)

huggingtweets/rusticgendarme

huggingtweets

2022-04-09T20:23:24Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/rusticgendarme/1649535793480/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1477404220685008896/bEbHFn3g_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">merz▫️▫️▫️▫️</div> <div style="text-align: center; font-size: 14px;">@rusticgendarme</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from merz▫️▫️▫️▫️. | Data | merz▫️▫️▫️▫️ | | --- | --- | | Tweets downloaded | 3220 | | Retweets | 527 | | Short tweets | 613 | | Tweets kept | 2080 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1yxv7eg1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @rusticgendarme's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2eajj2bh) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2eajj2bh/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/rusticgendarme') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

Hodiden/autotrain-TestProj-722121991

Hodiden

2022-04-09T19:21:44Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain", "unk", "dataset:Hodiden/autotrain-data-TestProj", "co2_eq_emissions", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-09T04:53:23Z

--- tags: autotrain language: unk widget: - text: "I love AutoTrain 🤗" datasets: - Hodiden/autotrain-data-TestProj co2_eq_emissions: 8.052949236815056 --- # Model Trained Using AutoTrain - Problem type: Summarization - Model ID: 722121991 - CO2 Emissions (in grams): 8.052949236815056 ## Validation Metrics - Loss: 1.123626708984375 - Rouge1: 56.1275 - Rouge2: 33.5648 - RougeL: 51.986 - RougeLsum: 51.9943 - Gen Len: 13.2823 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Hodiden/autotrain-TestProj-722121991 ```

alexjercan/codebert-base-buggy-token-classification

alexjercan

2022-04-09T16:00:35Z

7

2

transformers

[ "transformers", "pytorch", "roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-04T07:02:54Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: codebert-base-buggy-token-classification results: [] ---  # codebert-base-buggy-token-classification This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5217 - Precision: 0.6942 - Recall: 0.0940 - F1: 0.1656 - Accuracy: 0.7714 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6

damlab/GO-language

damlab

2022-04-09T14:28:07Z

7

1

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "dataset:damlab/uniprot", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-04-08T18:26:38Z

--- license: mit datasets: - damlab/uniprot metrics: - accuracy widget: - text: 'involved_in GO:0006468 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372' example_title: 'Function' --- # GO-Language model ## Table of Contents - [Summary](#model-summary) - [Model Description](#model-description) - [Intended Uses & Limitations](#intended-uses-&-limitations) - [How to Use](#how-to-use) - [Training Data](#training-data) - [Training Procedure](#training-procedure) - [Preprocessing](#preprocessing) - [Training](#training) - [Evaluation Results](#evaluation-results) - [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info) ## Summary This model was built as a way to encode the Gene Ontology definition of a protein as vector representation. It was trained on a collection of gene-ontology terms from model organisms. Each function was sorted by the ID number and combined with its annotation description ie (`is_a`, `enables`, `located_in`, etc). The model is tokenized such that each description and GO term is its own token. This is intended to be used as a translation model between PROT-BERT and GO-Language. That type of translation model will be useful for predicting the function of novel genes. ## Model Description This model was trained using the damlab/uniprot dataset on the `go` field with 256 token chunks and a 15% mask rate. ## Intended Uses & Limitations This model is a useful encapsulation of gene ontology functions. It allows both an exploration of gene-level similarities as well as comparisons between functional terms. ## How to use As this is a BERT-style Masked Language learner, it can be used to determine the most likely token a masked position. ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="damlab/GO-language") unmasker("involved_in [MASK] involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372") [{'score': 0.1040298342704773, 'token': 103, 'token_str': 'GO:0002250', 'sequence': 'involved_in GO:0002250 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.018045395612716675, 'token': 21, 'token_str': 'GO:0005576', 'sequence': 'involved_in GO:0005576 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.015035462565720081, 'token': 50, 'token_str': 'GO:0000139', 'sequence': 'involved_in GO:0000139 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.01181247178465128, 'token': 37, 'token_str': 'GO:0007165', 'sequence': 'involved_in GO:0007165 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'}, {'score': 0.01000668853521347, 'token': 14, 'token_str': 'GO:0005737', 'sequence': 'involved_in GO:0005737 involved_in GO:0007165 located_in GO:0042470 involved_in GO:0070372'} ] ``` ## Training Data The dataset was trained using [damlab/uniprot](https://huggingface.co/datasets/damlab/uniprot) from a random initial model. The Gene Ontology functions were sorted (by ID number) along with annotating term. ## Training Procedure ### Preprocessing All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation. ### Training Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. ## BibTeX Entry and Citation Info [More Information Needed]

Saitomar/Fellowship-Challenge-CV

Saitomar

2022-04-09T14:01:46Z

0

null

[ "tensorboard", "region:us" ]

null

2022-04-09T10:36:13Z

# Fatima's Fellowship Challenge This card contains the model checkpoint, and training metrics of the computer vision coding challenge of the fellowship program. - Epochs : 30 - Batch size : 32 - Learing rate : 0.0005 - Model : ResNet-50 - Optimizer : Adam - Dataset : CIFAR10

edwardjross/xlm-roberta-base-finetuned-recipe-all

edwardjross

2022-04-09T13:19:55Z

324

14

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "arxiv:2004.12184", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-08T14:01:31Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-recipe-all results: [] widget: - text: "1 sheet of frozen puff pastry (thawed)" - text: "1/2 teaspoon fresh thyme, minced" - text: "2-3 medium tomatoes" - text: "1 petit oignon rouge" ---  # xlm-roberta-base-finetuned-recipe-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the recipe ingredient [NER dataset](https://github.com/cosylabiiit/recipe-knowledge-mining) from the paper [A Named Entity Based Approach to Model Recipes](https://arxiv.org/abs/2004.12184) (using both the `gk` and `ar` datasets). It achieves the following results on the evaluation set: - Loss: 0.1169 - F1: 0.9672 On the test set it obtains an F1 of 0.9615, slightly above the CRF used in the paper. ## Model description Predicts tag of each token in an ingredient string. | Tag | Significance | Example | | --- | --- | --- | | NAME | Name of Ingredient | salt, pepper | | STATE | Processing State of Ingredient. | ground, thawed | | UNIT | Measuring unit(s). | gram, cup | | QUANTITY | Quantity associated with the unit(s). | 1, 1 1/2 , 2-4 | | SIZE | Portion sizes mentioned. | small, large | | TEMP | Temperature applied prior to cooking. | hot, frozen | | DF (DRY/FRESH) | Fresh otherwise as mentioned. | dry, fresh | ## Intended uses & limitations * Only trained on ingredient strings. * Tags subtokens; tag should be propagated to whole word * Works best with pre-tokenisation splitting of symbols (such as parentheses) and numbers (e.g. 50g -> 50 g) * Typically only detects the first ingredient if there are multiple. * Only trained on two American English data sources * Tags TEMP and DF have very few training data. ## Training and evaluation data Both the `ar` (AllRecipes.com) and `gk` (FOOD.com) datasets obtained from the TSVs from the authors' [repository](https://github.com/cosylabiiit/recipe-knowledge-mining). ## Training procedure It follows the overall procedure from Chapter 4 of [Natural Language Processing with Transformers](https://www.oreilly.com/library/view/natural-language-processing/9781098103231/) by Tunstall, von Wera and Wolf. See the [training notebook](https://github.com/EdwardJRoss/nlp_transformers_exercises/blob/master/notebooks/ch4-ner-recipe-stanford-crf.ipynb) for details. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2529 | 1.0 | 331 | 0.1303 | 0.9592 | | 0.1164 | 2.0 | 662 | 0.1224 | 0.9640 | | 0.0904 | 3.0 | 993 | 0.1156 | 0.9671 | | 0.0585 | 4.0 | 1324 | 0.1169 | 0.9672 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6

Yu-Ping/BERT-Fake_News_Classifier

Yu-Ping

2022-04-09T13:06:21Z

0

null

[ "bert-base-cased", "text classifier", "PyTorch", "license:apache-2.0", "model-index", "region:us" ]

null

2022-04-09T07:44:23Z

--- language: - en - TW thumbnail: https://colab.research.google.com/drive/1L3PvqNjMF-K_ykztrNEqKhky279EcPaN?usp=sharing tags: - bert-base-cased - text classifier - PyTorch license: apache-2.0 datasets: - True.csv (downloaded from https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) - Fake.csv (downloaded from https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset) metrics: - accuracy - auc model-index: - name: bert-base-cased results: - task: type: fake-news-classifier name: Text Classification dataset: type: news name: Fake and real news metrics: - type: accuracy value: 90.92% ---

tau/tavbert-tr

tau

2022-04-09T12:55:55Z

4

1

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "language model", "tr", "dataset:oscar", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-04-09T12:52:34Z

--- language: tr tags: - roberta - language model datasets: - oscar --- # TavBERT base model A Turkish BERT-style masked language model operating over characters, pre-trained by masking spans of characters, similarly to SpanBERT (Joshi et al., 2020). ### How to use ```python import numpy as np import torch from transformers import AutoModelForMaskedLM, AutoTokenizer model = AutoModelForMaskedLM.from_pretrained("tau/tavbert-tr") tokenizer = AutoTokenizer.from_pretrained("tau/tavbert-tr") def mask_sentence(sent, span_len=5): start_pos = np.random.randint(0, len(sent) - span_len) masked_sent = sent[:start_pos] + '[MASK]' * span_len + sent[start_pos + span_len:] print("Masked sentence:", masked_sent) output = model(**tokenizer.encode_plus(masked_sent, return_tensors='pt'))['logits'][0][1:-1] preds = [int(x) for x in torch.argmax(torch.softmax(output, axis=1), axis=1)[start_pos:start_pos + span_len]] pred_sent = sent[:start_pos] + ''.join(tokenizer.convert_ids_to_tokens(preds)) + sent[start_pos + span_len:] print("Model's prediction:", pred_sent) ``` ## Training data OSCAR (Ortiz, 2019) Turkish section (27 GB text, 77 million sentences).

huggingtweets/notsorobot

huggingtweets

2022-04-09T12:41:46Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-08T16:11:26Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbBT6WF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">3bkreno</div> <div style="text-align: center; font-size: 14px;">@notsorob</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 3bkreno. | Data | 3bkreno | | --- | --- | | Tweets downloaded | 26419 | | Retweets | 111 | | Short tweets | -8796 | | Tweets kept | 8796 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1l7p1yze/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @notsorob's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ypaq5o5y) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ypaq5o5y/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/notsorob') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

jicoc22578/autotrain-livedoor_news-722922024

jicoc22578

2022-04-09T10:47:55Z

3

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "autotrain", "ja", "dataset:jicoc22578/autotrain-data-livedoor_news", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-09T10:33:57Z

--- tags: autotrain language: ja widget: - text: "Windows 11搭載PCを買ったら最低限やっておきたいこと" - text: "3月デスクトップOSシェア、Windowsが増加しMacが減少" - text: "raytrek、Core i7-12700HとRTX 3070 Tiを搭載するノートPC" datasets: - jicoc22578/autotrain-data-livedoor_news co2_eq_emissions: 0.019299491458156143 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 722922024 - CO2 Emissions (in grams): 0.019299491458156143 ## Validation Metrics - Loss: 0.19609540700912476 - Accuracy: 0.9457627118644067 - Macro F1: 0.9404319054946133 - Micro F1: 0.9457627118644067 - Weighted F1: 0.9456037443251943 - Macro Precision: 0.9420917371721244 - Micro Precision: 0.9457627118644067 - Weighted Precision: 0.9457910238180336 - Macro Recall: 0.9391783746329772 - Micro Recall: 0.9457627118644067 - Weighted Recall: 0.9457627118644067 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/jicoc22578/autotrain-livedoor_news-722922024 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("jicoc22578/autotrain-livedoor_news-722922024", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

gdwangh/distilbert-base-uncased-finetuned-cola

gdwangh

2022-04-09T10:39:17Z

14

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-31T14:34:17Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5197669430092784 ---  # 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: 0.6532 - Matthews Correlation: 0.5198 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5228 | 1.0 | 535 | 0.5270 | 0.4212 | | 0.3448 | 2.0 | 1070 | 0.5360 | 0.5073 | | 0.2305 | 3.0 | 1605 | 0.6532 | 0.5198 | | 0.1691 | 4.0 | 2140 | 0.7934 | 0.5171 | | 0.128 | 5.0 | 2675 | 0.8732 | 0.5166 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

Chikashi/t5-small-finetuned-wikihow_3epoch_b4_lr3e-3

Chikashi

2022-04-09T08:34:39Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wikihow", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-08T23:02:19Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wikihow metrics: - rouge model-index: - name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wikihow type: wikihow args: all metrics: - name: Rouge1 type: rouge value: 26.7383 ---  # t5-small-finetuned-wikihow_3epoch_b4_lr3e-3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset. It achieves the following results on the evaluation set: - Loss: 2.3400 - Rouge1: 26.7383 - Rouge2: 10.1981 - Rougel: 22.8642 - Rougelsum: 26.0922 - Gen Len: 18.524 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 3.2548 | 0.13 | 5000 | 2.9708 | 22.0519 | 6.7142 | 18.7677 | 21.4627 | 17.9546 | | 3.1153 | 0.25 | 10000 | 2.9099 | 20.2838 | 5.8365 | 17.5009 | 19.7112 | 18.4981 | | 3.0478 | 0.38 | 15000 | 2.8763 | 22.8282 | 7.3649 | 19.6843 | 22.2312 | 18.1331 | | 3.0146 | 0.51 | 20000 | 2.8484 | 23.2465 | 7.4295 | 19.621 | 22.6246 | 18.5115 | | 2.9572 | 0.64 | 25000 | 2.7902 | 23.8681 | 7.9617 | 20.4984 | 23.2066 | 18.5544 | | 2.9425 | 0.76 | 30000 | 2.7577 | 23.4402 | 7.5289 | 19.7382 | 22.7941 | 18.4613 | | 2.9075 | 0.89 | 35000 | 2.7343 | 23.0082 | 7.5408 | 19.8426 | 22.3832 | 18.1218 | | 2.8705 | 1.02 | 40000 | 2.7136 | 23.9492 | 7.8861 | 20.3675 | 23.3035 | 18.4869 | | 2.7967 | 1.14 | 45000 | 2.6923 | 24.2394 | 8.2895 | 20.7275 | 23.6127 | 18.3486 | | 2.7794 | 1.27 | 50000 | 2.6639 | 24.4062 | 8.2481 | 20.8957 | 23.8077 | 18.4258 | | 2.7776 | 1.4 | 55000 | 2.6321 | 24.6213 | 8.4161 | 21.0528 | 23.968 | 18.351 | | 2.7397 | 1.53 | 60000 | 2.6116 | 24.16 | 8.3605 | 20.618 | 23.5037 | 18.6049 | | 2.7199 | 1.65 | 65000 | 2.5846 | 24.2606 | 8.3829 | 20.6274 | 23.6252 | 18.4742 | | 2.7044 | 1.78 | 70000 | 2.5663 | 25.0452 | 8.896 | 21.4554 | 24.4748 | 18.3143 | | 2.6928 | 1.91 | 75000 | 2.5365 | 25.1312 | 9.008 | 21.6376 | 24.4963 | 18.5605 | | 2.6281 | 2.03 | 80000 | 2.5209 | 25.5311 | 9.1521 | 21.729 | 24.8864 | 18.2597 | | 2.5333 | 2.16 | 85000 | 2.4860 | 25.4834 | 9.2969 | 21.7257 | 24.8802 | 18.3831 | | 2.5308 | 2.29 | 90000 | 2.4619 | 26.0526 | 9.605 | 22.2178 | 25.4353 | 18.4235 | | 2.5136 | 2.42 | 95000 | 2.4356 | 25.9434 | 9.6537 | 22.2957 | 25.312 | 18.4647 | | 2.4801 | 2.54 | 100000 | 2.4098 | 26.1109 | 9.7637 | 22.3844 | 25.4771 | 18.5765 | | 2.4494 | 2.67 | 105000 | 2.3835 | 26.332 | 9.9472 | 22.4243 | 25.6933 | 18.5985 | | 2.4393 | 2.8 | 110000 | 2.3590 | 26.6896 | 10.2248 | 22.8743 | 26.0665 | 18.4883 | | 2.4071 | 2.93 | 115000 | 2.3400 | 26.7383 | 10.1981 | 22.8642 | 26.0922 | 18.524 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

nielsr/segformer-test-v6

nielsr

2022-04-09T08:21:01Z

8

0

transformers

[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "dataset:segments/sidewalk-semantic", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-segmentation

2022-04-09T07:53:39Z

--- license: apache-2.0 tags: - vision - image-segmentation datasets: - segments/sidewalk-semantic widget: - src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg example_title: Brugge ---

nikhedward/bart-large-cnn-finetuned-multi-news1

nikhedward

2022-04-09T04:51:07Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:multi_news", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-09T02:56:22Z

--- license: mit tags: - generated_from_trainer datasets: - multi_news metrics: - rouge model-index: - name: bart-large-cnn-finetuned-multi-news1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: multi_news type: multi_news args: default metrics: - name: Rouge1 type: rouge value: 42.1215 ---  # bart-large-cnn-finetuned-multi-news1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 2.0858 - Rouge1: 42.1215 - Rouge2: 14.9986 - Rougel: 23.4737 - Rougelsum: 36.4212 - Gen Len: 133.703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.1984 | 1.0 | 750 | 2.0858 | 42.1215 | 14.9986 | 23.4737 | 36.4212 | 133.703 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

avialfont/dummy-finetuned-amazon-en-es

avialfont

2022-04-09T03:35:50Z

4

0

transformers

[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-08T15:20:54Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: avialfont/dummy-finetuned-amazon-en-es results: [] ---  # avialfont/dummy-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.6755 - Validation Loss: 3.8033 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 3627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2942 | 4.4915 | 0 | | 6.2878 | 3.9207 | 1 | | 5.6755 | 3.8033 | 2 | ### Framework versions - Transformers 4.16.2 - TensorFlow 2.8.0 - Datasets 1.18.3 - Tokenizers 0.11.6

MeerAnwar/CodingChallengeFatimaFellowship

MeerAnwar

2022-04-09T01:35:28Z

0

null

[ "region:us" ]

null

2022-04-08T14:35:45Z

# 1. Deep Learning for Vision </p> Upside down detector: Train a model to detect if images are upside down * Pick a dataset of natural images (we suggest looking at datasets on the Hugging Face Hub) * Synthetically turn some of the images upside down. Create a training and test set. * Build a neural network (using TensorFlow, PyTorch, or any framework you like) * Train it to classify image orientation until a reasonable accuracy is reached * Upload the model to the Hugging Face Hub, and add a link to your model below. * Look at some of the images that were classified incorrectly. Please explain what you might do to improve your model performance on these images in the future (you do not need to implement these suggestions)

nateraw/test-save-keras-sequential

nateraw

2022-04-08T20:15:54Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2022-04-08T19:07:35Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>

lysandre/test-save-keras-sequential

lysandre

2022-04-08T20:01:39Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2022-04-08T19:32:35Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training Metrics Model history needed

bmichele/poetry-generation-nextline-mbart-gut-en-single

bmichele

2022-04-08T19:13:43Z

4

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-08T18:46:39Z

# poetry-generation-nextline-mbart-gut-en-single * `nextline`: generates a poem line from previous line(s) * `mbart`: base model is [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) * `gut`: trained on Project Gutenberg data * `en`: English language * `single`: uses only last poem line as input for generation

nateraw/autoencoder-keras-mnist-demo-new

nateraw

2022-04-08T18:37:12Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2022-04-08T18:37:04Z

--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ## Training Metrics Model history needed ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>

krinal214/augmented_Squad_Translated

krinal214

2022-04-08T18:15:59Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-08T15:58:32Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: augmented_Squad_Translated results: [] ---  # augmented_Squad_Translated This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1154 | 1.0 | 10835 | 0.5251 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.1 - Datasets 1.18.4 - Tokenizers 0.11.6

MaRiOrOsSi/t5-base-finetuned-question-answering

MaRiOrOsSi

2022-04-08T18:00:14Z

1,273

32

transformers

[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "Generative Question Answering", "en", "dataset:duorc", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-04-08T07:36:44Z

--- language: en datasets: - duorc widget: - text: "question: Is Giacomo Italian? context: Giacomo is 25 years old and he was born in Tuscany" - text: "question: Where does Christian come from? context: Christian is a student of UNISI but he come from Caserta" - text: "question: Is the dog coat grey? context: You have a beautiful dog with a brown coat" tags: - Generative Question Answering --- # T5 for Generative Question Answering This model is the result produced by Christian Di Maio and Giacomo Nunziati for the Language Processing Technologies exam. Reference for [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) fine-tuned on [DuoRC](https://huggingface.co/datasets/duorc) for **Generative Question Answering** by just prepending the *question* to the *context*. ## Code The code used for T5 training is available at this [repository](https://github.com/nunziati/bert-vs-t5-for-question-answering/blob/main/train_t5_selfrc.py). ## Results The results are evaluated on: - DuoRC/SelfRC -> Test Subset - DuoRC/ParaphraseRC -> Test Subset - SQUADv1 -> Validation Subset Removing all tokens not related to dictionary words from the evaluation metrics. The model used as reference is BERT finetuned on SQUAD v1. | Model | SelfRC | ParaphraseRC | SQUAD |--|--|--|--| | T5-BASE-FINETUNED | **F1**: 49.00 **EM**: 31.38 | **F1**: 28.75 **EM**: 15.18 | **F1**: 63.28 **EM**: 37.24 | | BERT-BASE-FINETUNED | **F1**: 47.18 **EM**: 30.76 | **F1**: 21.20 **EM**: 12.62 | **F1**: 77.19 **EM**: 57.81 | ## How to use it 🚀 ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline model_name = "MaRiOrOsSi/t5-base-finetuned-question-answering" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelWithLMHead.from_pretrained(model_name) question = "What is 42?" context = "42 is the answer to life, the universe and everything" input = f"question: {question} context: {context}" encoded_input = tokenizer([input], return_tensors='pt', max_length=512, truncation=True) output = model.generate(input_ids = encoded_input.input_ids, attention_mask = encoded_input.attention_mask) output = tokenizer.decode(output[0], skip_special_tokens=True) print(output) ``` ## Citation Created by [Christian Di Maio](https://it.linkedin.com/in/christiandimaio) and [Giacomo Nunziati](https://it.linkedin.com/in/giacomo-nunziati-b19572185) > Made with <span style="color: #e25555;">&hearts;</span> in Italy

caush/TestMeanFraction2

caush

2022-04-08T17:51:14Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "camembert", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T17:26:33Z

--- license: mit tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: TestMeanFraction2 results: [] ---  # TestMeanFraction2 This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3967 - Matthews Correlation: 0.2537 ## Model description More information needed ## Intended uses & limitations "La panique totale" Cette femme trouve une énorme araignée suspendue à sa douche. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 0.13 | 50 | 1.1126 | 0.1589 | | No log | 0.25 | 100 | 1.0540 | 0.1884 | | No log | 0.38 | 150 | 1.1533 | 0.0818 | | No log | 0.51 | 200 | 1.0676 | 0.1586 | | No log | 0.64 | 250 | 0.9949 | 0.2280 | | No log | 0.76 | 300 | 1.0343 | 0.2629 | | No log | 0.89 | 350 | 1.0203 | 0.2478 | | No log | 1.02 | 400 | 1.0041 | 0.2752 | | No log | 1.15 | 450 | 1.0808 | 0.2256 | | 1.023 | 1.27 | 500 | 1.0029 | 0.2532 | | 1.023 | 1.4 | 550 | 1.0204 | 0.2508 | | 1.023 | 1.53 | 600 | 1.1377 | 0.1689 | | 1.023 | 1.65 | 650 | 1.0499 | 0.2926 | | 1.023 | 1.78 | 700 | 1.0441 | 0.2474 | | 1.023 | 1.91 | 750 | 1.0279 | 0.2611 | | 1.023 | 2.04 | 800 | 1.1511 | 0.2804 | | 1.023 | 2.16 | 850 | 1.2381 | 0.2512 | | 1.023 | 2.29 | 900 | 1.3340 | 0.2385 | | 1.023 | 2.42 | 950 | 1.4372 | 0.2842 | | 0.7325 | 2.54 | 1000 | 1.3967 | 0.2537 | | 0.7325 | 2.67 | 1050 | 1.4272 | 0.2624 | | 0.7325 | 2.8 | 1100 | 1.3869 | 0.1941 | | 0.7325 | 2.93 | 1150 | 1.4983 | 0.2063 | | 0.7325 | 3.05 | 1200 | 1.4959 | 0.2409 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0a0+0aef44c - Datasets 2.0.0 - Tokenizers 0.11.6

leonadase/bert-base-chinese-finetuned-ner-v1

leonadase

2022-04-08T17:49:01Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:fdner", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-08T13:26:02Z

--- tags: - generated_from_trainer datasets: - fdner metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-chinese-finetuned-ner-v1 results: - task: name: Token Classification type: token-classification dataset: name: fdner type: fdner args: fdner metrics: - name: Precision type: precision value: 0.981203007518797 - name: Recall type: recall value: 0.9886363636363636 - name: F1 type: f1 value: 0.9849056603773584 - name: Accuracy type: accuracy value: 0.9909536373916321 ---  # bert-base-chinese-finetuned-ner-v1 This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the fdner dataset. It achieves the following results on the evaluation set: - Loss: 0.0413 - Precision: 0.9812 - Recall: 0.9886 - F1: 0.9849 - Accuracy: 0.9910 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 8 | 2.0640 | 0.0 | 0.0 | 0.0 | 0.4323 | | No log | 2.0 | 16 | 1.7416 | 0.0204 | 0.0227 | 0.0215 | 0.5123 | | No log | 3.0 | 24 | 1.5228 | 0.0306 | 0.0265 | 0.0284 | 0.5456 | | No log | 4.0 | 32 | 1.2597 | 0.0961 | 0.1591 | 0.1198 | 0.6491 | | No log | 5.0 | 40 | 1.0273 | 0.1588 | 0.2159 | 0.1830 | 0.7450 | | No log | 6.0 | 48 | 0.8026 | 0.2713 | 0.3258 | 0.2960 | 0.8208 | | No log | 7.0 | 56 | 0.6547 | 0.36 | 0.4091 | 0.3830 | 0.8513 | | No log | 8.0 | 64 | 0.5180 | 0.4650 | 0.5038 | 0.4836 | 0.8873 | | No log | 9.0 | 72 | 0.4318 | 0.5139 | 0.5606 | 0.5362 | 0.9067 | | No log | 10.0 | 80 | 0.3511 | 0.6169 | 0.6894 | 0.6512 | 0.9291 | | No log | 11.0 | 88 | 0.2887 | 0.6691 | 0.6894 | 0.6791 | 0.9414 | | No log | 12.0 | 96 | 0.2396 | 0.7042 | 0.7576 | 0.7299 | 0.9516 | | No log | 13.0 | 104 | 0.2052 | 0.7568 | 0.8371 | 0.7950 | 0.9587 | | No log | 14.0 | 112 | 0.1751 | 0.8303 | 0.8712 | 0.8503 | 0.9610 | | No log | 15.0 | 120 | 0.1512 | 0.8464 | 0.8977 | 0.8713 | 0.9668 | | No log | 16.0 | 128 | 0.1338 | 0.8759 | 0.9091 | 0.8922 | 0.9710 | | No log | 17.0 | 136 | 0.1147 | 0.8959 | 0.9129 | 0.9043 | 0.9746 | | No log | 18.0 | 144 | 0.1011 | 0.9326 | 0.9432 | 0.9379 | 0.9761 | | No log | 19.0 | 152 | 0.0902 | 0.9251 | 0.9356 | 0.9303 | 0.9795 | | No log | 20.0 | 160 | 0.0806 | 0.9440 | 0.9583 | 0.9511 | 0.9804 | | No log | 21.0 | 168 | 0.0743 | 0.9586 | 0.9659 | 0.9623 | 0.9812 | | No log | 22.0 | 176 | 0.0649 | 0.9511 | 0.9583 | 0.9547 | 0.9851 | | No log | 23.0 | 184 | 0.0595 | 0.9591 | 0.9773 | 0.9681 | 0.9876 | | No log | 24.0 | 192 | 0.0537 | 0.9625 | 0.9735 | 0.9680 | 0.9883 | | No log | 25.0 | 200 | 0.0505 | 0.9701 | 0.9848 | 0.9774 | 0.9894 | | No log | 26.0 | 208 | 0.0464 | 0.9737 | 0.9811 | 0.9774 | 0.9904 | | No log | 27.0 | 216 | 0.0439 | 0.9737 | 0.9811 | 0.9774 | 0.9906 | | No log | 28.0 | 224 | 0.0428 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | | No log | 29.0 | 232 | 0.0417 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | | No log | 30.0 | 240 | 0.0413 | 0.9812 | 0.9886 | 0.9849 | 0.9910 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

malcolm/TSC_finetuning-sentiment-movie-model

malcolm

2022-04-08T16:44:47Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T14:33:40Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: TSC_finetuning-sentiment-movie-model results: [] ---  # TSC_finetuning-sentiment-movie-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1480 - Accuracy: 0.9578 - F1: 0.9757 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

huggingtweets/lilpeeplyric

huggingtweets

2022-04-08T15:15:13Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-08T15:14:31Z

--- language: en thumbnail: http://www.huggingtweets.com/lilpeeplyric/1649430909105/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1445263525878902787/yW8p2-e__400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">lil peep lyrics bot</div> <div style="text-align: center; font-size: 14px;">@lilpeeplyric</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from lil peep lyrics bot. | Data | lil peep lyrics bot | | --- | --- | | Tweets downloaded | 3250 | | Retweets | 0 | | Short tweets | 0 | | Tweets kept | 3250 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jgq3lf6/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lilpeeplyric's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1lbjza1d) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1lbjza1d/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/lilpeeplyric') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

nielsr/segformer-test-v5

nielsr

2022-04-08T15:05:50Z

6

0

transformers

[ "transformers", "pytorch", "segformer", "vision", "image-segmentation", "dataset:segments/sidewalk-semantic", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-segmentation

2022-04-08T14:51:17Z

--- license: apache-2.0 tags: - vision - image-segmentation datasets: - segments/sidewalk-semantic widget: - src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg example_title: Brugge ---

xaqren/sentiment_analysis

xaqren

2022-04-08T14:59:55Z

9

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "exbert", "en", "dataset:Confidential", "arxiv:1810.04805", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-05T13:46:58Z

--- language: en tags: - exbert license: apache-2.0 datasets: - Confidential --- # BERT base model (uncased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference between english and English. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model description [xaqren/sentiment_analysis] This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text-classification.

iceboy95/SqueezeNet_VisionQ1_20220512

iceboy95

2022-04-08T14:15:01Z

0

null

[ "license:afl-3.0", "region:us" ]

null

2022-04-08T14:09:42Z

--- license: afl-3.0 --- ## Description SqueezeNet from PyTorch-zoo, pretrained with ImageNet and fine-tuned with scenic dataset from kaggle https://www.kaggle.com/datasets/arnaud58/landscape-pictures ## Results Trained with 8K samples, tested with 120++ non-overlapping samples. Accuracy: 0.978261 f1-score: 0.978417

philschmid/MiniLMv2-L6-H384-sst2

philschmid

2022-04-08T13:56:53Z

4

1

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T13:54:14Z

--- tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: MiniLMv2-L6-H384-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9197247706422018 ---  # MiniLMv2-L6-H384-sst2 This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2532 - Accuracy: 0.9197 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5787 | 1.0 | 264 | 0.3496 | 0.8624 | | 0.3413 | 2.0 | 528 | 0.2599 | 0.8991 | | 0.2716 | 3.0 | 792 | 0.2651 | 0.9048 | | 0.2343 | 4.0 | 1056 | 0.2532 | 0.9197 | | 0.2165 | 5.0 | 1320 | 0.2636 | 0.9151 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

profoz/mlops-demo

profoz

2022-04-08T13:56:10Z

8

1

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "classification", "sequence-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- language: - en tags: - classification - sequence-classification license: apache-2.0 --- Github repository [here](https://github.com/sinanuozdemir/oreilly-transformers-nlp)

gymball/FatimaFellowship-UpsideDown

gymball

2022-04-08T12:18:42Z

0

null

[ "Image Classification", "en", "dataset:cifar100", "license:unlicense", "region:us" ]

null

2022-04-07T16:18:53Z

--- language: - en tags: - Image Classification license: unlicense datasets: - cifar100 --- This repo contains a model that is capable of detecting upside images. This is part of my submission for the Fatima Fellowship Selection Task.

srmukundb/distilbert-base-uncased-finetuned-squad

srmukundb

2022-04-08T12:08:02Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad_v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-08T00:45:45Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: distilbert-base-uncased-finetuned-squad results: [] ---  # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4104 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2182 | 1.0 | 8235 | 1.2318 | | 0.9451 | 2.0 | 16470 | 1.2693 | | 0.7554 | 3.0 | 24705 | 1.4104 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

ybelkada/japanese-roberta-question-answering

ybelkada

2022-04-08T11:38:39Z

171

1

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "extractive-qa", "ja", "dataset:SkelterLabsInc/JaQuAD", "license:cc-by-sa-3.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-08T08:52:22Z

--- license: cc-by-sa-3.0 language: ja tags: - question-answering - extractive-qa pipeline_tag: - None datasets: - SkelterLabsInc/JaQuAD metrics: - Exact match - F1 score --- # RoBERTa base Japanese - JaQuAD ## Description A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD). Please refer [RoBERTa base Japanese](https://huggingface.co/rinna/japanese-roberta-base) for details about the pre-training model. The codes for the fine-tuning are available [on this notebook](https://huggingface.co/ybelkada/japanese-roberta-question-answering/blob/main/roberta_ja_qa.ipynb) ## Usage ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer question = 'アレクサンダー・グラハム・ベルは、どこで生まれたの?' context = 'アレクサンダー・グラハム・ベルは、スコットランド生まれの科学者、発明家、工学者である。世界初の>実用的電話の発明で知られている。' model = AutoModelForQuestionAnswering.from_pretrained( 'ybelkada/japanese-roberta-question-answering') tokenizer = AutoTokenizer.from_pretrained( 'ybelkada/japanese-roberta-question-answering') inputs = tokenizer( question, context, add_special_tokens=True, return_tensors="pt") input_ids = inputs["input_ids"].tolist()[0] outputs = model(**inputs) answer_start_scores = outputs.start_logits answer_end_scores = outputs.end_logits # Get the most likely beginning of answer with the argmax of the score. answer_start = torch.argmax(answer_start_scores) # Get the most likely end of answer with the argmax of the score. # 1 is added to `answer_end` because the index pointed by score is inclusive. answer_end = torch.argmax(answer_end_scores) + 1 answer = tokenizer.convert_tokens_to_string( tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])) # answer = 'スコットランド' ``` ## License The fine-tuned model is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license. ## Miscellaneous The Q&A widget does not work on that model. Tried also with ```Pipeline``` and I was able to reproduce the error, needs a further investigation

huggingtweets/emarobot

huggingtweets

2022-04-08T11:13:49Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-04-08T11:12:40Z

--- language: en thumbnail: http://www.huggingtweets.com/emarobot/1649416424059/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1317183233495388160/nLbBT6WF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">3bkreno</div> <div style="text-align: center; font-size: 14px;">@emarobot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 3bkreno. | Data | 3bkreno | | --- | --- | | Tweets downloaded | 970 | | Retweets | 111 | | Short tweets | 129 | | Tweets kept | 841 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/mfd65acm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @emarobot's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1i5j7avt) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1i5j7avt/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/emarobot') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

marcosfp/distilbert-base-uncased-finetuned-objectivity-rotten

marcosfp

2022-04-08T11:10:02Z

11

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T10:59:03Z

--- license: gpl-3.0 --- Objectivity sentence classification model based on **distilbert-base-uncased-finetuned-sst-2-english**. It was fine-tuned with Rotten-IMDB movie review [data](http://www.cs.cornell.edu/people/pabo/movie-review-data/) using extracted sentences from film plots as objective examples and review comments as subjective language examples. With a test set of 5%, we obtained an accuracy of 96% and f1 of the same value. Please, feel free to try the demo online with subjective language examples like "I think...", "I believe...", and more objective claims. For any further comments contact me, at [email protected].

afbudiman/distilled-indobert-classification

afbudiman

2022-04-08T09:32:57Z

9

1

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:indonlu", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T06:49:31Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - indonlu metrics: - accuracy - f1 model-index: - name: distilled-indobert-classification results: - task: name: Text Classification type: text-classification dataset: name: indonlu type: indonlu args: smsa metrics: - name: Accuracy type: accuracy value: 0.9015873015873016 - name: F1 type: f1 value: 0.9014926755197933 ---  # distilled-indobert-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. It achieves the following results on the evaluation set: - Loss: 0.6015 - Accuracy: 0.9016 - F1: 0.9015 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0427 | 1.0 | 688 | 0.6306 | 0.8683 | 0.8684 | | 0.5332 | 2.0 | 1376 | 0.5621 | 0.8794 | 0.8779 | | 0.3021 | 3.0 | 2064 | 0.6785 | 0.8905 | 0.8896 | | 0.1851 | 4.0 | 2752 | 0.6085 | 0.8968 | 0.8959 | | 0.1152 | 5.0 | 3440 | 0.6015 | 0.9016 | 0.9015 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6

btjiong/robbert-twitter-sentiment-custom

btjiong

2022-04-08T08:17:25Z

5

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:dutch_social", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-07T18:07:19Z

--- license: mit tags: - generated_from_trainer datasets: - dutch_social metrics: - accuracy - f1 - precision - recall model-index: - name: robbert-twitter-sentiment-custom results: - task: name: Text Classification type: text-classification dataset: name: dutch_social type: dutch_social args: dutch_social metrics: - name: Accuracy type: accuracy value: 0.788 - name: F1 type: f1 value: 0.7878005279207152 - name: Precision type: precision value: 0.7877102066609215 - name: Recall type: recall value: 0.788 ---  # robbert-twitter-sentiment-custom This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the dutch_social dataset. It achieves the following results on the evaluation set: - Loss: 0.6656 - Accuracy: 0.788 - F1: 0.7878 - Precision: 0.7877 - Recall: 0.788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.8287 | 1.0 | 282 | 0.7178 | 0.7007 | 0.6958 | 0.6973 | 0.7007 | | 0.4339 | 2.0 | 564 | 0.5873 | 0.7667 | 0.7668 | 0.7681 | 0.7667 | | 0.2045 | 3.0 | 846 | 0.6656 | 0.788 | 0.7878 | 0.7877 | 0.788 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0+cpu - Datasets 2.0.0 - Tokenizers 0.11.6

philschmid/roberta-large-sst2

philschmid

2022-04-08T08:03:59Z

148

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-04-08T07:27:49Z

--- license: mit tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: roberta-large-sst2 results: - task: name: Text Classification type: text-classification dataset: name: GLUE SST2 type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9644495412844036 ---  # roberta-large-sst2 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.1400 - Accuracy: 0.9644 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: sagemaker_data_parallel - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3688 | 1.0 | 264 | 0.1444 | 0.9564 | | 0.1529 | 2.0 | 528 | 0.1502 | 0.9518 | | 0.107 | 3.0 | 792 | 0.1388 | 0.9530 | | 0.0666 | 4.0 | 1056 | 0.1400 | 0.9644 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2+cu113 - Datasets 1.18.4 - Tokenizers 0.11.6

sail/poolformer_s24

sail

2022-04-08T07:48:50Z

137

1

transformers

[ "transformers", "pytorch", "poolformer", "image-classification", "vision", "dataset:imagenet", "arxiv:2111.11418", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:05Z

--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # PoolFormer (S24 model) PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sail-sg/poolformer). ## Model description PoolFormer is a model that replaces attention token mixer in transfomrers with extremely simple operator, pooling. Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only the most basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 48%/60% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=sail/poolformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import PoolFormerFeatureExtractor, PoolFormerForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = PoolFormerFeatureExtractor.from_pretrained('sail/poolformer_s24') model = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s24') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. ## Training data The poolformer model was trained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/sail-sg/poolformer/blob/main/train.py#L529-L572). ### Pretraining The model was trained on TPU-v3s. Training resolution is 224. For all hyperparameters (such as batch size and learning rate), please refer to the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | # params | URL | |---------------------------------------|-------------------------|----------|------------------------------------------------------------------| | PoolFormer-S12 | 77.2 | 12M | https://huggingface.co/sail/poolformer_s12 | | **PoolFormer-S24** | **80.3** | **21M** | **https://huggingface.co/sail/poolformer_s24** | | PoolFormer-S36 | 81.4 | 31M | https://huggingface.co/sail/poolformer_s36 | | PoolFormer-M36 | 82.1 | 56M | https://huggingface.co/sail/poolformer_m36 | | PoolFormer-M48 | 82.5 | 73M | https://huggingface.co/sail/poolformer_m48 | ### BibTeX entry and citation info ```bibtex @article{yu2021metaformer, title={MetaFormer is Actually What You Need for Vision}, author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, journal={arXiv preprint arXiv:2111.11418}, year={2021} } ```