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 2025-07-16 06:27:54	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-16 06:27:41	card stringlengths 11 1.01M
sofia425/khipu-finetuned-amazon_reviews_multi	sofia425	2023-03-22T17:52:41Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T17:47:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9085 --- <!-- 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. --> # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Accuracy: 0.9085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2305 \| 1.0 \| 63 \| 0.2953 \| 0.895 \| \| 0.196 \| 2.0 \| 126 \| 0.2836 \| 0.9085 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
mathichpp/khipu-finetuned-amazon_reviews_multi	mathichpp	2023-03-22T17:52:05Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T17:48:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9025 --- <!-- 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. --> # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2815 - Accuracy: 0.9025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.254 \| 1.0 \| 63 \| 0.2662 \| 0.9067 \| \| 0.2024 \| 2.0 \| 126 \| 0.2815 \| 0.9025 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
brianlorenzo/TALLER-IA-Comentarios-De-Amazon	brianlorenzo	2023-03-22T17:52:04Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T17:48:25Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: TALLER-IA-Comentarios-De-Amazon results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.90375 --- <!-- 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. --> # TALLER-IA-Comentarios-De-Amazon This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3000 - Accuracy: 0.9038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2229 \| 1.0 \| 63 \| 0.2589 \| 0.908 \| \| 0.2068 \| 2.0 \| 126 \| 0.3000 \| 0.9038 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
jp9999/house	jp9999	2023-03-22T17:51:47Z	0	0	null	[ "region:us" ]	null	2023-03-22T17:50:47Z	create a house on a 700 sqm lot with 8 car garage and a pool on the second floor, 5 bedrooms garden
leinho/khipu-finetuned-amazon_reviews_multi	leinho	2023-03-22T17:51:46Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T17:47:00Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.90725 --- <!-- 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. --> # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2864 - Accuracy: 0.9073 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2609 \| 1.0 \| 63 \| 0.2640 \| 0.905 \| \| 0.1918 \| 2.0 \| 126 \| 0.2864 \| 0.9073 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
maleperezt/khipu-finetuned-amazon_reviews_multi	maleperezt	2023-03-22T17:50:43Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T17:46:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9055 --- <!-- 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. --> # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3014 - Accuracy: 0.9055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2226 \| 1.0 \| 63 \| 0.2641 \| 0.9085 \| \| 0.1862 \| 2.0 \| 126 \| 0.3014 \| 0.9055 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
sd-concepts-library/ahx-beta-41b373e	sd-concepts-library	2023-03-22T17:48:15Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-03-22T17:48:14Z	--- license: mit --- ### ahx-beta-41b373e on Stable Diffusion This is the `<ahx-beta-41b373e>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<ahx-beta-41b373e> 0](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/6.jpeg) ![<ahx-beta-41b373e> 1](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/5.jpeg) ![<ahx-beta-41b373e> 2](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/2.jpeg) ![<ahx-beta-41b373e> 3](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/0.jpeg) ![<ahx-beta-41b373e> 4](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/3.jpeg) ![<ahx-beta-41b373e> 5](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/4.jpeg) ![<ahx-beta-41b373e> 6](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/1.jpeg)
facebook/esmfold_v1	facebook	2023-03-22T17:39:28Z	8,997,176	27	transformers	[ "transformers", "pytorch", "esm", "license:mit", "endpoints_compatible", "region:us" ]	null	2022-11-01T18:24:14Z	--- license: mit --- # ESMFold ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the [accompanying paper](https://www.science.org/doi/10.1126/science.ade2574). If you're interested in using ESMFold in practice, please check out the associated [tutorial notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb).
nguyenvulebinh/mbart-large-50-latin-only	nguyenvulebinh	2023-03-22T17:30:12Z	14	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "mbart-50", "multilingual", "en", "arxiv:2008.00401", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-22T17:19:04Z	--- language: - multilingual - en license: mit tags: - mbart-50 --- # mBART-50 mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. ## Model description mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning. Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously. mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages. The pre-training objective is explained below. Multilingual Denoising Pretraining: The model incorporates N languages by concatenating data: `D = {D1, ..., DN }` where each Di is a collection of monolingual documents in language `i`. The source documents are noised using two schemes, first randomly shuffling the original sentences' order, and second a novel in-filling scheme, where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text. 35% of each instance's words are masked by random sampling a span length according to a Poisson distribution `(λ = 3.5)`. The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict the sentence. ## Checking ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained('facebook/mbart-large-50') tokenizer = AutoTokenizer.from_pretrained('facebook/mbart-large-50') src_text = "UN Chief Says There Is <mask> Military Solution <mask> Syria" encoded_hi = tokenizer(src_text, return_tensors="pt") generated_output = model.generate(encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"], return_dict_in_generate=True, return_dict=True, output_hidden_states=True) text_output = tokenizer.batch_decode(generated_output.sequences, skip_special_tokens=True) new_model = AutoModelForSeq2SeqLM.from_pretrained('nguyenvulebinh/mbart-large-50-latin-only') new_tokenizer = AutoTokenizer.from_pretrained('nguyenvulebinh/mbart-large-50-latin-only') new_encoded_hi = new_tokenizer(src_text, return_tensors="pt") new_generated_output = new_model.generate(new_encoded_hi, forced_bos_token_id=new_tokenizer.lang_code_to_id["en_XX"], return_dict_in_generate=True, return_dict=True, output_hidden_states=True) new_text_output = new_tokenizer.batch_decode(new_generated_output.sequences, skip_special_tokens=True) assert text_output == new_text_output assert torch.equal(generated_output.encoder_hidden_states[-1], new_generated_output.encoder_hidden_states[-1]) assert torch.equal(generated_output.decoder_hidden_states[-1][-1], new_generated_output.decoder_hidden_states[-1][-1]) print(new_text_output) # ['UN Chief Says There Is No Military Solution to the War in Syria'] ``` ## Languages covered English (en_XX) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
AnaniyaX/decision-distilbert-uncased	AnaniyaX	2023-03-22T17:24:16Z	9	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "dataset:textvqa", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T19:21:09Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AnaniyaX/decision-distilbert-uncased results: [] datasets: - textvqa - squad widget: - text: 'What does the sign says' example_title: 'Visual Question Example 1' - text: 'What does string theory talks about' example_title: 'Textual Question Example 1' --- <!-- 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. --> # AnaniyaX/decision-distilbert-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on textvqa and squad. It achieves the following results on the evaluation set: - Train Loss: 0.0097 - Train Accuracy: 0.9976 - Epoch: 9 ## Model description The Text-Visual Question Classifier is a Hugging Face model that can classify questions as either text-based or visual-based. It uses a natural language processing and techniques to analyze the question and determine its type. The model has been trained on a large dataset of questions labeled as either text-based or visual-based, and has achieved high accuracy in identifying the correct type of question. ## Intended uses & limitations #### Applications This model can be used in various applications such as chatbots, virtual assistants, search engines, and recommendation systems. For example, it can help chatbots to provide more accurate responses by understanding the type of question being asked. It can also help search engines to retrieve more relevant results by filtering out irrelevant content based on the type of question. #### Limitations: The model may not perform well on questions that are ambiguous or have multiple interpretations. It may also be biased towards certain types of questions based on the training data. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-06, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Epoch \| \|:----------:\|:--------------:\|:-----:\| \| 0.1914 \| 0.9444 \| 0 \| \| 0.0711 \| 0.9768 \| 1 \| \| 0.0531 \| 0.9826 \| 2 \| \| 0.0427 \| 0.9868 \| 3 \| \| 0.0330 \| 0.9904 \| 4 \| \| 0.0264 \| 0.9923 \| 5 \| \| 0.0195 \| 0.9947 \| 6 \| \| 0.0149 \| 0.9960 \| 7 \| \| 0.0123 \| 0.9965 \| 8 \| \| 0.0097 \| 0.9976 \| 9 \| ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
livingbody/FlyingDunhuang	livingbody	2023-03-22T17:21:59Z	0	0	null	[ "paddlepaddle", "stable-diffusion", "stable-diffusion-ppdiffusers", "text-to-image", "ppdiffusers", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-03-22T14:09:49Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of FlyingDunhuang tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false --- # LoRA DreamBooth - livingbody/FlyingDunhuang 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of FlyingDunhuang 文本进行了训练。
system-technologies/biogpt	system-technologies	2023-03-22T17:10:41Z	13	0	transformers	[ "transformers", "pytorch", "biogpt", "text-generation", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-03-22T16:15:05Z	--- language: en license: mit widget: - text: COVID-19 is duplicated_from: microsoft/biogpt --- ## BioGPT Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> from transformers import BioGptTokenizer, BioGptForCausalLM >>> model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") >>> tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> set_seed(42) >>> generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True) [{'generated_text': 'COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population'}, {'generated_text': 'COVID-19 is one of the largest viral epidemics in the world.'}, {'generated_text': 'COVID-19 is a common condition affecting an estimated 1.1 million people in the United States alone.'}, {'generated_text': 'COVID-19 is a pandemic, the incidence has been increased in a manner similar to that in other'}, {'generated_text': 'COVID-19 is transmitted via droplets, air-borne, or airborne transmission.'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BioGptTokenizer, BioGptForCausalLM tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(encoded_input) ``` Beam-search decoding: ```python import torch from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") sentence = "COVID-19 is" inputs = tokenizer(sentence, return_tensors="pt") set_seed(42) with torch.no_grad(): beam_output = model.generate(inputs, min_length=100, max_length=1024, num_beams=5, early_stopping=True ) tokenizer.decode(beam_output[0], skip_special_tokens=True) 'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK), and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and more than 800,000 deaths.' ``` ## Citation If you find BioGPT useful in your research, please cite the following paper: ```latex @article{10.1093/bib/bbac409, author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan}, title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}", journal = {Briefings in Bioinformatics}, volume = {23}, number = {6}, year = {2022}, month = {09}, abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}", issn = {1477-4054}, doi = {10.1093/bib/bbac409}, url = {https://doi.org/10.1093/bib/bbac409}, note = {bbac409}, eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf}, } ```
TRiddle/ppo-SnowballTarget	TRiddle	2023-03-22T16:56:16Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-03-22T16:56:10Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: TRiddle/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jfforero/distilbert-base-uncased-finetuned-imdb	jfforero	2023-03-22T16:55:37Z	3	0	transformers	[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-03-22T12:58:39Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: jfforero/distilbert-base-uncased-finetuned-imdb 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. --> # jfforero/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8449 - Validation Loss: 2.5443 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.8449 \| 2.5443 \| 0 \| ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
Geotrend/bert-base-bg-cased	Geotrend	2023-03-22T16:53:29Z	21	0	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "bg", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- language: bg datasets: wikipedia license: apache-2.0 --- # bert-base-bg-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-bg-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-bg-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
sd-concepts-library/ahx-beta-41b2a57	sd-concepts-library	2023-03-22T16:53:05Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-03-22T16:53:03Z	--- license: mit --- ### ahx-beta-41b2a57 on Stable Diffusion This is the `<ahx-beta-41b2a57>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<ahx-beta-41b2a57> 0](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/0.jpeg) ![<ahx-beta-41b2a57> 1](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/3.jpeg) ![<ahx-beta-41b2a57> 2](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/2.jpeg) ![<ahx-beta-41b2a57> 3](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/5.jpeg) ![<ahx-beta-41b2a57> 4](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/4.jpeg) ![<ahx-beta-41b2a57> 5](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/1.jpeg) ![<ahx-beta-41b2a57> 6](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/6.jpeg)
socialmediaie/TRAC2020_ENG_B_bert-base-uncased	socialmediaie	2023-03-22T16:39:22Z	15	0	transformers	[ "transformers", "pytorch", "jax", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020 Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying. Our trained models as well as evaluation metrics during traing are available at: https://databank.illinois.edu/datasets/IDB-8882752# We also make a few of our models available in HuggingFace's models repository at https://huggingface.co/socialmediaie/, these models can be further fine-tuned on your dataset of choice. Our approach is described in our paper titled: > Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020 NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper. If you plan to use the dataset please cite the following resources: * Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). * Mishra, Shubhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Trained Models for Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8882752_V1. ``` @inproceedings{Mishra2020TRAC, author = {Mishra, Sudhanshu and Prasad, Shivangi and Mishra, Shubhanshu}, booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020)}, title = {{Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}}, year = {2020} } @data{illinoisdatabankIDB-8882752, author = {Mishra, Shubhanshu and Prasad, Shivangi and Mishra, Shubhanshu}, doi = {10.13012/B2IDB-8882752_V1}, publisher = {University of Illinois at Urbana-Champaign}, title = {{Trained models for Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}}, url = {https://doi.org/10.13012/B2IDB-8882752{\_}V1}, year = {2020} } ``` ## Usage The models can be used via the following code: ```python from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification import torch from pathlib import Path from scipy.special import softmax import numpy as np import pandas as pd TASK_LABEL_IDS = { "Sub-task A": ["OAG", "NAG", "CAG"], "Sub-task B": ["GEN", "NGEN"], "Sub-task C": ["OAG-GEN", "OAG-NGEN", "NAG-GEN", "NAG-NGEN", "CAG-GEN", "CAG-NGEN"] } model_version="databank" # other option is hugging face library if model_version == "databank": # Make sure you have downloaded the required model file from https://databank.illinois.edu/datasets/IDB-8882752 # Unzip the file at some model_path (we are using: "databank_model") model_path = next(Path("databank_model").glob(".//output//model")) # Assuming you get the following type of structure inside "databank_model" # 'databank_model/ALL/Sub-task C/output/bert-base-multilingual-uncased/model' lang, task, _, base_model, _ = model_path.parts tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForSequenceClassification.from_pretrained(model_path) else: lang, task, base_model = "ALL", "Sub-task C", "bert-base-multilingual-uncased" base_model = f"socialmediaie/TRAC2020_{lang}_{lang.split()[-1]}_{base_model}" tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForSequenceClassification.from_pretrained(base_model) # For doing inference set model in eval mode model.eval() # If you want to further fine-tune the model you can reset the model to model.train() task_labels = TASK_LABEL_IDS[task] sentence = "This is a good cat and this is a bad dog." processed_sentence = f"{tokenizer.cls_token} {sentence}" tokens = tokenizer.tokenize(sentence) indexed_tokens = tokenizer.convert_tokens_to_ids(tokens) tokens_tensor = torch.tensor([indexed_tokens]) with torch.no_grad(): logits, = model(tokens_tensor, labels=None) logits preds = logits.detach().cpu().numpy() preds_probs = softmax(preds, axis=1) preds = np.argmax(preds_probs, axis=1) preds_labels = np.array(task_labels)[preds] print(dict(zip(task_labels, preds_probs[0])), preds_labels) """You should get an output as follows: ({'CAG-GEN': 0.06762535, 'CAG-NGEN': 0.03244293, 'NAG-GEN': 0.6897794, 'NAG-NGEN': 0.15498641, 'OAG-GEN': 0.034373745, 'OAG-NGEN': 0.020792078}, array(['NAG-GEN'], dtype='<U8')) """ ```
TheAbyssYouSee/QW5pbWVsaWsyRA	TheAbyssYouSee	2023-03-22T16:35:14Z	0	23	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-21T07:22:16Z	--- license: creativeml-openrail-m ---
agucci/my-model	agucci	2023-03-22T16:29:04Z	0	0	sklearn	[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]	tabular-classification	2023-03-22T16:21:05Z	--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: example.pkl widget: structuredData: area error: - 30.29 - 96.05 - 48.31 compactness error: - 0.01911 - 0.01652 - 0.01484 concave points error: - 0.01037 - 0.0137 - 0.01093 concavity error: - 0.02701 - 0.02269 - 0.02813 fractal dimension error: - 0.003586 - 0.001698 - 0.002461 mean area: - 481.9 - 1130.0 - 748.9 mean compactness: - 0.1058 - 0.1029 - 0.1223 mean concave points: - 0.03821 - 0.07951 - 0.08087 mean concavity: - 0.08005 - 0.108 - 0.1466 mean fractal dimension: - 0.06373 - 0.05461 - 0.05796 mean perimeter: - 81.09 - 123.6 - 101.7 mean radius: - 12.47 - 18.94 - 15.46 mean smoothness: - 0.09965 - 0.09009 - 0.1092 mean symmetry: - 0.1925 - 0.1582 - 0.1931 mean texture: - 18.6 - 21.31 - 19.48 perimeter error: - 2.497 - 5.486 - 3.094 radius error: - 0.3961 - 0.7888 - 0.4743 smoothness error: - 0.006953 - 0.004444 - 0.00624 symmetry error: - 0.01782 - 0.01386 - 0.01397 texture error: - 1.044 - 0.7975 - 0.7859 worst area: - 677.9 - 1866.0 - 1156.0 worst compactness: - 0.2378 - 0.2336 - 0.2394 worst concave points: - 0.1015 - 0.1789 - 0.1514 worst concavity: - 0.2671 - 0.2687 - 0.3791 worst fractal dimension: - 0.0875 - 0.06589 - 0.08019 worst perimeter: - 96.05 - 165.9 - 124.9 worst radius: - 14.97 - 24.86 - 19.26 worst smoothness: - 0.1426 - 0.1193 - 0.1546 worst symmetry: - 0.3014 - 0.2551 - 0.2837 worst texture: - 24.64 - 26.58 - 26.0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> \| Hyperparameter \| Value \| \|--------------------------\|---------\| \| ccp_alpha \| 0.0 \| \| class_weight \| \| \| criterion \| gini \| \| max_depth \| \| \| max_features \| \| \| max_leaf_nodes \| \| \| min_impurity_decrease \| 0.0 \| \| min_impurity_split \| \| \| min_samples_leaf \| 1 \| \| min_samples_split \| 2 \| \| min_weight_fraction_leaf \| 0.0 \| \| random_state \| \| \| splitter \| best \| </details> ### Model Plot The model plot is below. <style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class="sk-top-container"><div class="sk-container"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="12a6e028-23d4-4552-93d7-bca81eacd271" type="checkbox" checked><label class="sk-toggleable__label" for="12a6e028-23d4-4552-93d7-bca81eacd271">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. \| Metric \| Value \| \|----------\|----------\| \| accuracy \| 0.935673 \| \| f1 score \| 0.935673 \| # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. BibTeX: ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2020}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is not ready to be used in production. # model_description This is a DecisionTreeClassifier model trained on breast cancer dataset. # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average. # confusion_matrix ![confusion_matrix](confusion_matrix.png)
jamesportis/vit-base-patch16-224-finetuned-flower	jamesportis	2023-03-22T16:16:44Z	21	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-03-22T16:09:11Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower 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. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2
whyoke/tumore_test	whyoke	2023-03-22T16:16:42Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	null	2023-03-22T15:14:24Z	--- license: other tags: - generated_from_trainer model-index: - name: tumore_test 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. --> # tumore_test This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0 - eval_mean_iou: nan - eval_mean_accuracy: nan - eval_overall_accuracy: nan - eval_per_category_iou: [nan] - eval_per_category_accuracy: [nan] - eval_runtime: 338.6408 - eval_samples_per_second: 1.161 - eval_steps_per_second: 0.582 - epoch: 1.27 - step: 2000 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.10.1 - Tokenizers 0.13.2
arrandi/ppo-LunarLander-v2	arrandi	2023-03-22T16:11:34Z	1	0	stable-baselines3	[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-12-16T12:55:37Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.45 +/- 21.16 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Handun/xlm-roberta-base-finetuned-panx-de-fr	Handun	2023-03-22T16:11:25Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-03-22T11:05:54Z	--- 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: 0.1634 - F1: 0.8588 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2889 \| 1.0 \| 715 \| 0.1818 \| 0.8338 \| \| 0.1435 \| 2.0 \| 1430 \| 0.1624 \| 0.8531 \| \| 0.0933 \| 3.0 \| 2145 \| 0.1634 \| 0.8588 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Mahmoud22/AraClassificationModel2	Mahmoud22	2023-03-22T16:07:23Z	3	0	transformers	[ "transformers", "pytorch", "electra", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T16:06:39Z	--- tags: - generated_from_trainer model-index: - name: result 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. --> # result This model is a fine-tuned version of [Mahmoud22/AraClassificationModel](https://huggingface.co/Mahmoud22/AraClassificationModel) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0295 - F1-macro: 0.9856 ## 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: 8e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-macro \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1818 \| 1.0 \| 1630 \| 0.0996 \| 0.9661 \| \| 0.0899 \| 2.0 \| 3260 \| 0.0398 \| 0.9837 \| \| 0.0326 \| 3.0 \| 4890 \| 0.0218 \| 0.9893 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
alespalla/distillbert_conv_quality_score	alespalla	2023-03-22T15:57:44Z	17	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "en", "dataset:conv_ai_2", "doi:10.57967/hf/0435", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-11T16:39:18Z	--- license: apache-2.0 tags: - transformers - pytorch datasets: - conv_ai_2 model-index: - name: distillbert_conv_quality_score results: [] language: - en --- # distillbert_conv_quality_score This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conv_ai_2 dataset. It was trained to generate a score (in the [0, 1] range) from a conversation It achieves the following results on the evaluation set: - training/loss: 0.0165 - validation/loss: 0.0149 ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "alespalla/distillbert_conv_quality_score" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) conversation = ''' Q: Begin A: lol ! do you think it is strange to feel like you have been through life before ? Q: Hellow A: I don't understand you 🙈. Also, try to guess: i like to ... Q: How are you? A: make time stop, funny you :) Q: What is your name? A: jessie. hows your day going ? 😃 ''' score = model(**tokenizer(conversation, return_tensors='pt')).logits.item() print(f"Score: {score}") ``` ## Training and evaluation data The training data was generated from `conv_ai_2` using the following function ```python from datasets import load_dataset def get_dataset(regression=False): db = load_dataset("conv_ai_2") def generate_converation(elem): text = "" for idx, txt in enumerate(elem["dialog"]): if idx % 2: text += f"A: {txt['text']}\n" else: text += f"Q: {txt['text']}\n" if regression: return {'text': text, "labels": (elem['eval_score'] - 1)/4} return {'text': text, "labels": elem['eval_score'] - 1} db = db.filter(lambda example: example["eval_score"] > 0) db = db.map(generate_converation, remove_columns=db['train'].column_names) db = db['train'].train_test_split(test_size=0.2).shuffle(42) return db ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - epochs: 40 - batch_size: 16 - learning_rate: 0.0002 - eval_steps: 82 - log_steps: 82 - save_steps: 41 - gradient_accumulation_steps: 1 - warmup_steps: 0 ### Training results \| step \| training/loss \| validation/loss \| \|:----:\|:-------------:\|:---------------:\| \| 81 \| 0.1020 \| 0.0794 \| \| 163 \| 0.0800 \| 0.0713 \| \| 245 \| 0.0553 \| 0.0491 \| \| 327 \| 0.0362 \| 0.0440 \| \| 409 \| 0.0282 \| 0.0352 \| \| 491 \| 0.0282 \| 0.0412 \| \| 573 \| 0.0256 \| 0.0293 \| \| 655 \| 0.0238 \| 0.0252 \| \| 737 \| 0.0175 \| 0.0226 \| \| 819 \| 0.0154 \| 0.0228 \| \| 901 \| 0.0116 \| 0.0205 \| \| 983 \| 0.0160 \| 0.0202 \| \| 1065 \| 0.0146 \| 0.0240 \| \| 1147 \| 0.0182 \| 0.0180 \| \| 1229 \| 0.0171 \| 0.0192 \| \| 1311 \| 0.0091 \| 0.0174 \| \| 1393 \| 0.0171 \| 0.0158 \| \| 1475 \| 0.0137 \| 0.0158 \| \| 1557 \| 0.0158 \| 0.0148 \| \| 1639 \| 0.0165 \| 0.0149 \| ### Framework versions - Transformers 4.26.1 - Datasets 2.10.1 - Tokenizers 0.13.2
pabloac31/rl_course_vizdoom_health_gathering_supreme	pabloac31	2023-03-22T15:48:41Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T15:48:33Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.34 +/- 4.87 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r pabloac31/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
LorenzoDeMattei/GePpeTto	LorenzoDeMattei	2023-03-22T15:39:46Z	3,547	13	transformers	[ "transformers", "pytorch", "jax", "safetensors", "gpt2", "text-generation", "it", "arxiv:2004.14253", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:04Z	--- language: it --- # GePpeTto GPT2 Model 🇮🇹 Pretrained GPT2 117M model for Italian. You can find further details in the paper: Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: https://arxiv.org/abs/2004.14253 ## Pretraining Corpus The pretraining set comprises two main sources. The first one is a dump of Italian Wikipedia (November 2019), consisting of 2.8GB of text. The second one is the ItWac corpus (Baroni et al., 2009), which amounts to 11GB of web texts. This collection provides a mix of standard and less standard Italian, on a rather wide chronological span, with older texts than the Wikipedia dump (the latter stretches only to the late 2000s). ## Pretraining details This model was trained using GPT2's Hugging Face implemenation on 4 NVIDIA Tesla T4 GPU for 620k steps. Training parameters: - GPT-2 small configuration - vocabulary size: 30k - Batch size: 32 - Block size: 100 - Adam Optimizer - Initial learning rate: 5e-5 - Warm up steps: 10k ## Perplexity scores \| Domain \| Perplexity \| \|---\|---\| \| Wikipedia \| 26.1052 \| \| ItWac \| 30.3965 \| \| Legal \| 37.2197 \| \| News \| 45.3859 \| \| Social Media \| 84.6408 \| For further details, qualitative analysis and human evaluation check out: https://arxiv.org/abs/2004.14253 ## Load Pretrained Model You can use this model by installing Huggingface library `transformers`. And you can use it directly by initializing it like this: ```python from transformers import GPT2Tokenizer, GPT2Model model = GPT2Model.from_pretrained('LorenzoDeMattei/GePpeTto') tokenizer = GPT2Tokenizer.from_pretrained( 'LorenzoDeMattei/GePpeTto', ) ``` ## Example using GPT2LMHeadModel ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, GPT2Tokenizer tokenizer = AutoTokenizer.from_pretrained("LorenzoDeMattei/GePpeTto") model = AutoModelWithLMHead.from_pretrained("LorenzoDeMattei/GePpeTto") text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer) prompts = [ "Wikipedia Geppetto", "Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso"] samples_outputs = text_generator( prompts, do_sample=True, max_length=50, top_k=50, top_p=0.95, num_return_sequences=3 ) for i, sample_outputs in enumerate(samples_outputs): print(100 * '-') print("Prompt:", prompts[i]) for sample_output in sample_outputs: print("Sample:", sample_output['generated_text']) print() ``` Output is, ``` ---------------------------------------------------------------------------------------------------- Prompt: Wikipedia Geppetto Sample: Wikipedia Geppetto rosso (film 1920) Geppetto rosso ("The Smokes in the Black") è un film muto del 1920 diretto da Henry H. Leonard. Il film fu prodotto dalla Selig Poly Sample: Wikipedia Geppetto Geppetto ("Geppetto" in piemontese) è un comune italiano di 978 abitanti della provincia di Cuneo in Piemonte. L'abitato, che si trova nel versante valtellinese, si sviluppa nella Sample: Wikipedia Geppetto di Natale (romanzo) Geppetto di Natale è un romanzo di Mario Caiano, pubblicato nel 2012. ---------------------------------------------------------------------------------------------------- Prompt: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso. Il burattino riesce a scappare. Dopo aver trovato un prezioso sacchetto si reca Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso, e l'unico che lo possiede, ma, di fronte a tutte queste prove Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso: - A voi gli occhi, le guance! A voi il mio pezzo! ``` ## Citation Please use the following bibtex entry: ``` @misc{mattei2020geppetto, title={GePpeTto Carves Italian into a Language Model}, author={Lorenzo De Mattei and Michele Cafagna and Felice Dell'Orletta and Malvina Nissim and Marco Guerini}, year={2020}, eprint={2004.14253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## References Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky wide web: a collection of very large linguistically processed webcrawled corpora. Language resources and evaluation, 43(3):209–226.
duyduong9htv/phobert-qa-finetuned-viet-qa	duyduong9htv	2023-03-22T15:28:44Z	33	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	question-answering	2022-09-19T22:23:22Z	--- tags: - generated_from_trainer model-index: - name: phobert-qa-finetuned-viet-qa 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. --> # phobert-qa-finetuned-viet-qa This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5288 ## 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.6004 \| 1.0 \| 2027 \| 1.5128 \| \| 1.3018 \| 2.0 \| 4054 \| 1.4657 \| \| 1.1052 \| 3.0 \| 6081 \| 1.4754 \| \| 0.9502 \| 4.0 \| 8108 \| 1.5288 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa	vocabtrimmer	2023-03-22T15:24:09Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "question answering", "fr", "dataset:lmqg/qg_frquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-21T19:02:06Z	--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: fr datasets: - lmqg/qg_frquad pipeline_tag: text2text-generation tags: - question answering widget: - text: "question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu." example_title: "Question Answering Example 1" - text: "question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l'électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D'un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu'être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire." example_title: "Question Answering Example 2" model-index: - name: vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_frquad type: default args: default metrics: - name: BLEU4 (Question Answering) type: bleu4_question_answering value: 14.81 - name: ROUGE-L (Question Answering) type: rouge_l_question_answering value: 26.59 - name: METEOR (Question Answering) type: meteor_question_answering value: 20.09 - name: BERTScore (Question Answering) type: bertscore_question_answering value: 88.11 - name: MoverScore (Question Answering) type: moverscore_question_answering value: 69.17 - name: AnswerF1Score (Question Answering) type: answer_f1_score__question_answering value: 40.01 - name: AnswerExactMatch (Question Answering) type: answer_exact_match_question_answering value: 23.71 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - Language model: [vocabtrimmer/mt5-small-trimmed-fr-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000) - Language: fr - Training data: [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - Online Demo: [https://autoqg.net/](https://autoqg.net/) - Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa") # model prediction answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa") output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` ## Evaluation - *Metric (Question Answering)*: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json) \| \| Score \| Type \| Dataset \| \|:-----------------\|--------:\|:--------\|:-----------------------------------------------------------------\| \| AnswerExactMatch \| 23.71 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| AnswerF1Score \| 40.01 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| BERTScore \| 88.11 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_1 \| 23.93 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_2 \| 19.95 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_3 \| 17.18 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_4 \| 14.81 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| METEOR \| 20.09 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| MoverScore \| 69.17 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| ROUGE_L \| 26.59 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-fr-120000 - max_length: 512 - max_length_output: 32 - epoch: 27 - batch: 32 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
cardiffnlp/xlm-roberta-base-tweet-sentiment-de	cardiffnlp	2023-03-22T14:57:00Z	17	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-06T09:14:30Z	# `cardiffnlp/xlm-roberta-base-tweet-sentiment-de` This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (german). Following metrics are computed on the `test` split of [cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(german). \| \| eval_f1_micro \| eval_recall_micro \| eval_precision_micro \| eval_f1_macro \| eval_recall_macro \| eval_precision_macro \| eval_accuracy \| \|---:\|----------------:\|--------------------:\|-----------------------:\|----------------:\|--------------------:\|-----------------------:\|----------------:\| \| 0 \| 73.22 \| 73.22 \| 73.22 \| 73.18 \| 73.22 \| 73.18 \| 73.22 \| Check the result file [here](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-de/raw/main/eval.json).
JessicaHsu/rl_course_vizdoom_health_gathering_supreme	JessicaHsu	2023-03-22T14:47:26Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T14:47:17Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.08 +/- 5.10 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r JessicaHsu/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
eolang/DRL-vizdoome_health_gathering_supreme	eolang	2023-03-22T14:39:05Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T07:15:06Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 18.61 +/- 4.52 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r eolang/DRL-vizdoome_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
helenai/wav2vec2-base-superb-ks-jpqd-ov	helenai	2023-03-22T14:31:21Z	4	0	transformers	[ "transformers", "openvino", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-03-09T08:14:06Z	--- license: apache-2.0 tags: - audio-classification - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: jpqd-wav2vec2-base-ft-keyword-spotting 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. --> # jpqd-wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset, using [superb/wav2vec2-base-superb-ks](https://huggingface.co/superb/wav2vec2-base-superb-ks) as a teacher model It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the JPQD image classification example. It achieves the following results on the evaluation set: - Loss: 0.5632 - Accuracy: 0.9756 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 12.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.2245 \| 1.0 \| 399 \| 2.2351 \| 0.6209 \| \| 6.9856 \| 2.0 \| 798 \| 7.0597 \| 0.7354 \| \| 10.013 \| 3.0 \| 1197 \| 9.8779 \| 0.8069 \| \| 11.3484 \| 4.0 \| 1596 \| 11.1949 \| 0.8719 \| \| 11.6849 \| 5.0 \| 1995 \| 11.5479 \| 0.9014 \| \| 11.5921 \| 6.0 \| 2394 \| 11.4193 \| 0.9495 \| \| 0.8911 \| 7.0 \| 2793 \| 0.7334 \| 0.9500 \| \| 0.8965 \| 8.0 \| 3192 \| 0.6553 \| 0.9685 \| \| 0.7198 \| 9.0 \| 3591 \| 0.6213 \| 0.9669 \| \| 0.7372 \| 10.0 \| 3990 \| 0.5929 \| 0.9675 \| \| 0.7004 \| 11.0 \| 4389 \| 0.5720 \| 0.9721 \| \| 0.6195 \| 12.0 \| 4788 \| 0.5632 \| 0.9756 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
JessicaHsu/ppo-CartPole-v1	JessicaHsu	2023-03-22T14:11:45Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T13:52:25Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -138.56 +/- 89.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': '__file__' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 80000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'JessicaHsu/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
Piemag/FirstPPO-LunarLander-v2	Piemag	2023-03-22T14:06:45Z	4	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T14:06:25Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.69 +/- 19.67 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
aubmindlab/araelectra-base-generator	aubmindlab	2023-03-22T13:55:02Z	64	2	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "safetensors", "electra", "fill-mask", "ar", "arxiv:1406.2661", "arxiv:2012.15516", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: ar datasets: - wikipedia - Osian - 1.5B-Arabic-Corpus - oscar-arabic-unshuffled - Assafir(private) widget: - text: " عاصمة لبنان هي [MASK] ." --- # AraELECTRA <img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/> ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). AraELECTRA achieves state-of-the-art results on Arabic QA dataset. For a detailed description, please refer to the AraELECTRA paper [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://arxiv.org/abs/2012.15516). ## How to use the generator in `transformers` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="aubmindlab/araelectra-base-generator", tokenizer="aubmindlab/araelectra-base-generator" ) print( fill_mask(" عاصمة لبنان هي [MASK] .) ) ``` # Preprocessing It is recommended to apply our preprocessing function before training/testing on any dataset. Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data `pip install arabert` ```python from arabert.preprocess import ArabertPreprocessor model_name="aubmindlab/araelectra-base" arabert_prep = ArabertPreprocessor(model_name=model_name) text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري" arabert_prep.preprocess(text) >>> output: ولن نبالغ إذا قلنا : إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري ``` # Model Model \| HuggingFace Model Name \| Size (MB/Params)\| ---\|:---:\|:---: AraELECTRA-base-generator \| [araelectra-base-generator](https://huggingface.co/aubmindlab/araelectra-base-generator) \| 227MB/60M \| AraELECTRA-base-discriminator \| [araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) \| 516MB/135M \| # Compute Model \| Hardware \| num of examples (seq len = 512) \| Batch Size \| Num of Steps \| Time (in days) ---\|:---:\|:---:\|:---:\|:---:\|:---: AraELECTRA-base \| TPUv3-8 \| - \| 256 \| 2M \| 24 # Dataset The pretraining data used for the new AraELECTRA model is also used for AraGPT2 and AraELECTRA. The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation) For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled: - OSCAR unshuffled and filtered. - [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01 - [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4) - [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619) - Assafir news articles. Huge thank you for Assafir for giving us the data # TensorFlow 1.x models You can find the PyTorch, TF2 and TF1 models in HuggingFace's Transformer Library under the ```aubmindlab``` username - `wget https://huggingface.co/aubmindlab/MODEL_NAME/resolve/main/tf1_model.tar.gz` where `MODEL_NAME` is any model under the `aubmindlab` name # If you used this model please cite us as : ``` @inproceedings{antoun-etal-2021-araelectra, title = "{A}ra{ELECTRA}: Pre-Training Text Discriminators for {A}rabic Language Understanding", author = "Antoun, Wissam and Baly, Fady and Hajj, Hazem", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Virtual)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.wanlp-1.20", pages = "191--195", } ``` # Acknowledgments Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT. # Contacts Wissam Antoun: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) \| [Twitter](https://twitter.com/wissam_antoun) \| [Github](https://github.com/WissamAntoun) \| <[email protected]> \| <[email protected]> Fady Baly: [Linkedin](https://www.linkedin.com/in/fadybaly/) \| [Twitter](https://twitter.com/fadybaly) \| [Github](https://github.com/fadybaly) \| <[email protected]> \| <[email protected]>
pszemraj/pegasus-large-summary-explain	pszemraj	2023-03-22T13:53:07Z	18	4	transformers	[ "transformers", "pytorch", "safetensors", "pegasus", "text2text-generation", "summarization", "en", "dataset:kmfoda/booksum", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-03-02T23:29:05Z	--- language: - en license: apache-2.0 tags: - summarization - pegasus datasets: - kmfoda/booksum metrics: - rouge widget: - text: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock. example_title: earthquakes - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).' example_title: scientific paper - text: ' the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics.' example_title: data science textbook - text: 'Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer-based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT-like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT-like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.' example_title: bigbird blog intro inference: parameters: max_length: 64 no_repeat_ngram_size: 2 encoder_no_repeat_ngram_size: 3 repetition_penalty: 2.4 length_penalty: 0.5 num_beams: 4 early_stopping: true model-index: - name: pszemraj/pegasus-large-summary-explain results: - task: type: summarization name: Summarization dataset: name: kmfoda/booksum type: kmfoda/booksum config: kmfoda--booksum split: test metrics: - type: rouge value: 29.1023 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhNjg4YTFlODU5MmVjNGVmNDRmMjQ4M2YyZGNmMWRlYjBhZmVhMTY3ZTUxNDkzNjY0OGVmNWJlNmY1OTkzNCIsInZlcnNpb24iOjF9.E_rVKqB7WEerLeRq6JIVTLZ1TgmsThFQJVKh11WH1qWa-cL3766psPWDKe8mK3lNkjmwbiDW0DZlDt4dm2ATCA - type: rouge value: 6.2441 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVmZmFlOTgwN2Q3ZWRkZGVkMzU1ZDRkYzU1MWMzMTk1NDM5YTU0MzFjNDljNmZlY2I2NjZmZjcyYjBkZGExZCIsInZlcnNpb24iOjF9.QnuGoMWX8cq5_ukRtiaLRLau_F9XiCjg313GC7Iu1VGK8Kj_9lzU43377VsH0fBWooA1zJjtIK0UA-YpGQQOAA - type: rouge value: 14.7503 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzJhNzE0YjZiZWQ4NDE1Yjg3ZGJjY2ZmYWEwYzU5MTRhYWNiNTcyODU1NzM5NTZhNjNlNmYwNDVlYmZmYjkxOCIsInZlcnNpb24iOjF9.m5BLUMefXa1KivIIE9-gYKYq5aRRbfpQWazqzXxfCsqqp38Lt0ymk6OwXSlQyB_5oksNHIDFKpJX4wjYx2i7Bw - type: rouge value: 27.2375 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTY1OTIxMzBkMGJiZmNiNjZjYmQ2MjUwMjBkYTg5Zjc1NjVlZjllNTg0MDM1NTdhZDJlZmIwOTczOGNkZDc5YyIsInZlcnNpb24iOjF9.bThI16mvqhEuGBhdao0w8j03vv9G9Quy-ITRZzalr41zOour9it4oxEPFCvmPf-nLCQkqgWKUDEzgr6Ww8qgBg - type: loss value: 2.979011058807373 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGM0NzM3YTI4Njg4NDY0ZjQzNTZmYTIxYzcxNDBlNzAwNTAxNDE4MTZjYmZmNzYwODU0OWQ1ZjM5YjRmMmFkZiIsInZlcnNpb24iOjF9.EPEP53AoqHz0rjVGStJI2dM7ivxFmOj572I3llWdAoejm3zO1Iq5WDArYsqOse_oLxYCgcqPmNVc5IcLW9x7Dg - type: gen_len value: 467.269 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjgzYzU2ZjkwN2RhNzJlZmQyZTBlYmUxMTZhNzg0ODMwMjA3OTUzNTIwOWFkZWVmNjVmMTJiZmZhNWFmY2UzZCIsInZlcnNpb24iOjF9.RW5tzk2fcc_m4bgaSopRDFhSR9R8hRaYKrstXH4X5iGP_Xwvhy5Q7-igd2ACnlxIfmtdTmMxLMsvHr5oAZEwDg --- # pszemraj/pegasus-large-summary-explain This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs. It achieves the following results on the evaluation set: - eval_loss: 1.1193 - eval_runtime: 6.6754 - eval_samples_per_second: 27.714 - eval_steps_per_second: 1.798 - epoch: 3.0 - step: 900 A 1-epoch checkpoint can be found at [pszemraj/pegasus-large-book-summary](https://huggingface.co/pszemraj/pegasus-large-book-summary), which is where the second training session started from. ## Model description - After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/booksum) dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of just more compact. - This quality (anecdotally) is favourable for learning/comprehension because summarization datasets that simply make the information more compact (* cough * arXiv) can be so dense that the overall time spent trying to _comprehend_ what it is saying can be the same as just reading the original material. ## Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 4 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0
RafaelEiji/bert_character	RafaelEiji	2023-03-22T13:43:23Z	3	1	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-31T14:02:02Z	--- tags: - generated_from_trainer model-index: - name: from_scratch 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. --> # from_scratch This model is a fine-tuned version of [tokenizer/config.json](https://huggingface.co/tokenizer/config.json) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4744 ## 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: 360 - eval_batch_size: 360 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-------:\|:---------------:\| \| 1.0952 \| 0.05 \| 20000 \| 1.0383 \| \| 0.936 \| 0.1 \| 40000 \| 0.8852 \| \| 0.8679 \| 0.14 \| 60000 \| 0.8207 \| \| 0.8276 \| 0.19 \| 80000 \| 0.7796 \| \| 0.796 \| 0.24 \| 100000 \| 0.7519 \| \| 0.7756 \| 0.29 \| 120000 \| 0.7299 \| \| 0.7545 \| 0.33 \| 140000 \| 0.7103 \| \| 0.7395 \| 0.38 \| 160000 \| 0.6947 \| \| 0.7236 \| 0.43 \| 180000 \| 0.6809 \| \| 0.7143 \| 0.48 \| 200000 \| 0.6705 \| \| 0.705 \| 0.52 \| 220000 \| 0.6585 \| \| 0.6904 \| 0.57 \| 240000 \| 0.6479 \| \| 0.6835 \| 0.62 \| 260000 \| 0.6388 \| \| 0.672 \| 0.67 \| 280000 \| 0.6290 \| \| 0.665 \| 0.72 \| 300000 \| 0.6217 \| \| 0.6581 \| 0.76 \| 320000 \| 0.6136 \| \| 0.6466 \| 0.81 \| 340000 \| 0.6071 \| \| 0.6396 \| 0.86 \| 360000 \| 0.6000 \| \| 0.6343 \| 0.91 \| 380000 \| 0.5940 \| \| 0.6286 \| 0.95 \| 400000 \| 0.5880 \| \| 0.6183 \| 1.0 \| 420000 \| 0.5809 \| \| 0.6134 \| 1.05 \| 440000 \| 0.5757 \| \| 0.6094 \| 1.1 \| 460000 \| 0.5693 \| \| 0.6032 \| 1.15 \| 480000 \| 0.5641 \| \| 0.5954 \| 1.19 \| 500000 \| 0.5596 \| \| 0.5915 \| 1.24 \| 520000 \| 0.5532 \| \| 0.5845 \| 1.29 \| 540000 \| 0.5489 \| \| 0.5823 \| 1.34 \| 560000 \| 0.5437 \| \| 0.5754 \| 1.38 \| 580000 \| 0.5393 \| \| 0.573 \| 1.43 \| 600000 \| 0.5345 \| \| 0.5643 \| 1.48 \| 620000 \| 0.5309 \| \| 0.5627 \| 1.53 \| 640000 \| 0.5262 \| \| 0.56 \| 1.57 \| 660000 \| 0.5220 \| \| 0.5554 \| 1.62 \| 680000 \| 0.5186 \| \| 0.5507 \| 1.67 \| 700000 \| 0.5152 \| \| 0.5494 \| 1.72 \| 720000 \| 0.5117 \| \| 0.5445 \| 1.77 \| 740000 \| 0.5076 \| \| 0.5396 \| 1.81 \| 760000 \| 0.5051 \| \| 0.5363 \| 1.86 \| 780000 \| 0.5026 \| \| 0.5356 \| 1.91 \| 800000 \| 0.4998 \| \| 0.5303 \| 1.96 \| 820000 \| 0.4982 \| \| 0.5583 \| 2.0 \| 840000 \| 0.5195 \| \| 0.5565 \| 2.05 \| 860000 \| 0.5180 \| \| 0.5535 \| 2.1 \| 880000 \| 0.5158 \| \| 0.5497 \| 2.15 \| 900000 \| 0.5133 \| \| 0.5511 \| 2.19 \| 920000 \| 0.5110 \| \| 0.5439 \| 2.24 \| 940000 \| 0.5085 \| \| 0.5413 \| 2.29 \| 960000 \| 0.5060 \| \| 0.5376 \| 2.34 \| 980000 \| 0.5023 \| \| 0.5333 \| 2.39 \| 1000000 \| 0.5004 \| \| 0.5322 \| 2.43 \| 1020000 \| 0.4973 \| \| 0.5312 \| 2.48 \| 1040000 \| 0.4941 \| \| 0.5281 \| 2.53 \| 1060000 \| 0.4921 \| \| 0.5267 \| 2.58 \| 1080000 \| 0.4902 \| \| 0.5257 \| 2.62 \| 1100000 \| 0.4871 \| \| 0.5174 \| 2.67 \| 1120000 \| 0.4849 \| \| 0.5183 \| 2.72 \| 1140000 \| 0.4825 \| \| 0.5181 \| 2.77 \| 1160000 \| 0.4807 \| \| 0.5116 \| 2.81 \| 1180000 \| 0.4784 \| \| 0.5092 \| 2.86 \| 1200000 \| 0.4769 \| \| 0.5109 \| 2.91 \| 1220000 \| 0.4757 \| \| 0.5102 \| 2.96 \| 1240000 \| 0.4739 \| ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2
XaneWayner/ppo-LunarLander-v2	XaneWayner	2023-03-22T13:18:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T13:17:52Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 275.38 +/- 17.28 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Yuvarraj/Streaming_ASR_PSG	Yuvarraj	2023-03-22T13:14:10Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "speech", "en", "dataset:librispeech_asr", "arxiv:2006.11477", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-03-22T13:10:02Z	--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Wav2Vec2-Large-960h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli Abstract We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate facebook/wav2vec2-large-960h on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import soundfile as sf import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` Result (WER): \| "clean" \| "other" \| \|---\|---\| \| 2.8 \| 6.3 \|
McCheng/a2c-PandaReachDense-v2	McCheng	2023-03-22T13:13:48Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T07:05:52Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.96 +/- 0.34 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Yureeh/Reinforce-PixelCopter	Yureeh	2023-03-22T13:08:25Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-21T22:43:22Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 27.30 +/- 24.78 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Ellipsoul/Reinforce-Pixelcopter-PLE-v0	Ellipsoul	2023-03-22T13:05:14Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T13:04:52Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.30 +/- 16.59 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
gcapde/gcapde-finetuned-amazon_reviews_multi	gcapde	2023-03-22T12:53:49Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T12:48:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: gcapde-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.908 --- <!-- 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. --> # gcapde-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2896 - Accuracy: 0.908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2333 \| 1.0 \| 63 \| 0.2837 \| 0.9002 \| \| 0.2117 \| 2.0 \| 126 \| 0.2896 \| 0.908 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
lucadiliello/opt-30b-deepspeed-inference-fp16-shard-4	lucadiliello	2023-03-22T12:49:04Z	4	0	transformers	[ "transformers", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-03-21T11:57:42Z	This is a copy of the original [OPT weights](https://huggingface.co/facebook/opt-30b) that is more efficient to use with the [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). In this repo the original tensors are split into 4 shards to target 4 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism. For specific details about the OPT model itself, please see the [original OPT model card](https://huggingface.co/facebook/opt-30b). For examples on using this repo please see the following: * https://github.com/huggingface/transformers-bloom-inference * https://github.com/microsoft/DeepSpeed-MII
lucadiliello/opt-30b-deepspeed-inference-fp16-shard-2	lucadiliello	2023-03-22T12:48:50Z	5	0	transformers	[ "transformers", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-03-21T11:57:48Z	This is a copy of the original [OPT weights](https://huggingface.co/facebook/opt-30b) that is more efficient to use with the [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). In this repo the original tensors are split into 2 shards to target 2 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism. For specific details about the OPT model itself, please see the [original OPT model card](https://huggingface.co/facebook/opt-30b). For examples on using this repo please see the following: * https://github.com/huggingface/transformers-bloom-inference * https://github.com/microsoft/DeepSpeed-MII
stanlochten/t5-KGQgen	stanlochten	2023-03-22T12:41:28Z	7	5	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "knowledge_graphs", "question_generation", "en", "dataset:web_questions", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: openrail datasets: - web_questions language: - en metrics: - bleu - bertscore library_name: transformers tags: - knowledge_graphs - question_generation --- T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format: `<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ...`, where `answer node(s)` refers to the node(s) which should contain the answer to the generated question. To load the model: ``` from transformers import T5ForConditionalGeneration, T5TokenizerFast model = T5ForConditionalGeneration.from_pretrained('stanlochten/t5-KGQgen') tokenizer = T5TokenizerFast.from_pretrained('t5-base', extra_ids=0, additional_special_tokens = ['<A>', '<H>', '<R>', '<T>']) ``` To generate questions from your graphs, where `graphs` is a list of strings for each graph: ``` print('Tokenizing...') inputs = tokenizer(graphs, return_tensors="pt", padding=True, truncation=True) print('Predicting...') y_hats = model.generate(inputs.input_ids) print('Decoding...') preds = tokenizer.batch_decode(y_hats, skip_special_tokens=True, clean_up_tokenization_spaces=True) ``` Good luck! [Associated research report](https://dspace.uba.uva.nl/server/api/core/bitstreams/fee95174-b7d4-4cd8-8545-f7ec8ab29e2d/content)
arrandi/PyramidTraining	arrandi	2023-03-22T12:40:59Z	8	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-03-22T12:38:59Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: arrandi/PyramidTraining 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929	pfunk	2023-03-22T12:35:57Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T12:35:48Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.32 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQPN_freq Agent Playing PongNoFrameskip-v4 This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x3.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x3]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x3 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x3 --policy-network-frequency 3000 --seed 929 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x3', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 3000, 'policy_tau': 1.0, 'save_model': True, 'seed': 929, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
qanastek/biomedical-specialities-classifier-french	qanastek	2023-03-22T12:33:55Z	5	0	transformers	[ "transformers", "pytorch", "camembert", "text-classification", "medical", "chemistry", "biology", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T11:13:24Z	--- license: apache-2.0 language: - fr metrics: - accuracy pipeline_tag: text-classification tags: - medical - chemistry - biology ---
stelladk/PPO-CleanRL-LunarLander	stelladk	2023-03-22T12:32:11Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T12:06:46Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -207.10 +/- 95.51 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'default_ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'stelladk/PPO-CleanRL-LunarLander' 'batch_size': 512 'minibatch_size': 128} ```
dvesely/dqn-SpaceInvadersNoFrameskip-v4	dvesely	2023-03-22T12:19:37Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T12:18:53Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 486.00 +/- 133.51 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dvesely -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dvesely -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dvesely ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.2), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0003), ('learning_starts', 50000), ('n_timesteps', 1500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 500), ('train_freq', 4), ('normalize', False)]) ```
marcatanante1/my_mind_model	marcatanante1	2023-03-22T12:15:53Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-03-22T09:45:53Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_mind_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. --> # my_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6450 - Accuracy: 0.0929 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.73 \| 2 \| 2.6408 \| 0.0752 \| \| No log \| 1.82 \| 5 \| 2.6441 \| 0.0619 \| \| No log \| 2.91 \| 8 \| 2.6435 \| 0.0973 \| \| 2.6293 \| 4.0 \| 11 \| 2.6446 \| 0.0885 \| \| 2.6293 \| 4.73 \| 13 \| 2.6449 \| 0.0841 \| \| 2.6293 \| 5.82 \| 16 \| 2.6463 \| 0.0841 \| \| 2.6293 \| 6.91 \| 19 \| 2.6452 \| 0.0885 \| \| 2.6177 \| 7.27 \| 20 \| 2.6450 \| 0.0929 \| ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888	pfunk	2023-03-22T12:15:06Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T12:14:57Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.11 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQPN_freq Agent Playing PongNoFrameskip-v4 This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x3.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x3]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x3 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x3 --policy-network-frequency 3000 --seed 888 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x3', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 3000, 'policy_tau': 1.0, 'save_model': True, 'seed': 888, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
kgmann/ai-image-det-resnet18	kgmann	2023-03-22T11:45:38Z	5	2	timm	[ "timm", "pytorch", "image-classification", "dataset:competitions/aiornot", "region:us" ]	image-classification	2023-03-22T11:34:13Z	--- tags: - image-classification - timm library_tag: timm datasets: - competitions/aiornot metrics: - accuracy --- # Model card for kgmann/ai-image-det-resnet18 This is a small resnet18 pretrained model, fine-tuned for 5 epochs on 80% of the [AI or Not dataset](https://huggingface.co/datasets/competitions/aiornot) and evaluated on the remaining 20% of the training dataset. It has an accuracy of 99% on the validation dataset.
arrandi/ppo-SnowballTarget	arrandi	2023-03-22T11:30:23Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-03-22T11:30:17Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: arrandi/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
alramalho/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-extended-labels	alramalho	2023-03-22T11:25:51Z	4	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "zero-shot-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	zero-shot-classification	2023-03-21T19:10:41Z	--- pipeline_tag: zero-shot-classification ---
blinoff/roberta-base-russian-v0	blinoff	2023-03-22T11:23:40Z	339	8	transformers	[ "transformers", "pytorch", "jax", "safetensors", "roberta", "fill-mask", "ru", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: ru widget: - text: "Мозг — это машина вывода, которая пытается <mask> ошибку в прогнозе." example_title: "brain_example" - text: "Никогда не спорьте с идиотами, <mask> опуститесь до их уровня, где они вас задавят своим опытом." example_title: "idiot_example" --- # RoBERTa-like language model trained on part of part of TAIGA corpus ## Training Details - about 60k steps ![]() ## Example pipeline ```python from transformers import pipeline from transformers import RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('blinoff/roberta-base-russian-v0', max_len=512) fill_mask = pipeline( "fill-mask", model="blinoff/roberta-base-russian-v0", tokenizer=tokenizer ) fill_mask("Мозг — это машина <mask>, которая пытается снизить ошибку в прогнозе.") # { # 'sequence': '<s>Мозг — это машина города, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.012859329581260681, # 'token': 2144, # 'token_str': 'ĠÐ³Ð¾ÑĢÐ¾Ð´Ð°' # }, # { # 'sequence': '<s>Мозг — это машина человека, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.01185101643204689, # 'token': 1470, # 'token_str': 'ĠÑĩÐµÐ»Ð¾Ð²ÐµÐºÐ°' # }, # { # 'sequence': '<s>Мозг — это машина дома, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.009940559044480324, # 'token': 1411, # 'token_str': 'ĠÐ´Ð¾Ð¼Ð°' # }, # { # 'sequence': '<s>Мозг — это машина женщина, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.007794599514454603, # 'token': 2707, # 'token_str': 'ĠÐ¶ÐµÐ½ÑīÐ¸Ð½Ð°' # }, # { # 'sequence': '<s>Мозг — это машина женщины, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.007725382689386606, # 'token': 3546, # 'token_str': 'ĠÐ¶ÐµÐ½ÑīÐ¸Ð½Ñĭ' # } ```
arrandi/poca-SoccerTwos	arrandi	2023-03-22T11:08:56Z	7	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]	reinforcement-learning	2023-03-22T11:08:47Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: arrandi/poca-SoccerTwos 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
antonioricciardi/ppo-Huggy	antonioricciardi	2023-03-22T10:56:56Z	3	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-03-22T10:56:50Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: antonioricciardi/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
faviasono/bert-base-banking77-pt2	faviasono	2023-03-22T10:52:02Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T09:57:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9264340389849328 --- <!-- 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-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3012 - F1: 0.9264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0535 \| 1.0 \| 626 \| 0.7552 \| 0.8650 \| \| 0.3807 \| 2.0 \| 1252 \| 0.3574 \| 0.9224 \| \| 0.1794 \| 3.0 \| 1878 \| 0.3012 \| 0.9264 \| ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2
Chattiori/PetalMix	Chattiori	2023-03-22T10:50:42Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T10:48:56Z	--- license: creativeml-openrail-m ---
vevlins/autotrain-classify-42751109216	vevlins	2023-03-22T10:43:30Z	16	0	transformers	[ "transformers", "pytorch", "beit", "image-classification", "autotrain", "vision", "dataset:vevlins/autotrain-data-classify", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-03-22T10:41:19Z	--- tags: - autotrain - vision - image-classification datasets: - vevlins/autotrain-data-classify widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.852147336270292 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 42751109216 - CO2 Emissions (in grams): 0.8521 ## Validation Metrics - Loss: 0.010 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000
satyaalmasian/temporal_tagger_BERT_tokenclassifier	satyaalmasian	2023-03-22T10:24:27Z	23	4	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	# BERT based temporal tagged Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger). # Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. We use BERT for token classification to tag the tokens in text with classes: ``` O -- outside of a tag I-TIME -- inside tag of time B-TIME -- beginning tag of time I-DATE -- inside tag of date B-DATE -- beginning tag of date I-DURATION -- inside tag of duration B-DURATION -- beginning tag of duration I-SET -- inside tag of the set B-SET -- beginning tag of the set ``` # Intended uses & limitations This model is best used accompanied with code from the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). Especially for inference, the direct output might be noisy and hard to decipher, in the repository we provide alignment functions and voting strategies for the final output. # How to use you can load the model as follows: ``` tokenizer = AutoTokenizer.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier", use_fast=False) model = BertForTokenClassification.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier") ``` for inference use: ``` processed_text = tokenizer(input_text, return_tensors="pt") result = model(**processed_text) classification= result[0] ``` for an example with post-processing, refer to the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). We provide a function `merge_tokens` to decipher the output. to further fine-tune, use the `Trainer` from hugginface. An example of a similar fine-tuning can be found [here](https://github.com/satya77/Transformer_Temporal_Tagger/blob/master/run_token_classifier.py). #Training data We use 3 data sources: [Tempeval-3](https://www.cs.york.ac.uk/semeval-2013/task1/index.php%3Fid=data.html), Wikiwars, Tweets datasets. For the correct data versions please refer to our [repository](https://github.com/satya77/Transformer_Temporal_Tagger). #Training procedure The model is trained from publicly available checkpoints on huggingface (`bert-base-uncased`), with a batch size of 34. We use a learning rate of 5e-05 with an Adam optimizer and linear weight decay. We fine-tune with 5 different random seeds, this version of the model is the only seed=4. For training, we use 2 NVIDIA A100 GPUs with 40GB of memory.
agcagc/ppo-LunarLander-v2-clear	agcagc	2023-03-22T10:04:14Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T10:04:03Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -145.06 +/- 94.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'agcagc/ppo-LunarLander-v2-clear' 'batch_size': 512 'minibatch_size': 128} ```
whit/mthwmdl	whit	2023-03-22T10:03:51Z	8	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-03-22T09:34:04Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mthwmdl Dreambooth model trained by whit with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
juliusco/GPT-2-finetuned-papers	juliusco	2023-03-22T10:01:37Z	4	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-03-22T08:20:52Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: juliusco/GPT-2-finetuned-papers 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. --> # juliusco/GPT-2-finetuned-papers This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.4240 - Validation Loss: 2.2215 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 2.4240 \| 2.2215 \| 0 \| ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
mrm8488/bloom-560m-finetuned-samsum	mrm8488	2023-03-22T10:00:28Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-27T15:31:39Z	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: bloom-560m-finetuned-samsum 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. --> # bloom-560m-finetuned-samsum This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9178 ## 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: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7663 \| 0.63 \| 200 \| 2.6934 \| \| 2.3769 \| 1.26 \| 400 \| 2.6274 \| \| 2.2776 \| 1.89 \| 600 \| 2.5818 \| \| 1.873 \| 2.52 \| 800 \| 2.7177 \| \| 1.6715 \| 3.15 \| 1000 \| 2.9178 \| \| 1.4515 \| 3.78 \| 1200 \| 2.8924 \| \| 1.0522 \| 4.42 \| 1400 \| 3.3753 \| \| 1.0237 \| 5.05 \| 1600 \| 3.8098 \| \| 0.7416 \| 5.68 \| 1800 \| 3.9139 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
marcovarrone/scvi-dlpfc-full-visium	marcovarrone	2023-03-22T09:58:46Z	0	1	scvi-tools	[ "scvi-tools", "biology", "genomics", "single-cell", "model_cls_name:SCVI", "scvi_version:0.20.0", "anndata_version:0.8.0", "modality:rna", "annotated:False", "license:cc-by-4.0", "region:us" ]	null	2023-03-22T09:51:57Z	--- license: cc-by-4.0 library_name: scvi-tools tags: - biology - genomics - single-cell - model_cls_name:SCVI - scvi_version:0.20.0 - anndata_version:0.8.0 - modality:rna - annotated:False --- # Description scVI model trained on the full DLPFC Visium data (including the pilot samples). # Model properties Many model properties are in the model tags. Some more are listed below. model_init_params: ```json { "n_hidden": 128, "n_latent": 5, "n_layers": 1, "dropout_rate": 0.1, "dispersion": "gene", "gene_likelihood": "zinb", "latent_distribution": "normal" } ``` model_setup_anndata_args: ```json { "layer": "counts", "batch_key": "patient", "labels_key": null, "size_factor_key": null, "categorical_covariate_keys": [ "sample", "study" ], "continuous_covariate_keys": null } ``` model_summary_stats: \| Summary Stat Key \| Value \| \|--------------------------\|--------\| \| n_batch \| 13 \| \| n_cells \| 166443 \| \| n_extra_categorical_covs \| 2 \| \| n_extra_continuous_covs \| 0 \| \| n_labels \| 1 \| \| n_vars \| 5000 \| model_data_registry: \| Registry Key \| scvi-tools Location \| \|------------------------\|--------------------------------------------\| \| X \| adata.layers['counts'] \| \| batch \| adata.obs['_scvi_batch'] \| \| extra_categorical_covs \| adata.obsm['_scvi_extra_categorical_covs'] \| \| labels \| adata.obs['_scvi_labels'] \| model_parent_module: scvi.model data_is_minified: False # Training data This is an optional link to where the training data is stored if it is too large to host on the huggingface Model hub. <!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make sure to provide this field if you want users to be able to access your training data. See the scvi-tools documentation for details. --> Training data url: N/A # Training code This is an optional link to the code used to train the model. Training code url: N/A # References 1. Maynard, Kristen R., et al. "Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex." Nature neuroscience 24.3 (2021): 425-436. 2. Huuki-Myers, Louise A., et al. "Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex." BioRxiv (2023): 2023-02.
nahiavl/huggy_20_03	nahiavl	2023-03-22T09:55:00Z	10	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-03-20T14:17:46Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: nahiavl/huggy_20_03 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gogo432754/finetuning-sentiment-model-3000-samples	gogo432754	2023-03-22T09:52:33Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T10:25:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8712871287128714 --- <!-- 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2955 - Accuracy: 0.87 - F1: 0.8713 ## 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 4.27.1 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2
ybelkada/clap-model-card	ybelkada	2023-03-22T09:50:52Z	0	1	null	[ "arxiv:2211.06687", "license:apache-2.0", "region:us" ]	null	2023-03-14T16:13:06Z	--- license: apache-2.0 --- # Model card for CLAP Model card for CLAP: Contrastive Language-Audio Pretraining ![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png) # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Citation](#citation) # TL;DR The abstract of the paper states that: > Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. # Usage You can use this model for zero shot audio classification or extracting audio and/or textual features. # Uses ## Perform zero-shot audio classification ### Using `pipeline` ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("ashraq/esc50") audio = dataset["train"]["audio"][-1]["array"] audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused") output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]) print(output) >>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}] ``` ## Run the model: You can also get the audio and text embeddings using `ClapModel` ### Run the model on CPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused") processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt") audio_embed = model.get_audio_features(inputs) ``` ### Run the model on GPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused").to(0) processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0) audio_embed = model.get_audio_features(inputs) ``` # Citation If you are using this model for your work, please consider citing the original paper: ``` @misc{https://doi.org/10.48550/arxiv.2211.06687, doi = {10.48550/ARXIV.2211.06687}, url = {https://arxiv.org/abs/2211.06687}, author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo}, keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
gonced8/godel-multiwoz	gonced8	2023-03-22T09:43:29Z	12	4	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "en", "dataset:multi_woz_v22", "license:gpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-01-24T12:30:06Z	--- license: gpl-3.0 datasets: - multi_woz_v22 language: - en metrics: - bleu - rouge --- Pretrained model: [GODEL-v1_1-base-seq2seq](https://huggingface.co/microsoft/GODEL-v1_1-base-seq2seq/) Fine-tuning dataset: [MultiWOZ 2.2](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2) # How to use: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("gonced8/godel-multiwoz") model = AutoModelForSeq2SeqLM.from_pretrained("gonced8/godel-multiwoz") # Encoder input context = [ "USER: I need train reservations from norwich to cambridge", "SYSTEM: I have 133 trains matching your request. Is there a specific day and time you would like to travel?", "USER: I'd like to leave on Monday and arrive by 18:00.", ] input_text = " EOS ".join(context[-5:]) + " => " model_inputs = tokenizer( input_text, max_length=512, truncation=True, return_tensors="pt" )["input_ids"] # Decoder input answer_start = "SYSTEM: " decoder_input_ids = tokenizer( "<pad>" + answer_start, max_length=256, truncation=True, add_special_tokens=False, return_tensors="pt", )["input_ids"] # Generate output = model.generate( model_inputs, decoder_input_ids=decoder_input_ids, max_length=256 ) output = tokenizer.decode( output[0], clean_up_tokenization_spaces=True, skip_special_tokens=True ) print(output) # SYSTEM: TR4634 arrives at 17:35. Would you like me to book that for you? ```
micromind/MNIST	micromind	2023-03-22T09:39:31Z	0	0	null	[ "image-classification", "en", "dataset:mnist", "license:mit", "region:us" ]	image-classification	2023-02-22T09:47:46Z	--- license: mit datasets: - mnist language: - en pipeline_tag: image-classification --- # micromind checkpoints for MNIST This repository contains checkpoints for the MNIST dataset for the following networks: \| Model \| Top 1 Accuracy \| Top 5 Accuracy \| \| ------------------ \|---------------- \| -------------- \| \| `PhiNet(alpha=0.5, beta=1, t_zero=6, num_layers=4, resolution=28)` \| 98.96% \| 100% \| \| `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=28)` \| 99.03% \| 99.98% \| \| `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=28)` \| 98.72% \| 99.99% \| \| `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=28)` \| 98.84% \| 99.99% \| \| `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=28)` \| 98.76% \| 99.97% \| To download and use this repo: ``` from micromind import PhiNet model = PhiNet.from_pretrained("MNIST", alpha=0.5, beta=1.0, t_zero=6, num_layers=4, num_classes=10, resolution=28) ``` ## Authors - [@fpaissan](https://www.github.com/fpaissan) - [@matteobeltrami](https://www.github.com/matteobeltrami) --- license: mit ---
bhadresh-savani/electra-base-squad2	bhadresh-savani	2023-03-22T09:36:46Z	117	0	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "electra", "question-answering", "dataset:squad_v2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2022-04-13T14:25:23Z	--- datasets: - squad_v2 license: cc-by-4.0 --- # electra-base for QA ## Overview Language model: electra-base Language: English Downstream-task: Extractive QA Training data: SQuAD 2.0 Eval data: SQuAD 2.0 Code: See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) Infrastructure: 1x Tesla v100 ## Hyperparameters ``` seed=42 batch_size = 32 n_epochs = 5 base_LM_model = "google/electra-base-discriminator" max_seq_len = 384 learning_rate = 1e-4 lr_schedule = LinearWarmup warmup_proportion = 0.1 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 77.30144024256717, "f1": 81.35438272008543, "total": 11873, "HasAns_exact": 74.34210526315789, "HasAns_f1": 82.45961302894314, "HasAns_total": 5928, "NoAns_exact": 80.25231286795626, "NoAns_f1": 80.25231286795626, "NoAns_total": 5945 ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/electra-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ### In FARM ```python from farm.modeling.adaptive_model import AdaptiveModel from farm.modeling.tokenization import Tokenizer from farm.infer import Inferencer model_name = "deepset/electra-base-squad2" # a) Get predictions nlp = Inferencer.load(model_name, task_type="question_answering") QA_input = [{"questions": ["Why is model conversion important?"], "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}] res = nlp.inference_from_dicts(dicts=QA_input) # b) Load model & tokenizer model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") tokenizer = Tokenizer.load(model_name) ``` ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/electra-base-squad2") # or reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-squad2") ``` ## Authors Vaishali Pal `vaishali.pal [at] deepset.ai` Branden Chan: `branden.chan [at] deepset.ai` Timo Möller: `timo.moeller [at] deepset.ai` Malte Pietsch: `malte.pietsch [at] deepset.ai` Tanay Soni: `tanay.soni [at] deepset.ai` Note: Borrowed this model from Haystack model repo for adding tensorflow model.
Mor1998/distilbert-base-uncased-distilled-dtkd-clinc	Mor1998	2023-03-22T08:54:02Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T08:05:52Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-dtkd-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9319354838709677 --- <!-- 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-distilled-dtkd-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0655 - Accuracy: 0.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6996 \| 1.0 \| 318 \| 0.4150 \| 0.5790 \| \| 0.3134 \| 2.0 \| 636 \| 0.2040 \| 0.8381 \| \| 0.1843 \| 3.0 \| 954 \| 0.1330 \| 0.8952 \| \| 0.1322 \| 4.0 \| 1272 \| 0.1032 \| 0.9119 \| \| 0.1053 \| 5.0 \| 1590 \| 0.0858 \| 0.9213 \| \| 0.0908 \| 6.0 \| 1908 \| 0.0771 \| 0.9258 \| \| 0.0813 \| 7.0 \| 2226 \| 0.0710 \| 0.9287 \| \| 0.0754 \| 8.0 \| 2544 \| 0.0681 \| 0.9310 \| \| 0.0717 \| 9.0 \| 2862 \| 0.0660 \| 0.9310 \| \| 0.0701 \| 10.0 \| 3180 \| 0.0655 \| 0.9319 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
loveplay1983/distilbert-base-uncased-finetuned-emotion	loveplay1983	2023-03-22T08:51:15Z	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	2023-03-22T02:20:28Z	--- 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 config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.941 - name: F1 type: f1 value: 0.9410654356868428 --- <!-- 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: 0.1570 - Accuracy: 0.941 - F1: 0.9411 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.4447 \| 1.0 \| 1600 \| 0.1987 \| 0.9295 \| 0.9290 \| \| 0.155 \| 2.0 \| 3200 \| 0.1570 \| 0.941 \| 0.9411 \| ### Framework versions - Transformers 4.27.1 - Pytorch 1.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2
asuzuki/PPO-LunarLander-v2	asuzuki	2023-03-22T08:49:52Z	4	0	transformers	[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-01-06T08:49:20Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -117.50 +/- 52.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'asuzuki/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
bhadresh-savani/roberta-base-emotion	bhadresh-savani	2023-03-22T08:48:07Z	779	5	transformers	[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "emotion", "en", "dataset:emotion", "arxiv:1907.11692", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en license: apache-2.0 tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 model-index: - name: bhadresh-savani/roberta-base-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.931 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjg5OTI4ZTlkY2VmZjYzNGEzZGQ3ZjczYzY5YjJmMGVmZDQ4ZWNiYTAyZTJiZjlmMTU2MjE1NTllMWFhYzU0MiIsInZlcnNpb24iOjF9.dc44cEsbu900M2s64GyVIWKPagBzwI-dPlfvh0NGyJFMGKOcypke9P2ary9fBZITrH3UF6lza3sCh7vWYZFHBQ - type: precision value: 0.9168321948556312 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2EzYTcxNTExNGU1MmFiZjE3NGE5MDIyMDU2M2U3OGExOTdjZDE5YWU2NDhmOTJlYWMzY2NkN2U5MmRmZTE0MiIsInZlcnNpb24iOjF9.4U7vJ3ALdUUxySMhVeb4Qa1tSp3wphSIZkRYNMujz-KrOZW8kkcmCde3ioStBg3Qqyf1powYd88uk1R7DuWRBA - type: precision value: 0.931 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhmZGRlYWE5ZTAzMmJiMzlmMWZiM2VlYjdiNzI0NjVmN2M2YzcxM2EzYTg0OTFiZTE1MjVmNzE5NGEzYTg2ZCIsInZlcnNpb24iOjF9.8eCHAK0rlZWnhBNQdh9kcuAeItmDUAgK3KkZ7eC-GyYhi4HT5dZiS6btcC5EjkYVOS4czcjzqxfVz4PuZgtLDQ - type: precision value: 0.9357445689014415 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhZTdkNzYzMjhjZjc4MTAxNWZiYjgzMjhhNjRiZWRmYjc5YTA0NTQ1MzllMTYxMTVkMDk4OTE0ZGEyMTNhMiIsInZlcnNpb24iOjF9.YIZfj2Eo1nMX2GVSfqJy-Cp7VBubfUh2LuOnU60sG5Lci8FdlNbAanS1IzAyxU3U29lqiTasxfS_yrwAj5cmBQ - type: recall value: 0.8743657671177089 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2YTcyNzMwYzZiMmM1Yzc4YWZhNDM3ZDQyMjI1NWZhMjQyNmU5NTA0YmE2ZDBiZmY1MmUyZWRlMjRhMjFmYSIsInZlcnNpb24iOjF9.XKlFy_Cx4T4l7Otd8aAwWcI-fJ_dJ6V1Kp3uZm6OWjwCb1Do6mSdPFfwiMeBZZyfEIsNBnguegssZvHsOfTSAQ - type: recall value: 0.931 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzgzN2JkNzAzZDRjNjJmZjNkY2RmYzVkMWEzYTMzZDU4NzJlYzBmOWE4MTU0MGU0MTJhM2JjZDdjODhlZDExOCIsInZlcnNpb24iOjF9.9tSVB4yNBdFXpH3equwo1ZaEnVUktO6lm93UEJ-luKhxo6wgS54OLjgDq7IpJYwa3lvYyjy-sxzQEe9ri31WAg - type: recall value: 0.931 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGVhZTIyMmVmOTU1YWNjMmZiZjNmOTNlNzlhZTk3NjhlZmMwZGFkZWQxZTlhZWUwZGQyN2JhOWQyNWQ3MTVhOCIsInZlcnNpb24iOjF9.2odv2fK7zH0_S_7wC3obONzjxOipDdjWvddhnGdMnrIN6CiZwLp7XgizpqcWbwAQ_9YJwjC-6wXpbq2jTvN0Bw - type: f1 value: 0.8821236522209227 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDI0YTUxOTA2M2ZjNGM1OTJlZDAzZTAxNTg4YjY3OWNmMjNmMTk0YWRjZTE2Y2ZmYWI1ZmU3ZmJmNzNjMjBlOCIsInZlcnNpb24iOjF9.P5-TbuEUrCtX9H7F-tKn8LI1RBPhoJwjJm_l853WTSzdLioThAtIK5HBG0xgXT2uB0Q8v94qH2b8cz1j_WonDg - type: f1 value: 0.931 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjNmNDgyMmFjODYwNjcwOTJiOGM2N2YwYjUyMDk5Yjk2Y2I3NmFmZGFhYjU0NGM2OGUwZmRjNjcxYTU3YzgzNSIsInZlcnNpb24iOjF9.2ZoRJwQWVIcl_Ykxce1MnZ3mSxBGxGeNYFPxt9mivo9yTi3gUE7ua6JRpVEOnOUbevlWxVkUUNnmOPFqBN1sCQ - type: f1 value: 0.9300782840205046 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGE1OTcxNmNmMjQ3ZDAzYzk0N2Q1MGFjM2VhNWMyYmRjY2E3ZThjODExOTNlNWMxYzdlMWM2MDBiMTZhY2M2OSIsInZlcnNpb24iOjF9.r63SEArCiFB5m0ccV2q_t5uSOtjVnWdz4PfvCYUchm0JlrRC9YAm5oWKeO419wdyFY4rZFe014yv7sRcV-CgBQ - type: loss value: 0.15155883133411407 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2M4MmVlNjAzZjhiMWJlNWQxMDg5ZTRiYjFlZGYyMGMyYzU4M2IwY2E1M2E2MzA5NmU5ZjgwZTZmMDI5YjgzMyIsInZlcnNpb24iOjF9.kjgFJohkTxLKtzHJDlBvd6qolGQDSZLbrDE7C07xNGmarhTLc_A3MmLeC4MmQGOl1DxfnHflImIkdqPylyylDA --- # robert-base-emotion ## Model description: [roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining. [roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters ``` learning rate 2e-5, batch size 64, num_train_epochs=8, ``` ## Model Performance Comparision on Emotion Dataset from Twitter: \| Model \| Accuracy \| F1 Score \| Test Sample per Second \| \| --- \| --- \| --- \| --- \| \| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) \| 93.8 \| 93.79 \| 398.69 \| \| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) \| 94.05 \| 94.06 \| 190.152 \| \| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) \| 93.95 \| 93.97\| 195.639 \| \| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) \| 93.6 \| 93.65 \| 182.794 \| ## How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/roberta-base-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ Output: [[ {'label': 'sadness', 'score': 0.002281982684507966}, {'label': 'joy', 'score': 0.9726489186286926}, {'label': 'love', 'score': 0.021365027874708176}, {'label': 'anger', 'score': 0.0026395076420158148}, {'label': 'fear', 'score': 0.0007162453257478774}, {'label': 'surprise', 'score': 0.0003483477921690792} ]] """ ``` ## Dataset: [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). ## Training procedure [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) follow the above notebook by changing the model name to roberta ## Eval results ```json { 'test_accuracy': 0.9395, 'test_f1': 0.9397328860104454, 'test_loss': 0.14367154240608215, 'test_runtime': 10.2229, 'test_samples_per_second': 195.639, 'test_steps_per_second': 3.13 } ``` ## Reference: * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)
bhadresh-savani/bertweet-base-finetuned-emotion	bhadresh-savani	2023-03-22T08:42:15Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-07-11T15:57:26Z	--- tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bertweet-base-finetuned-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion args: default metrics: - type: accuracy value: 0.929 name: Accuracy - type: f1 value: 0.9295613935787139 name: F1 - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.925 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZThkYWEwYTdjY2IwMmE4NmM2Mzc3ZTVkNTNmNWYwNGUxYTM5ZDA5ODEwMGQ1ZGU0ZmJmY2U1ZDhjYWRlZjU2NSIsInZlcnNpb24iOjF9.QJYOUR_EPrYzbZGBb1N27BSlTQIdvd1hmUfnfPJdTGGrNoQwXBUA4amVsWh1txV_YtO8hcCx-b3pTqzpdy1FAw - type: precision value: 0.8722017563353339 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2Y3ZGM2NDk5ZTQyNTNjZDdmNjk5Y2IwNzkxNmU3MDM0YTljMTJjMzFmMTlkN2ZjN2NhZjNhYTVlMWY5NWFjNCIsInZlcnNpb24iOjF9.cBYScC_c6g1ECi3rj6HiRI3AMuoxg8wp7JKha0UKh1Q2qjzTr5ml8JAByPL0iu-Ix5BO2Bsx0fZNFhUS82LiCg - type: precision value: 0.925 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjVjYjgwYjE2ZWUyM2Y5MzE0ODk4NDA5MGM2ODIxYTgxZDYyMTUxNzcwZWQ2MjZjZGYwODkyNzFkMjAxOTUzYyIsInZlcnNpb24iOjF9.phgA4BJcqp4ZUhecNeuGU8OAf6f_asN9Mf6JfFGd0cPORYltd_N4Wf6EXqu6z1ADqWeeibteEyIUwmmMEbjYBQ - type: precision value: 0.9283646705517916 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjE4MDUyYjU4YTc4Mzk4YmIwMGIzZWEyYmU5ZDQ0MjRhZDQ4OGMwZjVmZmEyNDM5NzYyZTMzMTJiMmRkZTU4NiIsInZlcnNpb24iOjF9.LbYjoga-JSCzHZAF1fhm1CfuaSSI-ok0yXj3gtd4QTWY1TjzOHoMG3Q6zEGz84l6ASoHsvi9wjS7_EaSQLB4Dw - type: recall value: 0.8982480793145559 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzFiY2JmYWRhYmE1YzA1MmRhODI0MGE1NTRiMWE3YmNjZWQ4OGExMDg3NmUzYmUzYTYyNTdkNjM1Y2M0ODJmMCIsInZlcnNpb24iOjF9.dAq2gloG0O-4z5Ng7RZkFO7e0og3wBQBmIDzic6onwjw83yaHPVfRd1e0j6mNhMUifOwPLEavnYkBYa9DVFqCw - type: recall value: 0.925 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg5Y2M2NWIwZmI4YjEyMmIzYjNjZmQyMzhlYjg2ZDRmY2U1M2I1NzQzYjRmMzYxYzJkNTI5MDJjMmY5ZDVmNyIsInZlcnNpb24iOjF9.Z5hmQBUsoKAgqTXk47aUDNKf5jJ0mXzY9TAgM9vG8I3pgCT465PEfM-TOKfG_YcPMLd3tkB8AdwDpmVnNj5QCw - type: recall value: 0.925 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODIzMjY1YjgzYzAwNjk2NGI1ZjFhZTg4MGY1Mzg5NDhkM2EzY2JlMWM4MjZmNjg4ZmEyZDJmZTUwNDFkZmNiOCIsInZlcnNpb24iOjF9.S-9p04Lru9WTzm50mM5qGM4oA-TPgNw6uwxKr5AejU1iPKjyTDQvoumBs41T5OKL5zN_NyYXsFsCermSbirLAw - type: f1 value: 0.883488774573809 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMyMjc3NTkxNDNjNTJiNmRmMTA4NzY0MTgyMDc3ZDE4N2RlMTY1YzU4OGQ1YmM1NzY2OGQ4Y2I0MzVhOGU3OSIsInZlcnNpb24iOjF9.D65sLHNZGjp15ra4i5ccYyOX705Xq-hftZjDb6kqE5X-jhzA5VLev6FirhnhyYLBQmA6Q9T1eDYHKkVZG4CcBg - type: f1 value: 0.925 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTNkMmUwNjYxNGE4M2E2MDA0NTI4ZjA1OTNkMWEwN2MzY2JmMWYxYThiYTZmM2MwZjM5YTIzMGIzMGI4ODJlZSIsInZlcnNpb24iOjF9.cB4WUQN_weyKdMZehH0ECaTcD9Jl1xzmrOzJZz27OJeCPjY0uW8O63HnJZ_LmBF2xqd7HDypT4s8hZBMT-6eDw - type: f1 value: 0.9259820821054494 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTY3NDhjNmMzYzM1MjIyY2FkYjI5YTQ1NTdmODhmMGVlNjc2ZjQ3MWZmZWEyMDQ1OGI1NDllZTBhM2VjYzg2MSIsInZlcnNpb24iOjF9.Akd8PVgc2tyin_TaOZV1bio_b00g3QmlHA-GWV3rMX13B1imDLuPAuP-HWIwgqg-umQUkJzcUQlTqbcQ06v0DQ - type: loss value: 0.18158096075057983 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDZhODkzODQ2ZjYyMWQxYjEzNzRmMmQ0NjM3M2RiNDdlMTcwOGRhYjA0NWEwYTVjMmY0ZWY3NGQ3MzFhMTQ3ZSIsInZlcnNpb24iOjF9.jzv7qMmQuFmrsR3WoRAsCbrRJhNk0sfEcN07lCqhxUwYcO4rblVbBiePQtr0IDN067PbQmV6ES6W2cjHqvuHAA --- <!-- 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. --> # bertweet-base-finetuned-emotion This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1737 - Accuracy: 0.929 - F1: 0.9296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.9469 \| 1.0 \| 250 \| 0.3643 \| 0.895 \| 0.8921 \| \| 0.2807 \| 2.0 \| 500 \| 0.2173 \| 0.9245 \| 0.9252 \| \| 0.1749 \| 3.0 \| 750 \| 0.1859 \| 0.926 \| 0.9266 \| \| 0.1355 \| 4.0 \| 1000 \| 0.1737 \| 0.929 \| 0.9296 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
bhadresh-savani/electra-base-discriminator-finetuned-conll03-english	bhadresh-savani	2023-03-22T08:41:28Z	108	0	transformers	[ "transformers", "pytorch", "tf", "jax", "electra", "token-classification", "en", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-04-02T11:22:08Z	--- language: - en tags: - token-classification - pytorch license: apache-2.0 datasets: - conll2003 metrics: - Accuracy, F1 Score, Precision, Recall model-index: - name: bhadresh-savani/electra-base-discriminator-finetuned-conll03-english results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics: - name: Accuracy type: accuracy value: 0.9397659001450176 verified: true - name: Precision type: precision value: 0.9492206245667668 verified: true - name: Recall type: recall value: 0.9468813162653806 verified: true - name: F1 type: f1 value: 0.9480495273598721 verified: true - name: loss type: loss value: 0.3468747138977051 verified: true --- # Electra Base Discriminator conll03 English # Results: ``` *** predict metrics *** predict_accuracy = 0.9813 predict_f1 = 0.9137 predict_loss = 0.1251 predict_precision = 0.9098 predict_recall = 0.9177 predict_runtime = 0:00:10.11 predict_samples_per_second = 341.368 predict_steps_per_second = 42.696 ```
GanjinZero/biobart-v2-base	GanjinZero	2023-03-22T08:22:33Z	779	4	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "biobart", "biomedical", "en", "arxiv:2204.03905", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-21T15:57:43Z	--- language: - en license: apache-2.0 tags: - bart - biobart - biomedical inference: true widget: - text: "Influenza is a <mask> disease." - type: "text-generation" --- Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) V2 adopts a new biomedical vocab. ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and Sheng Yu}, year={2022}, eprint={2204.03905}, archivePrefix={arXiv} } ```
GanjinZero/biobart-base	GanjinZero	2023-03-22T08:22:29Z	463	5	transformers	[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "biobart", "biomedical", "en", "arxiv:2204.03905", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-12T07:00:32Z	--- language: - en license: apache-2.0 tags: - bart - biobart - biomedical inference: true widget: - text: "Influenza is a <mask> disease." - type: "text-generation" --- Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and Sheng Yu}, year={2022}, eprint={2204.03905}, archivePrefix={arXiv} } ```
LarryAIDraw/SaekiSayakaBloomInto_v10	LarryAIDraw	2023-03-22T08:12:59Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T08:10:34Z	--- license: creativeml-openrail-m --- https://civitai.com/models/22624/saeki-sayaka-bloom-into-you
LarryAIDraw/dunkerqueAzurLane_v10	LarryAIDraw	2023-03-22T08:12:32Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T08:09:25Z	--- license: creativeml-openrail-m --- https://civitai.com/models/19040/dunkerque-or-azur-lane
LarryAIDraw/liselotteCretiaSeirei_liselottecretia4	LarryAIDraw	2023-03-22T08:12:10Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T08:08:33Z	--- license: creativeml-openrail-m --- https://civitai.com/models/22379/liselotte-cretia-seirei-gensouki
patrickramos/bert-base-japanese-v2-wrime-fine-tune	patrickramos	2023-03-22T08:11:34Z	5,123	6	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "text-classification", "ja", "dataset:wrime", "license:cc-by-sa-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-05-22T09:42:14Z	--- license: cc-by-sa-3.0 language: - ja tag: - emotion-analysis datasets: - wrime widget: - text: "車のタイヤがパンクしてた。。いたずらの可能性が高いんだって。。" --- # WRIME-fine-tuned BERT base Japanese This model is a [Japanese BERT<sub>BASE</sub>](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) fine-tuned on the [WRIME](https://github.com/ids-cv/wrime) dataset. It was trained as part of the paper ["Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations"](https://aclanthology.org/2022.wassa-1.10/). Fine-tuning code is available at this [repo](https://github.com/PatrickJohnRamos/BERT-Japan-vaccination). # Intended uses and limitations This model can be used to predict intensities scores for eight emotions for writers and readers. Please refer to the `Fine-tuning data` section for the list of emotions. Because of the regression fine-tuning task, it is possible for the model to infer scores outside of the range of the scores of the fine-tuning data (`score < 0` or `score > 4`). # Model Architecture, Tokenization, and Pretraining The Japanese BERT<sub>BASE</sub> fine-tuned was `cl-tohoku/bert-base-japanese-v2`. Please refer to their [model card](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) for details regarding the model architecture, tokenization, pretraining data, and pretraining procedure. # Fine-tuning data The model is fine-tuned on [WRIME](https://github.com/ids-cv/wrime), a dataset of Japanese Tweets annotated with writer and reader emotion intensities. We use version 1 of the dataset. Each Tweet is accompanied by a set of writer emotion intensities (from the author of the Tweet) and three sets of reader emotions (from three annotators). The emotions follow Plutchhik's emotions, namely: * joy * sadness * anticipation * surprise * anger * fear * disgust * trust These emotion intensities follow a four-point scale: \| emotion intensity \| emotion presence\| \|---\|---\| \| 0 \| no \| \| 1 \| weak \| \| 2 \| medium \| \| 3 \| strong \| # Fine-tuning The BERT is fine-tuned to directly regress the emotion intensities of the writer and the averaged emotions of the readers from each Tweet, meaning there are 16 outputs (8 emotions per writer/reader). The fine-tuning was inspired by common BERT fine-tuning procedures. The BERT was fine-tuned on WRIME for 3 epochs using the AdamW optimizer with a learning rate of 2e-5, β<sub>1</sub>=0.9, β<sub>2</sub>=0.999, weight decay of 0.01, linear decay, a warmup ratio of 0.01, and a batch size of 32. Training was conducted with an NVIDIA Tesla K80 and finished in 3 hours. # Evaluation results Below are the MSEs of the BERT on the test split of WRIME. \| Annotator \| Joy \| Sadness \| Anticipation \| Surprise \| Anger \| Fear \| Disgust \| Trust \| Overall \| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \| Writer \| 0.658 \| 0.688 \| 0.746 \| 0.542 \| 0.486 \| 0.462 \| 0.664 \| 0.400 \| 0.581 \| \| Reader \| 0.192 \| 0.178 \| 0.211 \| 0.139 \| 0.032 \| 0.147 \| 0.123 \| 0.029 \| 0.131 \| \| Both \| 0.425 \| 0.433 \| 0.479 \| 0.341 \| 0.259 \| 0.304 \| 0.394 \| 0.214 \| 0.356 \|
Mor1998/distilbert-base-uncased-distilled-clinc	Mor1998	2023-03-22T07:58:27Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-22T07:48:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-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: 0.9470967741935484 --- <!-- 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-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1678 - Accuracy: 0.9471 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.511 \| 1.0 \| 318 \| 1.0358 \| 0.7565 \| \| 0.806 \| 2.0 \| 636 \| 0.5325 \| 0.8803 \| \| 0.4369 \| 3.0 \| 954 \| 0.3103 \| 0.9219 \| \| 0.2731 \| 4.0 \| 1272 \| 0.2269 \| 0.9368 \| \| 0.2042 \| 5.0 \| 1590 \| 0.1968 \| 0.9403 \| \| 0.175 \| 6.0 \| 1908 \| 0.1824 \| 0.9465 \| \| 0.1589 \| 7.0 \| 2226 \| 0.1745 \| 0.9465 \| \| 0.1498 \| 8.0 \| 2544 \| 0.1708 \| 0.9468 \| \| 0.1445 \| 9.0 \| 2862 \| 0.1686 \| 0.9461 \| \| 0.1425 \| 10.0 \| 3180 \| 0.1678 \| 0.9471 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
easyNLP/distilbert-base-uncased-finetuned-emotion	easyNLP	2023-03-22T07:45:07Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-23T08:32:13Z	--- 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: 0.2078 - Accuracy: 0.9225 - F1: 0.9228 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7799 \| 1.0 \| 250 \| 0.2978 \| 0.9045 \| 0.9020 \| \| 0.2346 \| 2.0 \| 500 \| 0.2078 \| 0.9225 \| 0.9228 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.10.3
domenicrosati/led-base-16384-biolaysum-both-with_references	domenicrosati	2023-03-22T07:41:03Z	4	0	transformers	[ "transformers", "pytorch", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-03-20T17:44:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: led-base-16384-biolaysum-both-with_references 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. --> # led-base-16384-biolaysum-both-with_references This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1595 - Rouge1: 0.4548 - Rouge2: 0.1555 - Rougel: 0.2435 - Rougelsum: 0.2435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 2.2751 \| 0.69 \| 5000 \| 2.2219 \| 0.4488 \| 0.1496 \| 0.2392 \| 0.2392 \| \| 2.0407 \| 1.37 \| 10000 \| 2.1595 \| 0.4548 \| 0.1555 \| 0.2435 \| 0.2435 \| \| 1.9246 \| 2.06 \| 15000 \| 2.1263 \| 0.4537 \| 0.1522 \| 0.2395 \| 0.2396 \| \| 1.9066 \| 2.75 \| 20000 \| 2.1091 \| 0.4562 \| 0.1538 \| 0.2409 \| 0.2409 \| \| 1.7802 \| 3.43 \| 25000 \| 2.0998 \| 0.4539 \| 0.1523 \| 0.2411 \| 0.2411 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.12.1
dhanyaXchandra/femasturboobs	dhanyaXchandra	2023-03-22T07:14:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T07:13:30Z	--- license: creativeml-openrail-m ---
dhanyaXchandra/cameltoe	dhanyaXchandra	2023-03-22T07:13:01Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T07:08:55Z	--- license: creativeml-openrail-m ---
Splend1dchan/canine-c-squad	Splend1dchan	2023-03-22T07:09:39Z	89	0	transformers	[ "transformers", "pytorch", "safetensors", "canine", "question-answering", "endpoints_compatible", "region:us" ]	question-answering	2022-04-08T14:16:41Z	python run_squad.py \ --model_name_or_path google/canine-c \ --do_train \ --do_eval \ --per_gpu_train_batch_size 1 \ --per_gpu_eval_batch_size 1 \ --gradient_accumulation_steps 128 \ --learning_rate 3e-5 \ --num_train_epochs 3 \ --max_seq_length 1024 \ --doc_stride 128 \ --max_answer_length 240 \ --output_dir canine-c-squad \ --model_type bert { "_name_or_path": "google/canine-c", "architectures": [ "CanineForQuestionAnswering" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 57344, "downsampling_rate": 4, "eos_token_id": 57345, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "local_transformer_stride": 128, "max_position_embeddings": 16384, "model_type": "canine", "num_attention_heads": 12, "num_hash_buckets": 16384, "num_hash_functions": 8, "num_hidden_layers": 12, "pad_token_id": 0, "torch_dtype": "float32", "transformers_version": "4.19.0.dev0", "type_vocab_size": 16, "upsampling_kernel_size": 4, "use_cache": true } {'exact': 58.893093661305585, 'f1': 72.18823344945899}
Mor1998/distilbert-base-uncased-finetuned-clinc	Mor1998	2023-03-22T07:07:01Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-03-21T03:56:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9183870967741935 --- <!-- 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-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9184 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2896 \| 1.0 \| 318 \| 3.2890 \| 0.7432 \| \| 2.6284 \| 2.0 \| 636 \| 1.8756 \| 0.8377 \| \| 1.5483 \| 3.0 \| 954 \| 1.1572 \| 0.8961 \| \| 1.015 \| 4.0 \| 1272 \| 0.8573 \| 0.9132 \| \| 0.7953 \| 5.0 \| 1590 \| 0.7721 \| 0.9184 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3
dhanyaXchandra/skirtlift	dhanyaXchandra	2023-03-22T07:00:56Z	0	2	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T06:58:13Z	--- license: creativeml-openrail-m ---
pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555	pfunk	2023-03-22T06:59:25Z	0	0	cleanrl	[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-03-22T06:59:16Z	--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.20 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) DQPN_freq Agent Playing PongNoFrameskip-v4 This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x2.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x2]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x2 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x2 --policy-network-frequency 2000 --seed 555 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x2', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 2000, 'policy_tau': 1.0, 'save_model': True, 'seed': 555, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```
dhanyaXchandra/breastinclassbetter	dhanyaXchandra	2023-03-22T06:51:04Z	0	2	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T06:48:01Z	--- license: creativeml-openrail-m ---
LKINGKK/2131	LKINGKK	2023-03-22T06:50:54Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-03-22T06:50:16Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dhanyaXchandra/creampiev11	dhanyaXchandra	2023-03-22T06:47:34Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-03-22T06:46:27Z	--- license: creativeml-openrail-m ---
vietgpt/bert-30M-cased	vietgpt	2023-03-22T06:47:30Z	6	0	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "vi", "dataset:hieunguyen1053/binhvq-news-corpus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-02-27T20:38:15Z	--- license: apache-2.0 datasets: - hieunguyen1053/binhvq-news-corpus language: - vi library_name: transformers pipeline_tag: fill-mask widget: - text: "Tôi là <mask> viên trường Đại học Tôn Đức Thắng" example_title: "Example 1" ---

sofia425/khipu-finetuned-amazon_reviews_multi

sofia425

2023-03-22T17:52:41Z

3

0

transformers

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

text-classification

2023-03-22T17:47:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9085 ---  # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2836 - Accuracy: 0.9085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2305 | 1.0 | 63 | 0.2953 | 0.895 | | 0.196 | 2.0 | 126 | 0.2836 | 0.9085 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

mathichpp/khipu-finetuned-amazon_reviews_multi

mathichpp

2023-03-22T17:52:05Z

5

0

transformers

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

text-classification

2023-03-22T17:48:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9025 ---  # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2815 - Accuracy: 0.9025 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.254 | 1.0 | 63 | 0.2662 | 0.9067 | | 0.2024 | 2.0 | 126 | 0.2815 | 0.9025 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

brianlorenzo/TALLER-IA-Comentarios-De-Amazon

brianlorenzo

2023-03-22T17:52:04Z

7

0

transformers

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

text-classification

2023-03-22T17:48:25Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: TALLER-IA-Comentarios-De-Amazon results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.90375 ---  # TALLER-IA-Comentarios-De-Amazon This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3000 - Accuracy: 0.9038 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2229 | 1.0 | 63 | 0.2589 | 0.908 | | 0.2068 | 2.0 | 126 | 0.3000 | 0.9038 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

jp9999/house

jp9999

2023-03-22T17:51:47Z

0

null

[ "region:us" ]

null

2023-03-22T17:50:47Z

create a house on a 700 sqm lot with 8 car garage and a pool on the second floor, 5 bedrooms garden

leinho/khipu-finetuned-amazon_reviews_multi

leinho

2023-03-22T17:51:46Z

3

0

transformers

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

text-classification

2023-03-22T17:47:00Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.90725 ---  # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2864 - Accuracy: 0.9073 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2609 | 1.0 | 63 | 0.2640 | 0.905 | | 0.1918 | 2.0 | 126 | 0.2864 | 0.9073 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

maleperezt/khipu-finetuned-amazon_reviews_multi

maleperezt

2023-03-22T17:50:43Z

3

0

transformers

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

text-classification

2023-03-22T17:46:21Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: khipu-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.9055 ---  # khipu-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.3014 - Accuracy: 0.9055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2226 | 1.0 | 63 | 0.2641 | 0.9085 | | 0.1862 | 2.0 | 126 | 0.3014 | 0.9055 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

sd-concepts-library/ahx-beta-41b373e

sd-concepts-library

2023-03-22T17:48:15Z

0

null

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

null

2023-03-22T17:48:14Z

--- license: mit --- ### ahx-beta-41b373e on Stable Diffusion This is the `<ahx-beta-41b373e>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<ahx-beta-41b373e> 0](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/6.jpeg) ![<ahx-beta-41b373e> 1](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/5.jpeg) ![<ahx-beta-41b373e> 2](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/2.jpeg) ![<ahx-beta-41b373e> 3](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/0.jpeg) ![<ahx-beta-41b373e> 4](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/3.jpeg) ![<ahx-beta-41b373e> 5](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/4.jpeg) ![<ahx-beta-41b373e> 6](https://huggingface.co/sd-concepts-library/ahx-beta-41b373e/resolve/main/concept_images/1.jpeg)

facebook/esmfold_v1

facebook

2023-03-22T17:39:28Z

8,997,176

27

transformers

[ "transformers", "pytorch", "esm", "license:mit", "endpoints_compatible", "region:us" ]

null

2022-11-01T18:24:14Z

--- license: mit --- # ESMFold ESMFold is a state-of-the-art end-to-end protein folding model based on an ESM-2 backbone. It does not require any lookup or MSA step, and therefore does not require any external databases to be present in order to make predictions. As a result, inference time is very significantly faster than AlphaFold2. For details on the model architecture and training, please refer to the [accompanying paper](https://www.science.org/doi/10.1126/science.ade2574). If you're interested in using ESMFold in practice, please check out the associated [tutorial notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb).

nguyenvulebinh/mbart-large-50-latin-only

nguyenvulebinh

2023-03-22T17:30:12Z

14

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "mbart-50", "multilingual", "en", "arxiv:2008.00401", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-22T17:19:04Z

--- language: - multilingual - en license: mit tags: - mbart-50 --- # mBART-50 mBART-50 is a multilingual Sequence-to-Sequence model pre-trained using the "Multilingual Denoising Pretraining" objective. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. ## Model description mBART-50 is a multilingual Sequence-to-Sequence model. It was introduced to show that multilingual translation models can be created through multilingual fine-tuning. Instead of fine-tuning on one direction, a pre-trained model is fine-tuned on many directions simultaneously. mBART-50 is created using the original mBART model and extended to add extra 25 languages to support multilingual machine translation models of 50 languages. The pre-training objective is explained below. **Multilingual Denoising Pretraining**: The model incorporates N languages by concatenating data: `D = {D1, ..., DN }` where each Di is a collection of monolingual documents in language `i`. The source documents are noised using two schemes, first randomly shuffling the original sentences' order, and second a novel in-filling scheme, where spans of text are replaced with a single mask token. The model is then tasked to reconstruct the original text. 35% of each instance's words are masked by random sampling a span length according to a Poisson distribution `(λ = 3.5)`. The decoder input is the original text with one position offset. A language id symbol `LID` is used as the initial token to predict the sentence. ## Checking ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained('facebook/mbart-large-50') tokenizer = AutoTokenizer.from_pretrained('facebook/mbart-large-50') src_text = "UN Chief Says There Is <mask> Military Solution <mask> Syria" encoded_hi = tokenizer(src_text, return_tensors="pt") generated_output = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"], return_dict_in_generate=True, return_dict=True, output_hidden_states=True) text_output = tokenizer.batch_decode(generated_output.sequences, skip_special_tokens=True) new_model = AutoModelForSeq2SeqLM.from_pretrained('nguyenvulebinh/mbart-large-50-latin-only') new_tokenizer = AutoTokenizer.from_pretrained('nguyenvulebinh/mbart-large-50-latin-only') new_encoded_hi = new_tokenizer(src_text, return_tensors="pt") new_generated_output = new_model.generate(**new_encoded_hi, forced_bos_token_id=new_tokenizer.lang_code_to_id["en_XX"], return_dict_in_generate=True, return_dict=True, output_hidden_states=True) new_text_output = new_tokenizer.batch_decode(new_generated_output.sequences, skip_special_tokens=True) assert text_output == new_text_output assert torch.equal(generated_output.encoder_hidden_states[-1], new_generated_output.encoder_hidden_states[-1]) assert torch.equal(generated_output.decoder_hidden_states[-1][-1], new_generated_output.decoder_hidden_states[-1][-1]) print(new_text_output) # ['UN Chief Says There Is No Military Solution to the War in Syria'] ``` ## Languages covered English (en_XX) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

AnaniyaX/decision-distilbert-uncased

AnaniyaX

2023-03-22T17:24:16Z

9

0

transformers

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

text-classification

2023-03-21T19:21:09Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: AnaniyaX/decision-distilbert-uncased results: [] datasets: - textvqa - squad widget: - text: 'What does the sign says' example_title: 'Visual Question Example 1' - text: 'What does string theory talks about' example_title: 'Textual Question Example 1' ---  # AnaniyaX/decision-distilbert-uncased This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on textvqa and squad. It achieves the following results on the evaluation set: - Train Loss: 0.0097 - Train Accuracy: 0.9976 - Epoch: 9 ## Model description The Text-Visual Question Classifier is a Hugging Face model that can classify questions as either text-based or visual-based. It uses a natural language processing and techniques to analyze the question and determine its type. The model has been trained on a large dataset of questions labeled as either text-based or visual-based, and has achieved high accuracy in identifying the correct type of question. ## Intended uses & limitations #### Applications This model can be used in various applications such as chatbots, virtual assistants, search engines, and recommendation systems. For example, it can help chatbots to provide more accurate responses by understanding the type of question being asked. It can also help search engines to retrieve more relevant results by filtering out irrelevant content based on the type of question. #### Limitations: The model may not perform well on questions that are ambiguous or have multiple interpretations. It may also be biased towards certain types of questions based on the training data. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-06, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train Accuracy | Epoch | |:----------:|:--------------:|:-----:| | 0.1914 | 0.9444 | 0 | | 0.0711 | 0.9768 | 1 | | 0.0531 | 0.9826 | 2 | | 0.0427 | 0.9868 | 3 | | 0.0330 | 0.9904 | 4 | | 0.0264 | 0.9923 | 5 | | 0.0195 | 0.9947 | 6 | | 0.0149 | 0.9960 | 7 | | 0.0123 | 0.9965 | 8 | | 0.0097 | 0.9976 | 9 | ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

livingbody/FlyingDunhuang

livingbody

2023-03-22T17:21:59Z

0

null

[ "paddlepaddle", "stable-diffusion", "stable-diffusion-ppdiffusers", "text-to-image", "ppdiffusers", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-03-22T14:09:49Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of FlyingDunhuang tags: - stable-diffusion - stable-diffusion-ppdiffusers - text-to-image - ppdiffusers - lora inference: false --- # LoRA DreamBooth - livingbody/FlyingDunhuang 本仓库的 LoRA 权重是基于 runwayml/stable-diffusion-v1-5 训练而来的，我们采用[DreamBooth](https://dreambooth.github.io/)的技术并使用 a photo of FlyingDunhuang 文本进行了训练。

system-technologies/biogpt

system-technologies

2023-03-22T17:10:41Z

13

0

transformers

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

text-generation

2023-03-22T16:15:05Z

--- language: en license: mit widget: - text: COVID-19 is duplicated_from: microsoft/biogpt --- ## BioGPT Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> from transformers import BioGptTokenizer, BioGptForCausalLM >>> model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") >>> tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer) >>> set_seed(42) >>> generator("COVID-19 is", max_length=20, num_return_sequences=5, do_sample=True) [{'generated_text': 'COVID-19 is a disease that spreads worldwide and is currently found in a growing proportion of the population'}, {'generated_text': 'COVID-19 is one of the largest viral epidemics in the world.'}, {'generated_text': 'COVID-19 is a common condition affecting an estimated 1.1 million people in the United States alone.'}, {'generated_text': 'COVID-19 is a pandemic, the incidence has been increased in a manner similar to that in other'}, {'generated_text': 'COVID-19 is transmitted via droplets, air-borne, or airborne transmission.'}] ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BioGptTokenizer, BioGptForCausalLM tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` Beam-search decoding: ```python import torch from transformers import BioGptTokenizer, BioGptForCausalLM, set_seed tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") model = BioGptForCausalLM.from_pretrained("microsoft/biogpt") sentence = "COVID-19 is" inputs = tokenizer(sentence, return_tensors="pt") set_seed(42) with torch.no_grad(): beam_output = model.generate(**inputs, min_length=100, max_length=1024, num_beams=5, early_stopping=True ) tokenizer.decode(beam_output[0], skip_special_tokens=True) 'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK), and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and more than 800,000 deaths.' ``` ## Citation If you find BioGPT useful in your research, please cite the following paper: ```latex @article{10.1093/bib/bbac409, author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan}, title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}", journal = {Briefings in Bioinformatics}, volume = {23}, number = {6}, year = {2022}, month = {09}, abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}", issn = {1477-4054}, doi = {10.1093/bib/bbac409}, url = {https://doi.org/10.1093/bib/bbac409}, note = {bbac409}, eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf}, } ```

TRiddle/ppo-SnowballTarget

TRiddle

2023-03-22T16:56:16Z

1

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-03-22T16:56:10Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Find your model_id: TRiddle/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

jfforero/distilbert-base-uncased-finetuned-imdb

jfforero

2023-03-22T16:55:37Z

3

0

transformers

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

fill-mask

2023-03-22T12:58:39Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: jfforero/distilbert-base-uncased-finetuned-imdb results: [] ---  # jfforero/distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8449 - Validation Loss: 2.5443 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8449 | 2.5443 | 0 | ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

Geotrend/bert-base-bg-cased

Geotrend

2023-03-22T16:53:29Z

21

0

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "bg", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:04Z

--- language: bg datasets: wikipedia license: apache-2.0 --- # bert-base-bg-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). ## How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-bg-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-bg-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). ### How to cite ```bibtex @inproceedings{smallermbert, title={Load What You Need: Smaller Versions of Mutlilingual BERT}, author={Abdaoui, Amine and Pradel, Camille and Sigel, Grégoire}, booktitle={SustaiNLP / EMNLP}, year={2020} } ``` ## Contact Please contact [email protected] for any question, feedback or request.

sd-concepts-library/ahx-beta-41b2a57

sd-concepts-library

2023-03-22T16:53:05Z

0

null

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

null

2023-03-22T16:53:03Z

--- license: mit --- ### ahx-beta-41b2a57 on Stable Diffusion This is the `<ahx-beta-41b2a57>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<ahx-beta-41b2a57> 0](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/0.jpeg) ![<ahx-beta-41b2a57> 1](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/3.jpeg) ![<ahx-beta-41b2a57> 2](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/2.jpeg) ![<ahx-beta-41b2a57> 3](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/5.jpeg) ![<ahx-beta-41b2a57> 4](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/4.jpeg) ![<ahx-beta-41b2a57> 5](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/1.jpeg) ![<ahx-beta-41b2a57> 6](https://huggingface.co/sd-concepts-library/ahx-beta-41b2a57/resolve/main/concept_images/6.jpeg)

socialmediaie/TRAC2020_ENG_B_bert-base-uncased

socialmediaie

2023-03-22T16:39:22Z

15

0

transformers

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

text-classification

2022-03-02T23:29:05Z

# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020 Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying. Our trained models as well as evaluation metrics during traing are available at: https://databank.illinois.edu/datasets/IDB-8882752# We also make a few of our models available in HuggingFace's models repository at https://huggingface.co/socialmediaie/, these models can be further fine-tuned on your dataset of choice. Our approach is described in our paper titled: > Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020 NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper. If you plan to use the dataset please cite the following resources: * Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). * Mishra, Shubhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Trained Models for Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8882752_V1. ``` @inproceedings{Mishra2020TRAC, author = {Mishra, Sudhanshu and Prasad, Shivangi and Mishra, Shubhanshu}, booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020)}, title = {{Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}}, year = {2020} } @data{illinoisdatabankIDB-8882752, author = {Mishra, Shubhanshu and Prasad, Shivangi and Mishra, Shubhanshu}, doi = {10.13012/B2IDB-8882752_V1}, publisher = {University of Illinois at Urbana-Champaign}, title = {{Trained models for Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}}, url = {https://doi.org/10.13012/B2IDB-8882752{\_}V1}, year = {2020} } ``` ## Usage The models can be used via the following code: ```python from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification import torch from pathlib import Path from scipy.special import softmax import numpy as np import pandas as pd TASK_LABEL_IDS = { "Sub-task A": ["OAG", "NAG", "CAG"], "Sub-task B": ["GEN", "NGEN"], "Sub-task C": ["OAG-GEN", "OAG-NGEN", "NAG-GEN", "NAG-NGEN", "CAG-GEN", "CAG-NGEN"] } model_version="databank" # other option is hugging face library if model_version == "databank": # Make sure you have downloaded the required model file from https://databank.illinois.edu/datasets/IDB-8882752 # Unzip the file at some model_path (we are using: "databank_model") model_path = next(Path("databank_model").glob("./*/output/*/model")) # Assuming you get the following type of structure inside "databank_model" # 'databank_model/ALL/Sub-task C/output/bert-base-multilingual-uncased/model' lang, task, _, base_model, _ = model_path.parts tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForSequenceClassification.from_pretrained(model_path) else: lang, task, base_model = "ALL", "Sub-task C", "bert-base-multilingual-uncased" base_model = f"socialmediaie/TRAC2020_{lang}_{lang.split()[-1]}_{base_model}" tokenizer = AutoTokenizer.from_pretrained(base_model) model = AutoModelForSequenceClassification.from_pretrained(base_model) # For doing inference set model in eval mode model.eval() # If you want to further fine-tune the model you can reset the model to model.train() task_labels = TASK_LABEL_IDS[task] sentence = "This is a good cat and this is a bad dog." processed_sentence = f"{tokenizer.cls_token} {sentence}" tokens = tokenizer.tokenize(sentence) indexed_tokens = tokenizer.convert_tokens_to_ids(tokens) tokens_tensor = torch.tensor([indexed_tokens]) with torch.no_grad(): logits, = model(tokens_tensor, labels=None) logits preds = logits.detach().cpu().numpy() preds_probs = softmax(preds, axis=1) preds = np.argmax(preds_probs, axis=1) preds_labels = np.array(task_labels)[preds] print(dict(zip(task_labels, preds_probs[0])), preds_labels) """You should get an output as follows: ({'CAG-GEN': 0.06762535, 'CAG-NGEN': 0.03244293, 'NAG-GEN': 0.6897794, 'NAG-NGEN': 0.15498641, 'OAG-GEN': 0.034373745, 'OAG-NGEN': 0.020792078}, array(['NAG-GEN'], dtype='<U8')) """ ```

TheAbyssYouSee/QW5pbWVsaWsyRA

TheAbyssYouSee

2023-03-22T16:35:14Z

0

23

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-21T07:22:16Z

--- license: creativeml-openrail-m ---

agucci/my-model

agucci

2023-03-22T16:29:04Z

0

sklearn

[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]

tabular-classification

2023-03-22T16:21:05Z

--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: example.pkl widget: structuredData: area error: - 30.29 - 96.05 - 48.31 compactness error: - 0.01911 - 0.01652 - 0.01484 concave points error: - 0.01037 - 0.0137 - 0.01093 concavity error: - 0.02701 - 0.02269 - 0.02813 fractal dimension error: - 0.003586 - 0.001698 - 0.002461 mean area: - 481.9 - 1130.0 - 748.9 mean compactness: - 0.1058 - 0.1029 - 0.1223 mean concave points: - 0.03821 - 0.07951 - 0.08087 mean concavity: - 0.08005 - 0.108 - 0.1466 mean fractal dimension: - 0.06373 - 0.05461 - 0.05796 mean perimeter: - 81.09 - 123.6 - 101.7 mean radius: - 12.47 - 18.94 - 15.46 mean smoothness: - 0.09965 - 0.09009 - 0.1092 mean symmetry: - 0.1925 - 0.1582 - 0.1931 mean texture: - 18.6 - 21.31 - 19.48 perimeter error: - 2.497 - 5.486 - 3.094 radius error: - 0.3961 - 0.7888 - 0.4743 smoothness error: - 0.006953 - 0.004444 - 0.00624 symmetry error: - 0.01782 - 0.01386 - 0.01397 texture error: - 1.044 - 0.7975 - 0.7859 worst area: - 677.9 - 1866.0 - 1156.0 worst compactness: - 0.2378 - 0.2336 - 0.2394 worst concave points: - 0.1015 - 0.1789 - 0.1514 worst concavity: - 0.2671 - 0.2687 - 0.3791 worst fractal dimension: - 0.0875 - 0.06589 - 0.08019 worst perimeter: - 96.05 - 165.9 - 124.9 worst radius: - 14.97 - 24.86 - 19.26 worst smoothness: - 0.1426 - 0.1193 - 0.1546 worst symmetry: - 0.3014 - 0.2551 - 0.2837 worst texture: - 24.64 - 26.58 - 26.0 --- # Model description [More Information Needed] ## Intended uses & limitations [More Information Needed] ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters. <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------|---------| | ccp_alpha | 0.0 | | class_weight | | | criterion | gini | | max_depth | | | max_features | | | max_leaf_nodes | | | min_impurity_decrease | 0.0 | | min_impurity_split | | | min_samples_leaf | 1 | | min_samples_split | 2 | | min_weight_fraction_leaf | 0.0 | | random_state | | | splitter | best | </details> ### Model Plot The model plot is below. <style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class="sk-top-container"><div class="sk-container"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="12a6e028-23d4-4552-93d7-bca81eacd271" type="checkbox" checked><label class="sk-toggleable__label" for="12a6e028-23d4-4552-93d7-bca81eacd271">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div> ## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.935673 | | f1 score | 0.935673 | # How to Get Started with the Model [More Information Needed] # Model Card Authors This model card is written by following authors: [More Information Needed] # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ``` # citation_bibtex bibtex @inproceedings{...,year={2020}} # get_started_code import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) # model_card_authors skops_user # limitations This model is not ready to be used in production. # model_description This is a DecisionTreeClassifier model trained on breast cancer dataset. # eval_method The model is evaluated using test split, on accuracy and F1 score with macro average. # confusion_matrix ![confusion_matrix](confusion_matrix.png)

jamesportis/vit-base-patch16-224-finetuned-flower

jamesportis

2023-03-22T16:16:44Z

21

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-03-22T16:09:11Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower results: [] ---  # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.1+cu116 - Datasets 2.7.1 - Tokenizers 0.13.2

whyoke/tumore_test

whyoke

2023-03-22T16:16:42Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]

null

2023-03-22T15:14:24Z

--- license: other tags: - generated_from_trainer model-index: - name: tumore_test results: [] ---  # tumore_test This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0 - eval_mean_iou: nan - eval_mean_accuracy: nan - eval_overall_accuracy: nan - eval_per_category_iou: [nan] - eval_per_category_accuracy: [nan] - eval_runtime: 338.6408 - eval_samples_per_second: 1.161 - eval_steps_per_second: 0.582 - epoch: 1.27 - step: 2000 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.10.1 - Tokenizers 0.13.2

arrandi/ppo-LunarLander-v2

arrandi

2023-03-22T16:11:34Z

1

0

stable-baselines3

[ "stable-baselines3", "tensorboard", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-12-16T12:55:37Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 282.45 +/- 21.16 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

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

Handun

2023-03-22T16:11:25Z

3

0

transformers

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

token-classification

2023-03-22T11:05:54Z

--- 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: 0.1634 - F1: 0.8588 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2889 | 1.0 | 715 | 0.1818 | 0.8338 | | 0.1435 | 2.0 | 1430 | 0.1624 | 0.8531 | | 0.0933 | 3.0 | 2145 | 0.1634 | 0.8588 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

Mahmoud22/AraClassificationModel2

Mahmoud22

2023-03-22T16:07:23Z

3

0

transformers

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

text-classification

2023-03-22T16:06:39Z

--- tags: - generated_from_trainer model-index: - name: result results: [] ---  # result This model is a fine-tuned version of [Mahmoud22/AraClassificationModel](https://huggingface.co/Mahmoud22/AraClassificationModel) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0295 - F1-macro: 0.9856 ## 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: 8e-05 - train_batch_size: 64 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | F1-macro | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1818 | 1.0 | 1630 | 0.0996 | 0.9661 | | 0.0899 | 2.0 | 3260 | 0.0398 | 0.9837 | | 0.0326 | 3.0 | 4890 | 0.0218 | 0.9893 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2

alespalla/distillbert_conv_quality_score

alespalla

2023-03-22T15:57:44Z

17

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "en", "dataset:conv_ai_2", "doi:10.57967/hf/0435", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-11T16:39:18Z

--- license: apache-2.0 tags: - transformers - pytorch datasets: - conv_ai_2 model-index: - name: distillbert_conv_quality_score results: [] language: - en --- # distillbert_conv_quality_score This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conv_ai_2 dataset. It was trained to generate a score (in the [0, 1] range) from a conversation It achieves the following results on the evaluation set: - training/loss: 0.0165 - validation/loss: 0.0149 ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name = "alespalla/distillbert_conv_quality_score" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) conversation = ''' Q: Begin A: lol ! do you think it is strange to feel like you have been through life before ? Q: Hellow A: I don't understand you 🙈. Also, try to guess: i like to ... Q: How are you? A: make time stop, funny you :) Q: What is your name? A: jessie. hows your day going ? 😃 ''' score = model(**tokenizer(conversation, return_tensors='pt')).logits.item() print(f"Score: {score}") ``` ## Training and evaluation data The training data was generated from `conv_ai_2` using the following function ```python from datasets import load_dataset def get_dataset(regression=False): db = load_dataset("conv_ai_2") def generate_converation(elem): text = "" for idx, txt in enumerate(elem["dialog"]): if idx % 2: text += f"A: {txt['text']}\n" else: text += f"Q: {txt['text']}\n" if regression: return {'text': text, "labels": (elem['eval_score'] - 1)/4} return {'text': text, "labels": elem['eval_score'] - 1} db = db.filter(lambda example: example["eval_score"] > 0) db = db.map(generate_converation, remove_columns=db['train'].column_names) db = db['train'].train_test_split(test_size=0.2).shuffle(42) return db ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - epochs: 40 - batch_size: 16 - learning_rate: 0.0002 - eval_steps: 82 - log_steps: 82 - save_steps: 41 - gradient_accumulation_steps: 1 - warmup_steps: 0 ### Training results | step | training/loss | validation/loss | |:----:|:-------------:|:---------------:| | 81 | 0.1020 | 0.0794 | | 163 | 0.0800 | 0.0713 | | 245 | 0.0553 | 0.0491 | | 327 | 0.0362 | 0.0440 | | 409 | 0.0282 | 0.0352 | | 491 | 0.0282 | 0.0412 | | 573 | 0.0256 | 0.0293 | | 655 | 0.0238 | 0.0252 | | 737 | 0.0175 | 0.0226 | | 819 | 0.0154 | 0.0228 | | 901 | 0.0116 | 0.0205 | | 983 | 0.0160 | 0.0202 | | 1065 | 0.0146 | 0.0240 | | 1147 | 0.0182 | 0.0180 | | 1229 | 0.0171 | 0.0192 | | 1311 | 0.0091 | 0.0174 | | 1393 | 0.0171 | 0.0158 | | 1475 | 0.0137 | 0.0158 | | 1557 | 0.0158 | 0.0148 | | 1639 | 0.0165 | 0.0149 | ### Framework versions - Transformers 4.26.1 - Datasets 2.10.1 - Tokenizers 0.13.2

pabloac31/rl_course_vizdoom_health_gathering_supreme

pabloac31

2023-03-22T15:48:41Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T15:48:33Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 12.34 +/- 4.87 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r pabloac31/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

LorenzoDeMattei/GePpeTto

LorenzoDeMattei

2023-03-22T15:39:46Z

3,547

13

transformers

[ "transformers", "pytorch", "jax", "safetensors", "gpt2", "text-generation", "it", "arxiv:2004.14253", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-03-02T23:29:04Z

--- language: it --- # GePpeTto GPT2 Model 🇮🇹 Pretrained GPT2 117M model for Italian. You can find further details in the paper: Lorenzo De Mattei, Michele Cafagna, Felice Dell’Orletta, Malvina Nissim, Marco Guerini "GePpeTto Carves Italian into a Language Model", arXiv preprint. Pdf available at: https://arxiv.org/abs/2004.14253 ## Pretraining Corpus The pretraining set comprises two main sources. The first one is a dump of Italian Wikipedia (November 2019), consisting of 2.8GB of text. The second one is the ItWac corpus (Baroni et al., 2009), which amounts to 11GB of web texts. This collection provides a mix of standard and less standard Italian, on a rather wide chronological span, with older texts than the Wikipedia dump (the latter stretches only to the late 2000s). ## Pretraining details This model was trained using GPT2's Hugging Face implemenation on 4 NVIDIA Tesla T4 GPU for 620k steps. Training parameters: - GPT-2 small configuration - vocabulary size: 30k - Batch size: 32 - Block size: 100 - Adam Optimizer - Initial learning rate: 5e-5 - Warm up steps: 10k ## Perplexity scores | Domain | Perplexity | |---|---| | Wikipedia | 26.1052 | | ItWac | 30.3965 | | Legal | 37.2197 | | News | 45.3859 | | Social Media | 84.6408 | For further details, qualitative analysis and human evaluation check out: https://arxiv.org/abs/2004.14253 ## Load Pretrained Model You can use this model by installing Huggingface library `transformers`. And you can use it directly by initializing it like this: ```python from transformers import GPT2Tokenizer, GPT2Model model = GPT2Model.from_pretrained('LorenzoDeMattei/GePpeTto') tokenizer = GPT2Tokenizer.from_pretrained( 'LorenzoDeMattei/GePpeTto', ) ``` ## Example using GPT2LMHeadModel ```python from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, GPT2Tokenizer tokenizer = AutoTokenizer.from_pretrained("LorenzoDeMattei/GePpeTto") model = AutoModelWithLMHead.from_pretrained("LorenzoDeMattei/GePpeTto") text_generator = pipeline('text-generation', model=model, tokenizer=tokenizer) prompts = [ "Wikipedia Geppetto", "Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso"] samples_outputs = text_generator( prompts, do_sample=True, max_length=50, top_k=50, top_p=0.95, num_return_sequences=3 ) for i, sample_outputs in enumerate(samples_outputs): print(100 * '-') print("Prompt:", prompts[i]) for sample_output in sample_outputs: print("Sample:", sample_output['generated_text']) print() ``` Output is, ``` ---------------------------------------------------------------------------------------------------- Prompt: Wikipedia Geppetto Sample: Wikipedia Geppetto rosso (film 1920) Geppetto rosso ("The Smokes in the Black") è un film muto del 1920 diretto da Henry H. Leonard. Il film fu prodotto dalla Selig Poly Sample: Wikipedia Geppetto Geppetto ("Geppetto" in piemontese) è un comune italiano di 978 abitanti della provincia di Cuneo in Piemonte. L'abitato, che si trova nel versante valtellinese, si sviluppa nella Sample: Wikipedia Geppetto di Natale (romanzo) Geppetto di Natale è un romanzo di Mario Caiano, pubblicato nel 2012. ---------------------------------------------------------------------------------------------------- Prompt: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso. Il burattino riesce a scappare. Dopo aver trovato un prezioso sacchetto si reca Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso, e l'unico che lo possiede, ma, di fronte a tutte queste prove Sample: Maestro Ciliegia regala il pezzo di legno al suo amico Geppetto, il quale lo prende per fabbricarsi un burattino maraviglioso: - A voi gli occhi, le guance! A voi il mio pezzo! ``` ## Citation Please use the following bibtex entry: ``` @misc{mattei2020geppetto, title={GePpeTto Carves Italian into a Language Model}, author={Lorenzo De Mattei and Michele Cafagna and Felice Dell'Orletta and Malvina Nissim and Marco Guerini}, year={2020}, eprint={2004.14253}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## References Marco Baroni, Silvia Bernardini, Adriano Ferraresi, and Eros Zanchetta. 2009. The WaCky wide web: a collection of very large linguistically processed webcrawled corpora. Language resources and evaluation, 43(3):209–226.

duyduong9htv/phobert-qa-finetuned-viet-qa

duyduong9htv

2023-03-22T15:28:44Z

33

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]

question-answering

2022-09-19T22:23:22Z

--- tags: - generated_from_trainer model-index: - name: phobert-qa-finetuned-viet-qa results: [] ---  # phobert-qa-finetuned-viet-qa This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5288 ## 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6004 | 1.0 | 2027 | 1.5128 | | 1.3018 | 2.0 | 4054 | 1.4657 | | 1.1052 | 3.0 | 6081 | 1.4754 | | 0.9502 | 4.0 | 8108 | 1.5288 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa

vocabtrimmer

2023-03-22T15:24:09Z

3

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "question answering", "fr", "dataset:lmqg/qg_frquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-21T19:02:06Z

--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: fr datasets: - lmqg/qg_frquad pipeline_tag: text2text-generation tags: - question answering widget: - text: "question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu." example_title: "Question Answering Example 1" - text: "question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l'électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D'un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu'être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire." example_title: "Question Answering Example 2" model-index: - name: vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_frquad type: default args: default metrics: - name: BLEU4 (Question Answering) type: bleu4_question_answering value: 14.81 - name: ROUGE-L (Question Answering) type: rouge_l_question_answering value: 26.59 - name: METEOR (Question Answering) type: meteor_question_answering value: 20.09 - name: BERTScore (Question Answering) type: bertscore_question_answering value: 88.11 - name: MoverScore (Question Answering) type: moverscore_question_answering value: 69.17 - name: AnswerF1Score (Question Answering) type: answer_f1_score__question_answering value: 40.01 - name: AnswerExactMatch (Question Answering) type: answer_exact_match_question_answering value: 23.71 --- # Model Card of `vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa` This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [vocabtrimmer/mt5-small-trimmed-fr-120000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa") # model prediction answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa") output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` ## Evaluation - ***Metric (Question Answering)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:-----------------|--------:|:--------|:-----------------------------------------------------------------| | AnswerExactMatch | 23.71 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | AnswerF1Score | 40.01 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | BERTScore | 88.11 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_1 | 23.93 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_2 | 19.95 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_3 | 17.18 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | Bleu_4 | 14.81 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | METEOR | 20.09 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | MoverScore | 69.17 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | | ROUGE_L | 26.59 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: vocabtrimmer/mt5-small-trimmed-fr-120000 - max_length: 512 - max_length_output: 32 - epoch: 27 - batch: 32 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-120000-frquad-qa/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

cardiffnlp/xlm-roberta-base-tweet-sentiment-de

cardiffnlp

2023-03-22T14:57:00Z

17

0

transformers

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

text-classification

2023-03-06T09:14:30Z

# `cardiffnlp/xlm-roberta-base-tweet-sentiment-de` This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (german). Following metrics are computed on the `test` split of [cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(german). | | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy | |---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:| | 0 | 73.22 | 73.22 | 73.22 | 73.18 | 73.22 | 73.18 | 73.22 | Check the result file [here](https://huggingface.co/cardiffnlp/xlm-roberta-base-tweet-sentiment-de/raw/main/eval.json).

JessicaHsu/rl_course_vizdoom_health_gathering_supreme

JessicaHsu

2023-03-22T14:47:26Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T14:47:17Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.08 +/- 5.10 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r JessicaHsu/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.9.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

eolang/DRL-vizdoome_health_gathering_supreme

eolang

2023-03-22T14:39:05Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T07:15:06Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 18.61 +/- 4.52 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r eolang/DRL-vizdoome_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

helenai/wav2vec2-base-superb-ks-jpqd-ov

helenai

2023-03-22T14:31:21Z

4

0

transformers

[ "transformers", "openvino", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:superb", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-03-09T08:14:06Z

--- license: apache-2.0 tags: - audio-classification - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: jpqd-wav2vec2-base-ft-keyword-spotting results: [] ---  # jpqd-wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset, using [superb/wav2vec2-base-superb-ks](https://huggingface.co/superb/wav2vec2-base-superb-ks) as a teacher model It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the JPQD image classification example. It achieves the following results on the evaluation set: - Loss: 0.5632 - Accuracy: 0.9756 ## 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: 7e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.5 - num_epochs: 12.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2245 | 1.0 | 399 | 2.2351 | 0.6209 | | 6.9856 | 2.0 | 798 | 7.0597 | 0.7354 | | 10.013 | 3.0 | 1197 | 9.8779 | 0.8069 | | 11.3484 | 4.0 | 1596 | 11.1949 | 0.8719 | | 11.6849 | 5.0 | 1995 | 11.5479 | 0.9014 | | 11.5921 | 6.0 | 2394 | 11.4193 | 0.9495 | | 0.8911 | 7.0 | 2793 | 0.7334 | 0.9500 | | 0.8965 | 8.0 | 3192 | 0.6553 | 0.9685 | | 0.7198 | 9.0 | 3591 | 0.6213 | 0.9669 | | 0.7372 | 10.0 | 3990 | 0.5929 | 0.9675 | | 0.7004 | 11.0 | 4389 | 0.5720 | 0.9721 | | 0.6195 | 12.0 | 4788 | 0.5632 | 0.9756 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

JessicaHsu/ppo-CartPole-v1

JessicaHsu

2023-03-22T14:11:45Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T13:52:25Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -138.56 +/- 89.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': '__file__' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 80000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'JessicaHsu/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```

Piemag/FirstPPO-LunarLander-v2

Piemag

2023-03-22T14:06:45Z

4

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T14:06:25Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.69 +/- 19.67 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

aubmindlab/araelectra-base-generator

aubmindlab

2023-03-22T13:55:02Z

64

2

transformers

[ "transformers", "pytorch", "tf", "tensorboard", "safetensors", "electra", "fill-mask", "ar", "arxiv:1406.2661", "arxiv:2012.15516", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: ar datasets: - wikipedia - Osian - 1.5B-Arabic-Corpus - oscar-arabic-unshuffled - Assafir(private) widget: - text: " عاصمة لبنان هي [MASK] ." --- # AraELECTRA <img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/> **ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). AraELECTRA achieves state-of-the-art results on Arabic QA dataset. For a detailed description, please refer to the AraELECTRA paper [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://arxiv.org/abs/2012.15516). ## How to use the generator in `transformers` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="aubmindlab/araelectra-base-generator", tokenizer="aubmindlab/araelectra-base-generator" ) print( fill_mask(" عاصمة لبنان هي [MASK] .) ) ``` # Preprocessing It is recommended to apply our preprocessing function before training/testing on any dataset. **Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data `pip install arabert`** ```python from arabert.preprocess import ArabertPreprocessor model_name="aubmindlab/araelectra-base" arabert_prep = ArabertPreprocessor(model_name=model_name) text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري" arabert_prep.preprocess(text) >>> output: ولن نبالغ إذا قلنا : إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري ``` # Model Model | HuggingFace Model Name | Size (MB/Params)| ---|:---:|:---: AraELECTRA-base-generator | [araelectra-base-generator](https://huggingface.co/aubmindlab/araelectra-base-generator) | 227MB/60M | AraELECTRA-base-discriminator | [araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) | 516MB/135M | # Compute Model | Hardware | num of examples (seq len = 512) | Batch Size | Num of Steps | Time (in days) ---|:---:|:---:|:---:|:---:|:---: AraELECTRA-base | TPUv3-8 | - | 256 | 2M | 24 # Dataset The pretraining data used for the new AraELECTRA model is also used for **AraGPT2 and AraELECTRA**. The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation) For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled: - OSCAR unshuffled and filtered. - [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01 - [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4) - [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619) - Assafir news articles. Huge thank you for Assafir for giving us the data # TensorFlow 1.x models **You can find the PyTorch, TF2 and TF1 models in HuggingFace's Transformer Library under the ```aubmindlab``` username** - `wget https://huggingface.co/aubmindlab/MODEL_NAME/resolve/main/tf1_model.tar.gz` where `MODEL_NAME` is any model under the `aubmindlab` name # If you used this model please cite us as : ``` @inproceedings{antoun-etal-2021-araelectra, title = "{A}ra{ELECTRA}: Pre-Training Text Discriminators for {A}rabic Language Understanding", author = "Antoun, Wissam and Baly, Fady and Hajj, Hazem", booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop", month = apr, year = "2021", address = "Kyiv, Ukraine (Virtual)", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.wanlp-1.20", pages = "191--195", } ``` # Acknowledgments Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT. # Contacts **Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <[email protected]> | <[email protected]> **Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <[email protected]> | <[email protected]>

pszemraj/pegasus-large-summary-explain

pszemraj

2023-03-22T13:53:07Z

18

4

transformers

[ "transformers", "pytorch", "safetensors", "pegasus", "text2text-generation", "summarization", "en", "dataset:kmfoda/booksum", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-03-02T23:29:05Z

--- language: - en license: apache-2.0 tags: - summarization - pegasus datasets: - kmfoda/booksum metrics: - rouge widget: - text: large earthquakes along a given fault segment do not occur at random intervals because it takes time to accumulate the strain energy for the rupture. The rates at which tectonic plates move and accumulate strain at their boundaries are approximately uniform. Therefore, in first approximation, one may expect that large ruptures of the same fault segment will occur at approximately constant time intervals. If subsequent main shocks have different amounts of slip across the fault, then the recurrence time may vary, and the basic idea of periodic mainshocks must be modified. For great plate boundary ruptures the length and slip often vary by a factor of 2. Along the southern segment of the San Andreas fault the recurrence interval is 145 years with variations of several decades. The smaller the standard deviation of the average recurrence interval, the more specific could be the long term prediction of a future mainshock. example_title: earthquakes - text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates are fed into a neural network that predicts values in the reconstructed domain. Then, this domain is mapped to the sensor domain where sensor measurements are available as supervision. Class and Section Problems Addressed Generalization (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid Representations (Section 3) Computation & memory efficiency, representation capacity, editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section 5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section 6) Edit ability, constraints, regularization. Table 2: The five classes of techniques in the neural field toolbox each addresses problems that arise in learning, inference, and control. (Section 3). We can supervise reconstruction via differentiable forward maps that transform Or project our domain (e.g, 3D reconstruction via 2D images; Section 4) With appropriate network architecture choices, we can overcome neural network spectral biases (blurriness) and efficiently compute derivatives and integrals (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations, and to achieve editable representations (Section 6). Collectively, these classes constitute a ''toolbox'' of techniques to help solve problems with neural fields There are three components in a conditional neural field: (1) An encoder or inference function € that outputs the conditioning latent variable 2 given an observation 0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS a latent code Or feature code_ (2) A mapping function 4 between Z and neural field parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the most probable z given the observations O: argmaxz P(2/0). The decoder maximizes the inverse conditional probability to find the most probable 0 given Z: arg- max P(Olz). We discuss different encoding schemes with different optimality guarantees (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable prior over the sur- face in its reconstruction domain to generalize to the partial observations. A neural network expresses a prior via the function space of its architecture and parameters 0, and generalization is influenced by the inductive bias of this function space (Section 5).' example_title: scientific paper - text: ' the big variety of data coming from diverse sources is one of the key properties of the big data phenomenon. It is, therefore, beneficial to understand how data is generated in various environments and scenarios, before looking at what should be done with this data and how to design the best possible architecture to accomplish this The evolution of IT architectures, described in Chapter 2, means that the data is no longer processed by a few big monolith systems, but rather by a group of services In parallel to the processing layer, the underlying data storage has also changed and became more distributed This, in turn, required a significant paradigm shift as the traditional approach to transactions (ACID) could no longer be supported. On top of this, cloud computing is becoming a major approach with the benefits of reducing costs and providing on-demand scalability but at the same time introducing concerns about privacy, data ownership, etc In the meantime the Internet continues its exponential growth: Every day both structured and unstructured data is published and available for processing: To achieve competitive advantage companies have to relate their corporate resources to external services, e.g. financial markets, weather forecasts, social media, etc While several of the sites provide some sort of API to access the data in a more orderly fashion; countless sources require advanced web mining and Natural Language Processing (NLP) processing techniques: Advances in science push researchers to construct new instruments for observing the universe O conducting experiments to understand even better the laws of physics and other domains. Every year humans have at their disposal new telescopes, space probes, particle accelerators, etc These instruments generate huge streams of data, which need to be stored and analyzed. The constant drive for efficiency in the industry motivates the introduction of new automation techniques and process optimization: This could not be done without analyzing the precise data that describe these processes. As more and more human tasks are automated, machines provide rich data sets, which can be analyzed in real-time to drive efficiency to new levels. Finally, it is now evident that the growth of the Internet of Things is becoming a major source of data. More and more of the devices are equipped with significant computational power and can generate a continuous data stream from their sensors. In the subsequent sections of this chapter, we will look at the domains described above to see what they generate in terms of data sets. We will compare the volumes but will also look at what is characteristic and important from their respective points of view. 3.1 The Internet is undoubtedly the largest database ever created by humans. While several well described; cleaned, and structured data sets have been made available through this medium, most of the resources are of an ambiguous, unstructured, incomplete or even erroneous nature. Still, several examples in the areas such as opinion mining, social media analysis, e-governance, etc, clearly show the potential lying in these resources. Those who can successfully mine and interpret the Internet data can gain unique insight and competitive advantage in their business An important area of data analytics on the edge of corporate IT and the Internet is Web Analytics.' example_title: data science textbook - text: 'Transformer-based models have shown to be very useful for many NLP tasks. However, a major limitation of transformers-based models is its O(n^2)O(n 2) time & memory complexity (where nn is sequence length). Hence, it''s computationally very expensive to apply transformer-based models on long sequences n > 512n>512. Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention try to remedy this problem by approximating the full attention matrix. You can checkout 🤗''s recent blog post in case you are unfamiliar with these models. BigBird (introduced in paper) is one of such recent models to address this issue. BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s attention) and can handle sequences up to a length of 4096 at a much lower computational cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts. BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this post is to give the reader an in-depth understanding of big bird implementation & ease one''s life in using BigBird with 🤗Transformers. But, before going into more depth, it is important to remember that the BigBird''s attention is an approximation of BERT''s full attention and therefore does not strive to be better than BERT''s full attention, but rather to be more efficient. It simply allows to apply transformer-based models to much longer sequences since BERT''s quadratic memory requirement quickly becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention would be preferred over block sparse attention (which we are going to discuss in this post). If you wonder why we need more compute when working with longer sequences, this blog post is just right for you! Some of the main questions one might have when working with standard BERT-like attention include: Do all tokens really have to attend to all other tokens? Why not compute attention only over important tokens? How to decide what tokens are important? How to attend to just a few tokens in a very efficient way? In this blog post, we will try to answer those questions. What tokens should be attended to? We will give a practical example of how attention works by considering the sentence ''BigBird is now available in HuggingFace for extractive question answering''. In BERT-like attention, every word would simply attend to all other tokens. Let''s think about a sensible choice of key tokens that a queried token actually only should attend to by writing some pseudo-code. Will will assume that the token available is queried and build a sensible list of key tokens to attend to. >>> # let''s consider following sentence as an example >>> example = [''BigBird'', ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'', ''question'', ''answering''] >>> # further let''s assume, we''re trying to understand the representation of ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an empty `set` and fill up the tokens of our interest as we proceed in this section. >>> key_tokens = [] # => currently ''available'' token doesn''t have anything to attend Nearby tokens should be important because, in a sentence (sequence of words), the current word is highly dependent on neighboring past & future tokens. This intuition is the idea behind the concept of sliding attention.' example_title: bigbird blog intro inference: parameters: max_length: 64 no_repeat_ngram_size: 2 encoder_no_repeat_ngram_size: 3 repetition_penalty: 2.4 length_penalty: 0.5 num_beams: 4 early_stopping: true model-index: - name: pszemraj/pegasus-large-summary-explain results: - task: type: summarization name: Summarization dataset: name: kmfoda/booksum type: kmfoda/booksum config: kmfoda--booksum split: test metrics: - type: rouge value: 29.1023 name: ROUGE-1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhNjg4YTFlODU5MmVjNGVmNDRmMjQ4M2YyZGNmMWRlYjBhZmVhMTY3ZTUxNDkzNjY0OGVmNWJlNmY1OTkzNCIsInZlcnNpb24iOjF9.E_rVKqB7WEerLeRq6JIVTLZ1TgmsThFQJVKh11WH1qWa-cL3766psPWDKe8mK3lNkjmwbiDW0DZlDt4dm2ATCA - type: rouge value: 6.2441 name: ROUGE-2 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVmZmFlOTgwN2Q3ZWRkZGVkMzU1ZDRkYzU1MWMzMTk1NDM5YTU0MzFjNDljNmZlY2I2NjZmZjcyYjBkZGExZCIsInZlcnNpb24iOjF9.QnuGoMWX8cq5_ukRtiaLRLau_F9XiCjg313GC7Iu1VGK8Kj_9lzU43377VsH0fBWooA1zJjtIK0UA-YpGQQOAA - type: rouge value: 14.7503 name: ROUGE-L verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzJhNzE0YjZiZWQ4NDE1Yjg3ZGJjY2ZmYWEwYzU5MTRhYWNiNTcyODU1NzM5NTZhNjNlNmYwNDVlYmZmYjkxOCIsInZlcnNpb24iOjF9.m5BLUMefXa1KivIIE9-gYKYq5aRRbfpQWazqzXxfCsqqp38Lt0ymk6OwXSlQyB_5oksNHIDFKpJX4wjYx2i7Bw - type: rouge value: 27.2375 name: ROUGE-LSUM verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTY1OTIxMzBkMGJiZmNiNjZjYmQ2MjUwMjBkYTg5Zjc1NjVlZjllNTg0MDM1NTdhZDJlZmIwOTczOGNkZDc5YyIsInZlcnNpb24iOjF9.bThI16mvqhEuGBhdao0w8j03vv9G9Quy-ITRZzalr41zOour9it4oxEPFCvmPf-nLCQkqgWKUDEzgr6Ww8qgBg - type: loss value: 2.979011058807373 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGM0NzM3YTI4Njg4NDY0ZjQzNTZmYTIxYzcxNDBlNzAwNTAxNDE4MTZjYmZmNzYwODU0OWQ1ZjM5YjRmMmFkZiIsInZlcnNpb24iOjF9.EPEP53AoqHz0rjVGStJI2dM7ivxFmOj572I3llWdAoejm3zO1Iq5WDArYsqOse_oLxYCgcqPmNVc5IcLW9x7Dg - type: gen_len value: 467.269 name: gen_len verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjgzYzU2ZjkwN2RhNzJlZmQyZTBlYmUxMTZhNzg0ODMwMjA3OTUzNTIwOWFkZWVmNjVmMTJiZmZhNWFmY2UzZCIsInZlcnNpb24iOjF9.RW5tzk2fcc_m4bgaSopRDFhSR9R8hRaYKrstXH4X5iGP_Xwvhy5Q7-igd2ACnlxIfmtdTmMxLMsvHr5oAZEwDg --- # pszemraj/pegasus-large-summary-explain This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs. It achieves the following results on the evaluation set: - eval_loss: 1.1193 - eval_runtime: 6.6754 - eval_samples_per_second: 27.714 - eval_steps_per_second: 1.798 - epoch: 3.0 - step: 900 A 1-epoch checkpoint can be found at [pszemraj/pegasus-large-book-summary](https://huggingface.co/pszemraj/pegasus-large-book-summary), which is where the second training session started from. ## Model description - After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/booksum) dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of **just** more compact. - This quality (anecdotally) is favourable for learning/comprehension because summarization datasets that simply make the information more compact (* cough * arXiv) can be so dense that the overall time spent trying to _comprehend_ what it is saying can be the same as just reading the original material. ## Intended uses & limitations - standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 4 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0

RafaelEiji/bert_character

RafaelEiji

2023-03-22T13:43:23Z

3

1

transformers

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

fill-mask

2023-01-31T14:02:02Z

--- tags: - generated_from_trainer model-index: - name: from_scratch results: [] ---  # from_scratch This model is a fine-tuned version of [tokenizer/config.json](https://huggingface.co/tokenizer/config.json) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4744 ## 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: 360 - eval_batch_size: 360 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 1.0952 | 0.05 | 20000 | 1.0383 | | 0.936 | 0.1 | 40000 | 0.8852 | | 0.8679 | 0.14 | 60000 | 0.8207 | | 0.8276 | 0.19 | 80000 | 0.7796 | | 0.796 | 0.24 | 100000 | 0.7519 | | 0.7756 | 0.29 | 120000 | 0.7299 | | 0.7545 | 0.33 | 140000 | 0.7103 | | 0.7395 | 0.38 | 160000 | 0.6947 | | 0.7236 | 0.43 | 180000 | 0.6809 | | 0.7143 | 0.48 | 200000 | 0.6705 | | 0.705 | 0.52 | 220000 | 0.6585 | | 0.6904 | 0.57 | 240000 | 0.6479 | | 0.6835 | 0.62 | 260000 | 0.6388 | | 0.672 | 0.67 | 280000 | 0.6290 | | 0.665 | 0.72 | 300000 | 0.6217 | | 0.6581 | 0.76 | 320000 | 0.6136 | | 0.6466 | 0.81 | 340000 | 0.6071 | | 0.6396 | 0.86 | 360000 | 0.6000 | | 0.6343 | 0.91 | 380000 | 0.5940 | | 0.6286 | 0.95 | 400000 | 0.5880 | | 0.6183 | 1.0 | 420000 | 0.5809 | | 0.6134 | 1.05 | 440000 | 0.5757 | | 0.6094 | 1.1 | 460000 | 0.5693 | | 0.6032 | 1.15 | 480000 | 0.5641 | | 0.5954 | 1.19 | 500000 | 0.5596 | | 0.5915 | 1.24 | 520000 | 0.5532 | | 0.5845 | 1.29 | 540000 | 0.5489 | | 0.5823 | 1.34 | 560000 | 0.5437 | | 0.5754 | 1.38 | 580000 | 0.5393 | | 0.573 | 1.43 | 600000 | 0.5345 | | 0.5643 | 1.48 | 620000 | 0.5309 | | 0.5627 | 1.53 | 640000 | 0.5262 | | 0.56 | 1.57 | 660000 | 0.5220 | | 0.5554 | 1.62 | 680000 | 0.5186 | | 0.5507 | 1.67 | 700000 | 0.5152 | | 0.5494 | 1.72 | 720000 | 0.5117 | | 0.5445 | 1.77 | 740000 | 0.5076 | | 0.5396 | 1.81 | 760000 | 0.5051 | | 0.5363 | 1.86 | 780000 | 0.5026 | | 0.5356 | 1.91 | 800000 | 0.4998 | | 0.5303 | 1.96 | 820000 | 0.4982 | | 0.5583 | 2.0 | 840000 | 0.5195 | | 0.5565 | 2.05 | 860000 | 0.5180 | | 0.5535 | 2.1 | 880000 | 0.5158 | | 0.5497 | 2.15 | 900000 | 0.5133 | | 0.5511 | 2.19 | 920000 | 0.5110 | | 0.5439 | 2.24 | 940000 | 0.5085 | | 0.5413 | 2.29 | 960000 | 0.5060 | | 0.5376 | 2.34 | 980000 | 0.5023 | | 0.5333 | 2.39 | 1000000 | 0.5004 | | 0.5322 | 2.43 | 1020000 | 0.4973 | | 0.5312 | 2.48 | 1040000 | 0.4941 | | 0.5281 | 2.53 | 1060000 | 0.4921 | | 0.5267 | 2.58 | 1080000 | 0.4902 | | 0.5257 | 2.62 | 1100000 | 0.4871 | | 0.5174 | 2.67 | 1120000 | 0.4849 | | 0.5183 | 2.72 | 1140000 | 0.4825 | | 0.5181 | 2.77 | 1160000 | 0.4807 | | 0.5116 | 2.81 | 1180000 | 0.4784 | | 0.5092 | 2.86 | 1200000 | 0.4769 | | 0.5109 | 2.91 | 1220000 | 0.4757 | | 0.5102 | 2.96 | 1240000 | 0.4739 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.13.1+cu117 - Datasets 2.8.0 - Tokenizers 0.13.2

XaneWayner/ppo-LunarLander-v2

XaneWayner

2023-03-22T13:18:24Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T13:17:52Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 275.38 +/- 17.28 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Yuvarraj/Streaming_ASR_PSG

Yuvarraj

2023-03-22T13:14:10Z

3

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "speech", "en", "dataset:librispeech_asr", "arxiv:2006.11477", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-03-22T13:10:02Z

--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # Wav2Vec2-Large-960h [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) ``` ## Evaluation This code snippet shows how to evaluate **facebook/wav2vec2-large-960h** on LibriSpeech's "clean" and "other" test data. ```python from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import soundfile as sf import torch from jiwer import wer librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda") processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h") def map_to_pred(batch): input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values with torch.no_grad(): logits = model(input_values.to("cuda")).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"])) ``` *Result (WER)*: | "clean" | "other" | |---|---| | 2.8 | 6.3 |

McCheng/a2c-PandaReachDense-v2

McCheng

2023-03-22T13:13:48Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T07:05:52Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.96 +/- 0.34 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

Yureeh/Reinforce-PixelCopter

Yureeh

2023-03-22T13:08:25Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-03-21T22:43:22Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 27.30 +/- 24.78 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Ellipsoul/Reinforce-Pixelcopter-PLE-v0

Ellipsoul

2023-03-22T13:05:14Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T13:04:52Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 22.30 +/- 16.59 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

gcapde/gcapde-finetuned-amazon_reviews_multi

gcapde

2023-03-22T12:53:49Z

5

0

transformers

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

text-classification

2023-03-22T12:48:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy model-index: - name: gcapde-finetuned-amazon_reviews_multi results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: es split: validation args: es metrics: - name: Accuracy type: accuracy value: 0.908 ---  # gcapde-finetuned-amazon_reviews_multi This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.2896 - Accuracy: 0.908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2333 | 1.0 | 63 | 0.2837 | 0.9002 | | 0.2117 | 2.0 | 126 | 0.2896 | 0.908 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

lucadiliello/opt-30b-deepspeed-inference-fp16-shard-4

lucadiliello

2023-03-22T12:49:04Z

4

0

transformers

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

text-generation

2023-03-21T11:57:42Z

This is a copy of the original [OPT weights](https://huggingface.co/facebook/opt-30b) that is more efficient to use with the [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). In this repo the original tensors are split into 4 shards to target 4 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism. For specific details about the OPT model itself, please see the [original OPT model card](https://huggingface.co/facebook/opt-30b). For examples on using this repo please see the following: * https://github.com/huggingface/transformers-bloom-inference * https://github.com/microsoft/DeepSpeed-MII

lucadiliello/opt-30b-deepspeed-inference-fp16-shard-2

lucadiliello

2023-03-22T12:48:50Z

5

0

transformers

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

text-generation

2023-03-21T11:57:48Z

This is a copy of the original [OPT weights](https://huggingface.co/facebook/opt-30b) that is more efficient to use with the [DeepSpeed-MII](https://github.com/microsoft/deepspeed-mii) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). In this repo the original tensors are split into 2 shards to target 2 GPUs, this allows the user to run the model with DeepSpeed-inference Tensor Parallelism. For specific details about the OPT model itself, please see the [original OPT model card](https://huggingface.co/facebook/opt-30b). For examples on using this repo please see the following: * https://github.com/huggingface/transformers-bloom-inference * https://github.com/microsoft/DeepSpeed-MII

stanlochten/t5-KGQgen

stanlochten

2023-03-22T12:41:28Z

7

5

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "knowledge_graphs", "question_generation", "en", "dataset:web_questions", "license:openrail", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-02T23:29:05Z

--- license: openrail datasets: - web_questions language: - en metrics: - bleu - bertscore library_name: transformers tags: - knowledge_graphs - question_generation --- T5-base model fine-tuned for question generation from knowledge graphs. Can be used to generate questions from linearized knowledge graphs, meaning graphs in the form of its all its triples listed in the following format: `<A> answer node(s) <H> head <R> relation <T> tail <H> head <R> relation <T> tail ... etc ...`, where `answer node(s)` refers to the node(s) which should contain the answer to the generated question. To load the model: ``` from transformers import T5ForConditionalGeneration, T5TokenizerFast model = T5ForConditionalGeneration.from_pretrained('stanlochten/t5-KGQgen') tokenizer = T5TokenizerFast.from_pretrained('t5-base', extra_ids=0, additional_special_tokens = ['<A>', '<H>', '<R>', '<T>']) ``` To generate questions from your graphs, where `graphs` is a list of strings for each graph: ``` print('Tokenizing...') inputs = tokenizer(graphs, return_tensors="pt", padding=True, truncation=True) print('Predicting...') y_hats = model.generate(inputs.input_ids) print('Decoding...') preds = tokenizer.batch_decode(y_hats, skip_special_tokens=True, clean_up_tokenization_spaces=True) ``` Good luck! [Associated research report](https://dspace.uba.uva.nl/server/api/core/bitstreams/fee95174-b7d4-4cd8-8545-f7ec8ab29e2d/content)

arrandi/PyramidTraining

arrandi

2023-03-22T12:40:59Z

8

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-03-22T12:38:59Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: arrandi/PyramidTraining 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929

pfunk

2023-03-22T12:35:57Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T12:35:48Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.32 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQPN_freq** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x3.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x3]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x3 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed929/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x3 --policy-network-frequency 3000 --seed 929 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x3', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 3000, 'policy_tau': 1.0, 'save_model': True, 'seed': 929, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

qanastek/biomedical-specialities-classifier-french

qanastek

2023-03-22T12:33:55Z

5

0

transformers

[ "transformers", "pytorch", "camembert", "text-classification", "medical", "chemistry", "biology", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-22T11:13:24Z

--- license: apache-2.0 language: - fr metrics: - accuracy pipeline_tag: text-classification tags: - medical - chemistry - biology ---

stelladk/PPO-CleanRL-LunarLander

stelladk

2023-03-22T12:32:11Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T12:06:46Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -207.10 +/- 95.51 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'default_ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'stelladk/PPO-CleanRL-LunarLander' 'batch_size': 512 'minibatch_size': 128} ```

dvesely/dqn-SpaceInvadersNoFrameskip-v4

dvesely

2023-03-22T12:19:37Z

2

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T12:18:53Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 486.00 +/- 133.51 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dvesely -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dvesely -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dvesely ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.2), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0003), ('learning_starts', 50000), ('n_timesteps', 1500000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 500), ('train_freq', 4), ('normalize', False)]) ```

marcatanante1/my_mind_model

marcatanante1

2023-03-22T12:15:53Z

8

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-03-22T09:45:53Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_mind_model results: [] ---  # my_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6450 - Accuracy: 0.0929 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.73 | 2 | 2.6408 | 0.0752 | | No log | 1.82 | 5 | 2.6441 | 0.0619 | | No log | 2.91 | 8 | 2.6435 | 0.0973 | | 2.6293 | 4.0 | 11 | 2.6446 | 0.0885 | | 2.6293 | 4.73 | 13 | 2.6449 | 0.0841 | | 2.6293 | 5.82 | 16 | 2.6463 | 0.0841 | | 2.6293 | 6.91 | 19 | 2.6452 | 0.0885 | | 2.6177 | 7.27 | 20 | 2.6450 | 0.0929 | ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888

pfunk

2023-03-22T12:15:06Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T12:14:57Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.11 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQPN_freq** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x3.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x3]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x3 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x3-seed888/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x3 --policy-network-frequency 3000 --seed 888 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x3', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 3000, 'policy_tau': 1.0, 'save_model': True, 'seed': 888, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

kgmann/ai-image-det-resnet18

kgmann

2023-03-22T11:45:38Z

5

2

timm

[ "timm", "pytorch", "image-classification", "dataset:competitions/aiornot", "region:us" ]

image-classification

2023-03-22T11:34:13Z

--- tags: - image-classification - timm library_tag: timm datasets: - competitions/aiornot metrics: - accuracy --- # Model card for kgmann/ai-image-det-resnet18 This is a small **resnet18** pretrained model, fine-tuned for 5 epochs on 80% of the [AI or Not dataset](https://huggingface.co/datasets/competitions/aiornot) and evaluated on the remaining 20% of the training dataset. It has an **accuracy of 99%** on the validation dataset.

arrandi/ppo-SnowballTarget

arrandi

2023-03-22T11:30:23Z

10

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-03-22T11:30:17Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget library_name: ml-agents --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget 2. Step 1: Write your model_id: arrandi/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

alramalho/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-extended-labels

alramalho

2023-03-22T11:25:51Z

4

0

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "zero-shot-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

zero-shot-classification

2023-03-21T19:10:41Z

--- pipeline_tag: zero-shot-classification ---

blinoff/roberta-base-russian-v0

blinoff

2023-03-22T11:23:40Z

339

8

transformers

[ "transformers", "pytorch", "jax", "safetensors", "roberta", "fill-mask", "ru", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: ru widget: - text: "Мозг — это машина вывода, которая пытается <mask> ошибку в прогнозе." example_title: "brain_example" - text: "Никогда не спорьте с идиотами, <mask> опуститесь до их уровня, где они вас задавят своим опытом." example_title: "idiot_example" --- # RoBERTa-like language model trained on part of part of TAIGA corpus ## Training Details - about 60k steps ![]() ## Example pipeline ```python from transformers import pipeline from transformers import RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('blinoff/roberta-base-russian-v0', max_len=512) fill_mask = pipeline( "fill-mask", model="blinoff/roberta-base-russian-v0", tokenizer=tokenizer ) fill_mask("Мозг — это машина <mask>, которая пытается снизить ошибку в прогнозе.") # { # 'sequence': '<s>Мозг — это машина города, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.012859329581260681, # 'token': 2144, # 'token_str': 'ĠÐ³Ð¾ÑĢÐ¾Ð´Ð°' # }, # { # 'sequence': '<s>Мозг — это машина человека, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.01185101643204689, # 'token': 1470, # 'token_str': 'ĠÑĩÐµÐ»Ð¾Ð²ÐµÐºÐ°' # }, # { # 'sequence': '<s>Мозг — это машина дома, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.009940559044480324, # 'token': 1411, # 'token_str': 'ĠÐ´Ð¾Ð¼Ð°' # }, # { # 'sequence': '<s>Мозг — это машина женщина, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.007794599514454603, # 'token': 2707, # 'token_str': 'ĠÐ¶ÐµÐ½ÑīÐ¸Ð½Ð°' # }, # { # 'sequence': '<s>Мозг — это машина женщины, которая пытается снизить ошибку в прогнозе.</s>', # 'score': 0.007725382689386606, # 'token': 3546, # 'token_str': 'ĠÐ¶ÐµÐ½ÑīÐ¸Ð½Ñĭ' # } ```

arrandi/poca-SoccerTwos

arrandi

2023-03-22T11:08:56Z

7

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]

reinforcement-learning

2023-03-22T11:08:47Z

--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos library_name: ml-agents --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos 2. Step 1: Write your model_id: arrandi/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

antonioricciardi/ppo-Huggy

antonioricciardi

2023-03-22T10:56:56Z

3

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-03-22T10:56:50Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: antonioricciardi/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

faviasono/bert-base-banking77-pt2

faviasono

2023-03-22T10:52:02Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:banking77", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-03-22T09:57:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - f1 model-index: - name: bert-base-banking77-pt2 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 config: default split: test args: default metrics: - name: F1 type: f1 value: 0.9264340389849328 ---  # bert-base-banking77-pt2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.3012 - F1: 0.9264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0535 | 1.0 | 626 | 0.7552 | 0.8650 | | 0.3807 | 2.0 | 1252 | 0.3574 | 0.9224 | | 0.1794 | 3.0 | 1878 | 0.3012 | 0.9264 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0+cu117 - Datasets 2.9.0 - Tokenizers 0.13.2

Chattiori/PetalMix

Chattiori

2023-03-22T10:50:42Z

0

1

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T10:48:56Z

--- license: creativeml-openrail-m ---

vevlins/autotrain-classify-42751109216

vevlins

2023-03-22T10:43:30Z

16

0

transformers

[ "transformers", "pytorch", "beit", "image-classification", "autotrain", "vision", "dataset:vevlins/autotrain-data-classify", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-03-22T10:41:19Z

--- tags: - autotrain - vision - image-classification datasets: - vevlins/autotrain-data-classify widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.852147336270292 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 42751109216 - CO2 Emissions (in grams): 0.8521 ## Validation Metrics - Loss: 0.010 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000

satyaalmasian/temporal_tagger_BERT_tokenclassifier

satyaalmasian

2023-03-22T10:24:27Z

23

4

transformers

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

token-classification

2022-03-02T23:29:05Z

# BERT based temporal tagged Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger). # Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. We use BERT for token classification to tag the tokens in text with classes: ``` O -- outside of a tag I-TIME -- inside tag of time B-TIME -- beginning tag of time I-DATE -- inside tag of date B-DATE -- beginning tag of date I-DURATION -- inside tag of duration B-DURATION -- beginning tag of duration I-SET -- inside tag of the set B-SET -- beginning tag of the set ``` # Intended uses & limitations This model is best used accompanied with code from the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). Especially for inference, the direct output might be noisy and hard to decipher, in the repository we provide alignment functions and voting strategies for the final output. # How to use you can load the model as follows: ``` tokenizer = AutoTokenizer.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier", use_fast=False) model = BertForTokenClassification.from_pretrained("satyaalmasian/temporal_tagger_BERT_tokenclassifier") ``` for inference use: ``` processed_text = tokenizer(input_text, return_tensors="pt") result = model(**processed_text) classification= result[0] ``` for an example with post-processing, refer to the [repository](https://github.com/satya77/Transformer_Temporal_Tagger). We provide a function `merge_tokens` to decipher the output. to further fine-tune, use the `Trainer` from hugginface. An example of a similar fine-tuning can be found [here](https://github.com/satya77/Transformer_Temporal_Tagger/blob/master/run_token_classifier.py). #Training data We use 3 data sources: [Tempeval-3](https://www.cs.york.ac.uk/semeval-2013/task1/index.php%3Fid=data.html), Wikiwars, Tweets datasets. For the correct data versions please refer to our [repository](https://github.com/satya77/Transformer_Temporal_Tagger). #Training procedure The model is trained from publicly available checkpoints on huggingface (`bert-base-uncased`), with a batch size of 34. We use a learning rate of 5e-05 with an Adam optimizer and linear weight decay. We fine-tune with 5 different random seeds, this version of the model is the only seed=4. For training, we use 2 NVIDIA A100 GPUs with 40GB of memory.

agcagc/ppo-LunarLander-v2-clear

agcagc

2023-03-22T10:04:14Z

0

null

[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T10:04:03Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -145.06 +/- 94.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'agcagc/ppo-LunarLander-v2-clear' 'batch_size': 512 'minibatch_size': 128} ```

whit/mthwmdl

whit

2023-03-22T10:03:51Z

8

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-03-22T09:34:04Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mthwmdl Dreambooth model trained by whit with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

juliusco/GPT-2-finetuned-papers

juliusco

2023-03-22T10:01:37Z

4

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-03-22T08:20:52Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: juliusco/GPT-2-finetuned-papers results: [] ---  # juliusco/GPT-2-finetuned-papers This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.4240 - Validation Loss: 2.2215 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.4240 | 2.2215 | 0 | ### Framework versions - Transformers 4.27.2 - TensorFlow 2.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

mrm8488/bloom-560m-finetuned-samsum

mrm8488

2023-03-22T10:00:28Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bloom", "text-generation", "generated_from_trainer", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-09-27T15:31:39Z

--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer model-index: - name: bloom-560m-finetuned-samsum results: [] ---  # bloom-560m-finetuned-samsum This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9178 ## 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: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7663 | 0.63 | 200 | 2.6934 | | 2.3769 | 1.26 | 400 | 2.6274 | | 2.2776 | 1.89 | 600 | 2.5818 | | 1.873 | 2.52 | 800 | 2.7177 | | 1.6715 | 3.15 | 1000 | 2.9178 | | 1.4515 | 3.78 | 1200 | 2.8924 | | 1.0522 | 4.42 | 1400 | 3.3753 | | 1.0237 | 5.05 | 1600 | 3.8098 | | 0.7416 | 5.68 | 1800 | 3.9139 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1

marcovarrone/scvi-dlpfc-full-visium

marcovarrone

2023-03-22T09:58:46Z

0

1

scvi-tools

[ "scvi-tools", "biology", "genomics", "single-cell", "model_cls_name:SCVI", "scvi_version:0.20.0", "anndata_version:0.8.0", "modality:rna", "annotated:False", "license:cc-by-4.0", "region:us" ]

null

2023-03-22T09:51:57Z

--- license: cc-by-4.0 library_name: scvi-tools tags: - biology - genomics - single-cell - model_cls_name:SCVI - scvi_version:0.20.0 - anndata_version:0.8.0 - modality:rna - annotated:False --- # Description scVI model trained on the full DLPFC Visium data (including the pilot samples). # Model properties Many model properties are in the model tags. Some more are listed below. **model_init_params**: ```json { "n_hidden": 128, "n_latent": 5, "n_layers": 1, "dropout_rate": 0.1, "dispersion": "gene", "gene_likelihood": "zinb", "latent_distribution": "normal" } ``` **model_setup_anndata_args**: ```json { "layer": "counts", "batch_key": "patient", "labels_key": null, "size_factor_key": null, "categorical_covariate_keys": [ "sample", "study" ], "continuous_covariate_keys": null } ``` **model_summary_stats**: | Summary Stat Key | Value | |--------------------------|--------| | n_batch | 13 | | n_cells | 166443 | | n_extra_categorical_covs | 2 | | n_extra_continuous_covs | 0 | | n_labels | 1 | | n_vars | 5000 | **model_data_registry**: | Registry Key | scvi-tools Location | |------------------------|--------------------------------------------| | X | adata.layers['counts'] | | batch | adata.obs['_scvi_batch'] | | extra_categorical_covs | adata.obsm['_scvi_extra_categorical_covs'] | | labels | adata.obs['_scvi_labels'] | **model_parent_module**: scvi.model **data_is_minified**: False # Training data This is an optional link to where the training data is stored if it is too large to host on the huggingface Model hub.  Training data url: N/A # Training code This is an optional link to the code used to train the model. Training code url: N/A # References 1. Maynard, Kristen R., et al. "Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex." Nature neuroscience 24.3 (2021): 425-436. 2. Huuki-Myers, Louise A., et al. "Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex." BioRxiv (2023): 2023-02.

nahiavl/huggy_20_03

nahiavl

2023-03-22T09:55:00Z

10

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2023-03-20T14:17:46Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser:**. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: nahiavl/huggy_20_03 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

gogo432754/finetuning-sentiment-model-3000-samples

gogo432754

2023-03-22T09:52:33Z

5

0

transformers

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

text-classification

2023-03-21T10:25:24Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8712871287128714 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2955 - Accuracy: 0.87 - F1: 0.8713 ## 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 4.27.1 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2

ybelkada/clap-model-card

ybelkada

2023-03-22T09:50:52Z

0

1

null

[ "arxiv:2211.06687", "license:apache-2.0", "region:us" ]

null

2023-03-14T16:13:06Z

--- license: apache-2.0 --- # Model card for CLAP Model card for CLAP: Contrastive Language-Audio Pretraining ![clap_image](https://s3.amazonaws.com/moonup/production/uploads/1678811100805-62441d1d9fdefb55a0b7d12c.png) # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Citation](#citation) # TL;DR The abstract of the paper states that: > Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. # Usage You can use this model for zero shot audio classification or extracting audio and/or textual features. # Uses ## Perform zero-shot audio classification ### Using `pipeline` ```python from datasets import load_dataset from transformers import pipeline dataset = load_dataset("ashraq/esc50") audio = dataset["train"]["audio"][-1]["array"] audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused") output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]) print(output) >>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}] ``` ## Run the model: You can also get the audio and text embeddings using `ClapModel` ### Run the model on CPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused") processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt") audio_embed = model.get_audio_features(**inputs) ``` ### Run the model on GPU: ```python from datasets import load_dataset from transformers import ClapModel, ClapProcessor librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = librispeech_dummy[0] model = ClapModel.from_pretrained("laion/clap-htsat-unfused").to(0) processor = ClapProcessor.from_pretrained("laion/clap-htsat-unfused") inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0) audio_embed = model.get_audio_features(**inputs) ``` # Citation If you are using this model for your work, please consider citing the original paper: ``` @misc{https://doi.org/10.48550/arxiv.2211.06687, doi = {10.48550/ARXIV.2211.06687}, url = {https://arxiv.org/abs/2211.06687}, author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo}, keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering}, title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

gonced8/godel-multiwoz

gonced8

2023-03-22T09:43:29Z

12

4

transformers

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "en", "dataset:multi_woz_v22", "license:gpl-3.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-01-24T12:30:06Z

--- license: gpl-3.0 datasets: - multi_woz_v22 language: - en metrics: - bleu - rouge --- Pretrained model: [GODEL-v1_1-base-seq2seq](https://huggingface.co/microsoft/GODEL-v1_1-base-seq2seq/) Fine-tuning dataset: [MultiWOZ 2.2](https://github.com/budzianowski/multiwoz/tree/master/data/MultiWOZ_2.2) # How to use: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained("gonced8/godel-multiwoz") model = AutoModelForSeq2SeqLM.from_pretrained("gonced8/godel-multiwoz") # Encoder input context = [ "USER: I need train reservations from norwich to cambridge", "SYSTEM: I have 133 trains matching your request. Is there a specific day and time you would like to travel?", "USER: I'd like to leave on Monday and arrive by 18:00.", ] input_text = " EOS ".join(context[-5:]) + " => " model_inputs = tokenizer( input_text, max_length=512, truncation=True, return_tensors="pt" )["input_ids"] # Decoder input answer_start = "SYSTEM: " decoder_input_ids = tokenizer( "<pad>" + answer_start, max_length=256, truncation=True, add_special_tokens=False, return_tensors="pt", )["input_ids"] # Generate output = model.generate( model_inputs, decoder_input_ids=decoder_input_ids, max_length=256 ) output = tokenizer.decode( output[0], clean_up_tokenization_spaces=True, skip_special_tokens=True ) print(output) # SYSTEM: TR4634 arrives at 17:35. Would you like me to book that for you? ```

micromind/MNIST

micromind

2023-03-22T09:39:31Z

0

null

[ "image-classification", "en", "dataset:mnist", "license:mit", "region:us" ]

image-classification

2023-02-22T09:47:46Z

--- license: mit datasets: - mnist language: - en pipeline_tag: image-classification --- # micromind checkpoints for MNIST This repository contains checkpoints for the MNIST dataset for the following networks: | Model | Top 1 Accuracy | Top 5 Accuracy | | ------------------ |---------------- | -------------- | | `PhiNet(alpha=0.5, beta=1, t_zero=6, num_layers=4, resolution=28)` | 98.96% | 100% | | `PhiNet(alpha=0.75, beta=1, t_zero=6, num_layers=5, resolution=28)` | 99.03% | 99.98% | | `PhiNet(alpha=0.35, beta=1, t_zero=6, num_layers=7, resolution=28)` | 98.72% | 99.99% | | `PhiNet(alpha=0.25, beta=1, t_zero=6, num_layers=7, resolution=28)` | 98.84% | 99.99% | | `PhiNet(alpha=0.25, beta=1, t_zero=5, num_layers=7, resolution=28)` | 98.76% | 99.97% | To download and use this repo: ``` from micromind import PhiNet model = PhiNet.from_pretrained("MNIST", alpha=0.5, beta=1.0, t_zero=6, num_layers=4, num_classes=10, resolution=28) ``` ## Authors - [@fpaissan](https://www.github.com/fpaissan) - [@matteobeltrami](https://www.github.com/matteobeltrami) --- license: mit ---

bhadresh-savani/electra-base-squad2

bhadresh-savani

2023-03-22T09:36:46Z

117

0

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "electra", "question-answering", "dataset:squad_v2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2022-04-13T14:25:23Z

--- datasets: - squad_v2 license: cc-by-4.0 --- # electra-base for QA ## Overview **Language model:** electra-base **Language:** English **Downstream-task:** Extractive QA **Training data:** SQuAD 2.0 **Eval data:** SQuAD 2.0 **Code:** See [example](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) in [FARM](https://github.com/deepset-ai/FARM/blob/master/examples/question_answering.py) **Infrastructure**: 1x Tesla v100 ## Hyperparameters ``` seed=42 batch_size = 32 n_epochs = 5 base_LM_model = "google/electra-base-discriminator" max_seq_len = 384 learning_rate = 1e-4 lr_schedule = LinearWarmup warmup_proportion = 0.1 doc_stride=128 max_query_length=64 ``` ## Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 77.30144024256717, "f1": 81.35438272008543, "total": 11873, "HasAns_exact": 74.34210526315789, "HasAns_f1": 82.45961302894314, "HasAns_total": 5928, "NoAns_exact": 80.25231286795626, "NoAns_f1": 80.25231286795626, "NoAns_total": 5945 ``` ## Usage ### In Transformers ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline model_name = "deepset/electra-base-squad2" # a) Get predictions nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) QA_input = { 'question': 'Why is model conversion important?', 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' } res = nlp(QA_input) # b) Load model & tokenizer model = AutoModelForQuestionAnswering.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` ### In FARM ```python from farm.modeling.adaptive_model import AdaptiveModel from farm.modeling.tokenization import Tokenizer from farm.infer import Inferencer model_name = "deepset/electra-base-squad2" # a) Get predictions nlp = Inferencer.load(model_name, task_type="question_answering") QA_input = [{"questions": ["Why is model conversion important?"], "text": "The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks."}] res = nlp.inference_from_dicts(dicts=QA_input) # b) Load model & tokenizer model = AdaptiveModel.convert_from_transformers(model_name, device="cpu", task_type="question_answering") tokenizer = Tokenizer.load(model_name) ``` ### In haystack For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/electra-base-squad2") # or reader = TransformersReader(model="deepset/electra-base-squad2",tokenizer="deepset/electra-base-squad2") ``` ## Authors Vaishali Pal `vaishali.pal [at] deepset.ai` Branden Chan: `branden.chan [at] deepset.ai` Timo Möller: `timo.moeller [at] deepset.ai` Malte Pietsch: `malte.pietsch [at] deepset.ai` Tanay Soni: `tanay.soni [at] deepset.ai` Note: Borrowed this model from Haystack model repo for adding tensorflow model.

Mor1998/distilbert-base-uncased-distilled-dtkd-clinc

Mor1998

2023-03-22T08:54:02Z

4

0

transformers

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

text-classification

2023-03-22T08:05:52Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-distilled-dtkd-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9319354838709677 ---  # distilbert-base-uncased-distilled-dtkd-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.0655 - Accuracy: 0.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6996 | 1.0 | 318 | 0.4150 | 0.5790 | | 0.3134 | 2.0 | 636 | 0.2040 | 0.8381 | | 0.1843 | 3.0 | 954 | 0.1330 | 0.8952 | | 0.1322 | 4.0 | 1272 | 0.1032 | 0.9119 | | 0.1053 | 5.0 | 1590 | 0.0858 | 0.9213 | | 0.0908 | 6.0 | 1908 | 0.0771 | 0.9258 | | 0.0813 | 7.0 | 2226 | 0.0710 | 0.9287 | | 0.0754 | 8.0 | 2544 | 0.0681 | 0.9310 | | 0.0717 | 9.0 | 2862 | 0.0660 | 0.9310 | | 0.0701 | 10.0 | 3180 | 0.0655 | 0.9319 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

loveplay1983/distilbert-base-uncased-finetuned-emotion

loveplay1983

2023-03-22T08:51:15Z

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

2023-03-22T02:20:28Z

--- 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 config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.941 - name: F1 type: f1 value: 0.9410654356868428 ---  # 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: 0.1570 - Accuracy: 0.941 - F1: 0.9411 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4447 | 1.0 | 1600 | 0.1987 | 0.9295 | 0.9290 | | 0.155 | 2.0 | 3200 | 0.1570 | 0.941 | 0.9411 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.11.0 - Datasets 2.10.1 - Tokenizers 0.13.2

asuzuki/PPO-LunarLander-v2

asuzuki

2023-03-22T08:49:52Z

4

0

transformers

[ "transformers", "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-01-06T08:49:20Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -117.50 +/- 52.66 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'asuzuki/ppo-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```

bhadresh-savani/roberta-base-emotion

bhadresh-savani

2023-03-22T08:48:07Z

779

5

transformers

[ "transformers", "pytorch", "tf", "jax", "safetensors", "roberta", "text-classification", "emotion", "en", "dataset:emotion", "arxiv:1907.11692", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- language: - en license: apache-2.0 tags: - text-classification - emotion - pytorch datasets: - emotion metrics: - Accuracy, F1 Score thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4 model-index: - name: bhadresh-savani/roberta-base-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.931 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjg5OTI4ZTlkY2VmZjYzNGEzZGQ3ZjczYzY5YjJmMGVmZDQ4ZWNiYTAyZTJiZjlmMTU2MjE1NTllMWFhYzU0MiIsInZlcnNpb24iOjF9.dc44cEsbu900M2s64GyVIWKPagBzwI-dPlfvh0NGyJFMGKOcypke9P2ary9fBZITrH3UF6lza3sCh7vWYZFHBQ - type: precision value: 0.9168321948556312 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2EzYTcxNTExNGU1MmFiZjE3NGE5MDIyMDU2M2U3OGExOTdjZDE5YWU2NDhmOTJlYWMzY2NkN2U5MmRmZTE0MiIsInZlcnNpb24iOjF9.4U7vJ3ALdUUxySMhVeb4Qa1tSp3wphSIZkRYNMujz-KrOZW8kkcmCde3ioStBg3Qqyf1powYd88uk1R7DuWRBA - type: precision value: 0.931 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjhmZGRlYWE5ZTAzMmJiMzlmMWZiM2VlYjdiNzI0NjVmN2M2YzcxM2EzYTg0OTFiZTE1MjVmNzE5NGEzYTg2ZCIsInZlcnNpb24iOjF9.8eCHAK0rlZWnhBNQdh9kcuAeItmDUAgK3KkZ7eC-GyYhi4HT5dZiS6btcC5EjkYVOS4czcjzqxfVz4PuZgtLDQ - type: precision value: 0.9357445689014415 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDhhZTdkNzYzMjhjZjc4MTAxNWZiYjgzMjhhNjRiZWRmYjc5YTA0NTQ1MzllMTYxMTVkMDk4OTE0ZGEyMTNhMiIsInZlcnNpb24iOjF9.YIZfj2Eo1nMX2GVSfqJy-Cp7VBubfUh2LuOnU60sG5Lci8FdlNbAanS1IzAyxU3U29lqiTasxfS_yrwAj5cmBQ - type: recall value: 0.8743657671177089 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2Y2YTcyNzMwYzZiMmM1Yzc4YWZhNDM3ZDQyMjI1NWZhMjQyNmU5NTA0YmE2ZDBiZmY1MmUyZWRlMjRhMjFmYSIsInZlcnNpb24iOjF9.XKlFy_Cx4T4l7Otd8aAwWcI-fJ_dJ6V1Kp3uZm6OWjwCb1Do6mSdPFfwiMeBZZyfEIsNBnguegssZvHsOfTSAQ - type: recall value: 0.931 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzgzN2JkNzAzZDRjNjJmZjNkY2RmYzVkMWEzYTMzZDU4NzJlYzBmOWE4MTU0MGU0MTJhM2JjZDdjODhlZDExOCIsInZlcnNpb24iOjF9.9tSVB4yNBdFXpH3equwo1ZaEnVUktO6lm93UEJ-luKhxo6wgS54OLjgDq7IpJYwa3lvYyjy-sxzQEe9ri31WAg - type: recall value: 0.931 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGVhZTIyMmVmOTU1YWNjMmZiZjNmOTNlNzlhZTk3NjhlZmMwZGFkZWQxZTlhZWUwZGQyN2JhOWQyNWQ3MTVhOCIsInZlcnNpb24iOjF9.2odv2fK7zH0_S_7wC3obONzjxOipDdjWvddhnGdMnrIN6CiZwLp7XgizpqcWbwAQ_9YJwjC-6wXpbq2jTvN0Bw - type: f1 value: 0.8821236522209227 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDI0YTUxOTA2M2ZjNGM1OTJlZDAzZTAxNTg4YjY3OWNmMjNmMTk0YWRjZTE2Y2ZmYWI1ZmU3ZmJmNzNjMjBlOCIsInZlcnNpb24iOjF9.P5-TbuEUrCtX9H7F-tKn8LI1RBPhoJwjJm_l853WTSzdLioThAtIK5HBG0xgXT2uB0Q8v94qH2b8cz1j_WonDg - type: f1 value: 0.931 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjNmNDgyMmFjODYwNjcwOTJiOGM2N2YwYjUyMDk5Yjk2Y2I3NmFmZGFhYjU0NGM2OGUwZmRjNjcxYTU3YzgzNSIsInZlcnNpb24iOjF9.2ZoRJwQWVIcl_Ykxce1MnZ3mSxBGxGeNYFPxt9mivo9yTi3gUE7ua6JRpVEOnOUbevlWxVkUUNnmOPFqBN1sCQ - type: f1 value: 0.9300782840205046 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGE1OTcxNmNmMjQ3ZDAzYzk0N2Q1MGFjM2VhNWMyYmRjY2E3ZThjODExOTNlNWMxYzdlMWM2MDBiMTZhY2M2OSIsInZlcnNpb24iOjF9.r63SEArCiFB5m0ccV2q_t5uSOtjVnWdz4PfvCYUchm0JlrRC9YAm5oWKeO419wdyFY4rZFe014yv7sRcV-CgBQ - type: loss value: 0.15155883133411407 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2M4MmVlNjAzZjhiMWJlNWQxMDg5ZTRiYjFlZGYyMGMyYzU4M2IwY2E1M2E2MzA5NmU5ZjgwZTZmMDI5YjgzMyIsInZlcnNpb24iOjF9.kjgFJohkTxLKtzHJDlBvd6qolGQDSZLbrDE7C07xNGmarhTLc_A3MmLeC4MmQGOl1DxfnHflImIkdqPylyylDA --- # robert-base-emotion ## Model description: [roberta](https://arxiv.org/abs/1907.11692) is Bert with better hyperparameter choices so they said it's Robustly optimized Bert during pretraining. [roberta-base](https://huggingface.co/roberta-base) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters ``` learning rate 2e-5, batch size 64, num_train_epochs=8, ``` ## Model Performance Comparision on Emotion Dataset from Twitter: | Model | Accuracy | F1 Score | Test Sample per Second | | --- | --- | --- | --- | | [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 | | [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 | | [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 | | [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 | ## How to Use the model: ```python from transformers import pipeline classifier = pipeline("text-classification",model='bhadresh-savani/roberta-base-emotion', return_all_scores=True) prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", ) print(prediction) """ Output: [[ {'label': 'sadness', 'score': 0.002281982684507966}, {'label': 'joy', 'score': 0.9726489186286926}, {'label': 'love', 'score': 0.021365027874708176}, {'label': 'anger', 'score': 0.0026395076420158148}, {'label': 'fear', 'score': 0.0007162453257478774}, {'label': 'surprise', 'score': 0.0003483477921690792} ]] """ ``` ## Dataset: [Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion). ## Training procedure [Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb) follow the above notebook by changing the model name to roberta ## Eval results ```json { 'test_accuracy': 0.9395, 'test_f1': 0.9397328860104454, 'test_loss': 0.14367154240608215, 'test_runtime': 10.2229, 'test_samples_per_second': 195.639, 'test_steps_per_second': 3.13 } ``` ## Reference: * [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)

bhadresh-savani/bertweet-base-finetuned-emotion

bhadresh-savani

2023-03-22T08:42:15Z

14

0

transformers

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

text-classification

2022-07-11T15:57:26Z

--- tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: bertweet-base-finetuned-emotion results: - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion args: default metrics: - type: accuracy value: 0.929 name: Accuracy - type: f1 value: 0.9295613935787139 name: F1 - task: type: text-classification name: Text Classification dataset: name: emotion type: emotion config: default split: test metrics: - type: accuracy value: 0.925 name: Accuracy verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZThkYWEwYTdjY2IwMmE4NmM2Mzc3ZTVkNTNmNWYwNGUxYTM5ZDA5ODEwMGQ1ZGU0ZmJmY2U1ZDhjYWRlZjU2NSIsInZlcnNpb24iOjF9.QJYOUR_EPrYzbZGBb1N27BSlTQIdvd1hmUfnfPJdTGGrNoQwXBUA4amVsWh1txV_YtO8hcCx-b3pTqzpdy1FAw - type: precision value: 0.8722017563353339 name: Precision Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2Y3ZGM2NDk5ZTQyNTNjZDdmNjk5Y2IwNzkxNmU3MDM0YTljMTJjMzFmMTlkN2ZjN2NhZjNhYTVlMWY5NWFjNCIsInZlcnNpb24iOjF9.cBYScC_c6g1ECi3rj6HiRI3AMuoxg8wp7JKha0UKh1Q2qjzTr5ml8JAByPL0iu-Ix5BO2Bsx0fZNFhUS82LiCg - type: precision value: 0.925 name: Precision Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjVjYjgwYjE2ZWUyM2Y5MzE0ODk4NDA5MGM2ODIxYTgxZDYyMTUxNzcwZWQ2MjZjZGYwODkyNzFkMjAxOTUzYyIsInZlcnNpb24iOjF9.phgA4BJcqp4ZUhecNeuGU8OAf6f_asN9Mf6JfFGd0cPORYltd_N4Wf6EXqu6z1ADqWeeibteEyIUwmmMEbjYBQ - type: precision value: 0.9283646705517916 name: Precision Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZjE4MDUyYjU4YTc4Mzk4YmIwMGIzZWEyYmU5ZDQ0MjRhZDQ4OGMwZjVmZmEyNDM5NzYyZTMzMTJiMmRkZTU4NiIsInZlcnNpb24iOjF9.LbYjoga-JSCzHZAF1fhm1CfuaSSI-ok0yXj3gtd4QTWY1TjzOHoMG3Q6zEGz84l6ASoHsvi9wjS7_EaSQLB4Dw - type: recall value: 0.8982480793145559 name: Recall Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzFiY2JmYWRhYmE1YzA1MmRhODI0MGE1NTRiMWE3YmNjZWQ4OGExMDg3NmUzYmUzYTYyNTdkNjM1Y2M0ODJmMCIsInZlcnNpb24iOjF9.dAq2gloG0O-4z5Ng7RZkFO7e0og3wBQBmIDzic6onwjw83yaHPVfRd1e0j6mNhMUifOwPLEavnYkBYa9DVFqCw - type: recall value: 0.925 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjg5Y2M2NWIwZmI4YjEyMmIzYjNjZmQyMzhlYjg2ZDRmY2U1M2I1NzQzYjRmMzYxYzJkNTI5MDJjMmY5ZDVmNyIsInZlcnNpb24iOjF9.Z5hmQBUsoKAgqTXk47aUDNKf5jJ0mXzY9TAgM9vG8I3pgCT465PEfM-TOKfG_YcPMLd3tkB8AdwDpmVnNj5QCw - type: recall value: 0.925 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODIzMjY1YjgzYzAwNjk2NGI1ZjFhZTg4MGY1Mzg5NDhkM2EzY2JlMWM4MjZmNjg4ZmEyZDJmZTUwNDFkZmNiOCIsInZlcnNpb24iOjF9.S-9p04Lru9WTzm50mM5qGM4oA-TPgNw6uwxKr5AejU1iPKjyTDQvoumBs41T5OKL5zN_NyYXsFsCermSbirLAw - type: f1 value: 0.883488774573809 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMyMjc3NTkxNDNjNTJiNmRmMTA4NzY0MTgyMDc3ZDE4N2RlMTY1YzU4OGQ1YmM1NzY2OGQ4Y2I0MzVhOGU3OSIsInZlcnNpb24iOjF9.D65sLHNZGjp15ra4i5ccYyOX705Xq-hftZjDb6kqE5X-jhzA5VLev6FirhnhyYLBQmA6Q9T1eDYHKkVZG4CcBg - type: f1 value: 0.925 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNTNkMmUwNjYxNGE4M2E2MDA0NTI4ZjA1OTNkMWEwN2MzY2JmMWYxYThiYTZmM2MwZjM5YTIzMGIzMGI4ODJlZSIsInZlcnNpb24iOjF9.cB4WUQN_weyKdMZehH0ECaTcD9Jl1xzmrOzJZz27OJeCPjY0uW8O63HnJZ_LmBF2xqd7HDypT4s8hZBMT-6eDw - type: f1 value: 0.9259820821054494 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTY3NDhjNmMzYzM1MjIyY2FkYjI5YTQ1NTdmODhmMGVlNjc2ZjQ3MWZmZWEyMDQ1OGI1NDllZTBhM2VjYzg2MSIsInZlcnNpb24iOjF9.Akd8PVgc2tyin_TaOZV1bio_b00g3QmlHA-GWV3rMX13B1imDLuPAuP-HWIwgqg-umQUkJzcUQlTqbcQ06v0DQ - type: loss value: 0.18158096075057983 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDZhODkzODQ2ZjYyMWQxYjEzNzRmMmQ0NjM3M2RiNDdlMTcwOGRhYjA0NWEwYTVjMmY0ZWY3NGQ3MzFhMTQ3ZSIsInZlcnNpb24iOjF9.jzv7qMmQuFmrsR3WoRAsCbrRJhNk0sfEcN07lCqhxUwYcO4rblVbBiePQtr0IDN067PbQmV6ES6W2cjHqvuHAA ---  # bertweet-base-finetuned-emotion This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1737 - Accuracy: 0.929 - F1: 0.9296 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 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: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.9469 | 1.0 | 250 | 0.3643 | 0.895 | 0.8921 | | 0.2807 | 2.0 | 500 | 0.2173 | 0.9245 | 0.9252 | | 0.1749 | 3.0 | 750 | 0.1859 | 0.926 | 0.9266 | | 0.1355 | 4.0 | 1000 | 0.1737 | 0.929 | 0.9296 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

bhadresh-savani/electra-base-discriminator-finetuned-conll03-english

bhadresh-savani

2023-03-22T08:41:28Z

108

0

transformers

[ "transformers", "pytorch", "tf", "jax", "electra", "token-classification", "en", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-04-02T11:22:08Z

--- language: - en tags: - token-classification - pytorch license: apache-2.0 datasets: - conll2003 metrics: - Accuracy, F1 Score, Precision, Recall model-index: - name: bhadresh-savani/electra-base-discriminator-finetuned-conll03-english results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics: - name: Accuracy type: accuracy value: 0.9397659001450176 verified: true - name: Precision type: precision value: 0.9492206245667668 verified: true - name: Recall type: recall value: 0.9468813162653806 verified: true - name: F1 type: f1 value: 0.9480495273598721 verified: true - name: loss type: loss value: 0.3468747138977051 verified: true --- # Electra Base Discriminator conll03 English # Results: ``` ***** predict metrics ***** predict_accuracy = 0.9813 predict_f1 = 0.9137 predict_loss = 0.1251 predict_precision = 0.9098 predict_recall = 0.9177 predict_runtime = 0:00:10.11 predict_samples_per_second = 341.368 predict_steps_per_second = 42.696 ```

GanjinZero/biobart-v2-base

GanjinZero

2023-03-22T08:22:33Z

779

4

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "biobart", "biomedical", "en", "arxiv:2204.03905", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-21T15:57:43Z

--- language: - en license: apache-2.0 tags: - bart - biobart - biomedical inference: true widget: - text: "Influenza is a <mask> disease." - type: "text-generation" --- Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) V2 adopts a new biomedical vocab. ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and Sheng Yu}, year={2022}, eprint={2204.03905}, archivePrefix={arXiv} } ```

GanjinZero/biobart-base

GanjinZero

2023-03-22T08:22:29Z

463

5

transformers

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "biobart", "biomedical", "en", "arxiv:2204.03905", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-03-12T07:00:32Z

--- language: - en license: apache-2.0 tags: - bart - biobart - biomedical inference: true widget: - text: "Influenza is a <mask> disease." - type: "text-generation" --- Paper: [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://arxiv.org/pdf/2204.03905.pdf) ``` @misc{BioBART, title={BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model}, author={Hongyi Yuan and Zheng Yuan and Ruyi Gan and Jiaxing Zhang and Yutao Xie and Sheng Yu}, year={2022}, eprint={2204.03905}, archivePrefix={arXiv} } ```

LarryAIDraw/SaekiSayakaBloomInto_v10

LarryAIDraw

2023-03-22T08:12:59Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T08:10:34Z

--- license: creativeml-openrail-m --- https://civitai.com/models/22624/saeki-sayaka-bloom-into-you

LarryAIDraw/dunkerqueAzurLane_v10

LarryAIDraw

2023-03-22T08:12:32Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T08:09:25Z

--- license: creativeml-openrail-m --- https://civitai.com/models/19040/dunkerque-or-azur-lane

LarryAIDraw/liselotteCretiaSeirei_liselottecretia4

LarryAIDraw

2023-03-22T08:12:10Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T08:08:33Z

--- license: creativeml-openrail-m --- https://civitai.com/models/22379/liselotte-cretia-seirei-gensouki

patrickramos/bert-base-japanese-v2-wrime-fine-tune

patrickramos

2023-03-22T08:11:34Z

5,123

6

transformers

[ "transformers", "pytorch", "tf", "safetensors", "bert", "text-classification", "ja", "dataset:wrime", "license:cc-by-sa-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-05-22T09:42:14Z

--- license: cc-by-sa-3.0 language: - ja tag: - emotion-analysis datasets: - wrime widget: - text: "車のタイヤがパンクしてた。。いたずらの可能性が高いんだって。。" --- # WRIME-fine-tuned BERT base Japanese This model is a [Japanese BERT<sub>BASE</sub>](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) fine-tuned on the [WRIME](https://github.com/ids-cv/wrime) dataset. It was trained as part of the paper ["Emotion Analysis of Writers and Readers of Japanese Tweets on Vaccinations"](https://aclanthology.org/2022.wassa-1.10/). Fine-tuning code is available at this [repo](https://github.com/PatrickJohnRamos/BERT-Japan-vaccination). # Intended uses and limitations This model can be used to predict intensities scores for eight emotions for writers and readers. Please refer to the `Fine-tuning data` section for the list of emotions. Because of the regression fine-tuning task, it is possible for the model to infer scores outside of the range of the scores of the fine-tuning data (`score < 0` or `score > 4`). # Model Architecture, Tokenization, and Pretraining The Japanese BERT<sub>BASE</sub> fine-tuned was `cl-tohoku/bert-base-japanese-v2`. Please refer to their [model card](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) for details regarding the model architecture, tokenization, pretraining data, and pretraining procedure. # Fine-tuning data The model is fine-tuned on [WRIME](https://github.com/ids-cv/wrime), a dataset of Japanese Tweets annotated with writer and reader emotion intensities. We use version 1 of the dataset. Each Tweet is accompanied by a set of writer emotion intensities (from the author of the Tweet) and three sets of reader emotions (from three annotators). The emotions follow Plutchhik's emotions, namely: * joy * sadness * anticipation * surprise * anger * fear * disgust * trust These emotion intensities follow a four-point scale: | emotion intensity | emotion presence| |---|---| | 0 | no | | 1 | weak | | 2 | medium | | 3 | strong | # Fine-tuning The BERT is fine-tuned to directly regress the emotion intensities of the writer and the averaged emotions of the readers from each Tweet, meaning there are 16 outputs (8 emotions per writer/reader). The fine-tuning was inspired by common BERT fine-tuning procedures. The BERT was fine-tuned on WRIME for 3 epochs using the AdamW optimizer with a learning rate of 2e-5, β<sub>1</sub>=0.9, β<sub>2</sub>=0.999, weight decay of 0.01, linear decay, a warmup ratio of 0.01, and a batch size of 32. Training was conducted with an NVIDIA Tesla K80 and finished in 3 hours. # Evaluation results Below are the MSEs of the BERT on the test split of WRIME. | Annotator | Joy | Sadness | Anticipation | Surprise | Anger | Fear | Disgust | Trust | Overall | |---|---|---|---|---|---|---|---|---|---| | Writer | 0.658 | 0.688 | 0.746 | 0.542 | 0.486 | 0.462 | 0.664 | 0.400 | 0.581 | | Reader | 0.192 | 0.178 | 0.211 | 0.139 | 0.032 | 0.147 | 0.123 | 0.029 | 0.131 | | Both | 0.425 | 0.433 | 0.479 | 0.341 | 0.259 | 0.304 | 0.394 | 0.214 | 0.356 |

Mor1998/distilbert-base-uncased-distilled-clinc

Mor1998

2023-03-22T07:58:27Z

3

0

transformers

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

text-classification

2023-03-22T07:48:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-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: 0.9470967741935484 ---  # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1678 - Accuracy: 0.9471 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.511 | 1.0 | 318 | 1.0358 | 0.7565 | | 0.806 | 2.0 | 636 | 0.5325 | 0.8803 | | 0.4369 | 3.0 | 954 | 0.3103 | 0.9219 | | 0.2731 | 4.0 | 1272 | 0.2269 | 0.9368 | | 0.2042 | 5.0 | 1590 | 0.1968 | 0.9403 | | 0.175 | 6.0 | 1908 | 0.1824 | 0.9465 | | 0.1589 | 7.0 | 2226 | 0.1745 | 0.9465 | | 0.1498 | 8.0 | 2544 | 0.1708 | 0.9468 | | 0.1445 | 9.0 | 2862 | 0.1686 | 0.9461 | | 0.1425 | 10.0 | 3180 | 0.1678 | 0.9471 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

easyNLP/distilbert-base-uncased-finetuned-emotion

easyNLP

2023-03-22T07:45:07Z

3

0

transformers

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

text-classification

2023-02-23T08:32:13Z

--- 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: 0.2078 - Accuracy: 0.9225 - F1: 0.9228 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7799 | 1.0 | 250 | 0.2978 | 0.9045 | 0.9020 | | 0.2346 | 2.0 | 500 | 0.2078 | 0.9225 | 0.9228 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.13.1+cu116 - Datasets 2.8.0 - Tokenizers 0.10.3

domenicrosati/led-base-16384-biolaysum-both-with_references

domenicrosati

2023-03-22T07:41:03Z

4

0

transformers

[ "transformers", "pytorch", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-03-20T17:44:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: led-base-16384-biolaysum-both-with_references results: [] ---  # led-base-16384-biolaysum-both-with_references This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1595 - Rouge1: 0.4548 - Rouge2: 0.1555 - Rougel: 0.2435 - Rougelsum: 0.2435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.2751 | 0.69 | 5000 | 2.2219 | 0.4488 | 0.1496 | 0.2392 | 0.2392 | | 2.0407 | 1.37 | 10000 | 2.1595 | 0.4548 | 0.1555 | 0.2435 | 0.2435 | | 1.9246 | 2.06 | 15000 | 2.1263 | 0.4537 | 0.1522 | 0.2395 | 0.2396 | | 1.9066 | 2.75 | 20000 | 2.1091 | 0.4562 | 0.1538 | 0.2409 | 0.2409 | | 1.7802 | 3.43 | 25000 | 2.0998 | 0.4539 | 0.1523 | 0.2411 | 0.2411 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.1 - Datasets 2.10.1 - Tokenizers 0.12.1

dhanyaXchandra/femasturboobs

dhanyaXchandra

2023-03-22T07:14:34Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T07:13:30Z

--- license: creativeml-openrail-m ---

dhanyaXchandra/cameltoe

dhanyaXchandra

2023-03-22T07:13:01Z

0

1

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T07:08:55Z

--- license: creativeml-openrail-m ---

Splend1dchan/canine-c-squad

Splend1dchan

2023-03-22T07:09:39Z

89

0

transformers

[ "transformers", "pytorch", "safetensors", "canine", "question-answering", "endpoints_compatible", "region:us" ]

question-answering

2022-04-08T14:16:41Z

python run_squad.py \ --model_name_or_path google/canine-c \ --do_train \ --do_eval \ --per_gpu_train_batch_size 1 \ --per_gpu_eval_batch_size 1 \ --gradient_accumulation_steps 128 \ --learning_rate 3e-5 \ --num_train_epochs 3 \ --max_seq_length 1024 \ --doc_stride 128 \ --max_answer_length 240 \ --output_dir canine-c-squad \ --model_type bert { "_name_or_path": "google/canine-c", "architectures": [ "CanineForQuestionAnswering" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 57344, "downsampling_rate": 4, "eos_token_id": 57345, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "local_transformer_stride": 128, "max_position_embeddings": 16384, "model_type": "canine", "num_attention_heads": 12, "num_hash_buckets": 16384, "num_hash_functions": 8, "num_hidden_layers": 12, "pad_token_id": 0, "torch_dtype": "float32", "transformers_version": "4.19.0.dev0", "type_vocab_size": 16, "upsampling_kernel_size": 4, "use_cache": true } {'exact': 58.893093661305585, 'f1': 72.18823344945899}

Mor1998/distilbert-base-uncased-finetuned-clinc

Mor1998

2023-03-22T07:07:01Z

6

0

transformers

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

text-classification

2023-03-21T03:56:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: - task: name: Text Classification type: text-classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - name: Accuracy type: accuracy value: 0.9183870967741935 ---  # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7721 - Accuracy: 0.9184 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 | | 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 | | 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 | | 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 | | 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.13.1+cu116 - Datasets 1.16.1 - Tokenizers 0.10.3

dhanyaXchandra/skirtlift

dhanyaXchandra

2023-03-22T07:00:56Z

0

2

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T06:58:13Z

--- license: creativeml-openrail-m ---

pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555

pfunk

2023-03-22T06:59:25Z

0

cleanrl

[ "cleanrl", "tensorboard", "PongNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-03-22T06:59:16Z

--- tags: - PongNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation library_name: cleanrl model-index: - name: DQPN_freq results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PongNoFrameskip-v4 type: PongNoFrameskip-v4 metrics: - type: mean_reward value: 20.20 +/- 0.00 name: mean_reward verified: false --- # (CleanRL) **DQPN_freq** Agent Playing **PongNoFrameskip-v4** This is a trained model of a DQPN_freq agent playing PongNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/P_DQPN_x2.py). ## Get Started To use this model, please install the `cleanrl` package with the following command: ``` pip install "cleanrl[P_DQPN_x2]" python -m cleanrl_utils.enjoy --exp-name P_DQPN_x2 --env-id PongNoFrameskip-v4 ``` Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail. ## Command to reproduce the training ```bash curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/dqpn_freq_atari.py curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/pyproject.toml curl -OL https://huggingface.co/pfunk/PongNoFrameskip-v4-P_DQPN_x2-seed555/raw/main/poetry.lock poetry install --all-extras python dqpn_freq_atari.py --track --wandb-entity pfunk --wandb-project-name dqpn --capture-video true --save-model true --upload-model true --hf-entity pfunk --exp-name P_DQPN_x2 --policy-network-frequency 2000 --seed 555 ``` # Hyperparameters ```python {'alg_type': 'dqpn_freq_atari.py', 'batch_size': 32, 'buffer_size': 1000000, 'capture_video': True, 'cuda': True, 'double_learning': False, 'end_e': 0.01, 'env_id': 'PongNoFrameskip-v4', 'exp_name': 'P_DQPN_x2', 'exploration_fraction': 0.2, 'gamma': 0.99, 'hf_entity': 'pfunk', 'learning_rate': 0.0001, 'learning_starts': 10000, 'max_gradient_norm': inf, 'policy_network_frequency': 2000, 'policy_tau': 1.0, 'save_model': True, 'seed': 555, 'start_e': 1.0, 'target_network_frequency': 1000, 'target_tau': 1.0, 'torch_deterministic': True, 'total_timesteps': 5000000, 'track': True, 'train_frequency': 1, 'upload_model': True, 'wandb_entity': 'pfunk', 'wandb_project_name': 'dqpn'} ```

dhanyaXchandra/breastinclassbetter

dhanyaXchandra

2023-03-22T06:51:04Z

0

2

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T06:48:01Z

--- license: creativeml-openrail-m ---

LKINGKK/2131

LKINGKK

2023-03-22T06:50:54Z

0

null

[ "arxiv:1910.09700", "region:us" ]

null

2023-03-22T06:50:16Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ### How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

dhanyaXchandra/creampiev11

dhanyaXchandra

2023-03-22T06:47:34Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-03-22T06:46:27Z

--- license: creativeml-openrail-m ---

vietgpt/bert-30M-cased

vietgpt

2023-03-22T06:47:30Z

6

0

transformers

[ "transformers", "pytorch", "safetensors", "roberta", "fill-mask", "vi", "dataset:hieunguyen1053/binhvq-news-corpus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-02-27T20:38:15Z

--- license: apache-2.0 datasets: - hieunguyen1053/binhvq-news-corpus language: - vi library_name: transformers pipeline_tag: fill-mask widget: - text: "Tôi là <mask> viên trường Đại học Tôn Đức Thắng" example_title: "Example 1" ---