license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['exbert'] | false | GPT-2 Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_mul... | 202a87dfa2d1db517060de1e950fab88 |
mit | ['exbert'] | false | How to use 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 >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello,... | 3f0c6e6a93fecbaef8d9530bb388ef1e |
mit | ['exbert'] | false | out-of-scope-use-cases): > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases > that require the generated text to be true. > > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do > not recommend... | ecf6e8d6cbe955318f5ed9bf93235bf1 |
other | ['text-generation'] | false | How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-350m") >>> generator("Hello, I'm am conscious and") [{'generated_text': "Hello, I'm am conscious and I'm a bit of a noob. I'... | 38945849f91f67a5473c66b01a650546 |
other | ['text-generation'] | false | Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data... | 9005458ccc2bf0aadea5e639395a9aee |
mit | ['javanese-distilbert-small'] | false | Javanese DistilBERT Small Javanese DistilBERT Small is a masked language model based on the [DistilBERT model](https://arxiv.org/abs/1910.01108). It was trained on the latest (late December 2020) Javanese Wikipedia articles. The model was originally HuggingFace's pretrained [English DistilBERT model](https://huggingf... | 3f01c15a4729f080b5c727bffd43db98 |
mit | ['javanese-distilbert-small'] | false | params | Arch. | Training/Validation data (text) | |-----------------------------|---------|------------------|-------------------------------------| | `javanese-distilbert-small` | 66M | DistilBERT Small | Javanese Wikipedia (319 MB of text) | | 94aa262721cd68599bd55f1cca521267 |
mit | ['javanese-distilbert-small'] | false | Evaluation Results The model was trained for 5 epochs and the following is the final result once the training ended. | train loss | valid loss | perplexity | total time | |------------|------------|------------|------------| | 3.088 | 3.153 | 23.54 | 1:46:37 | | e5946096abd1385d240fc8e33ba3752a |
mit | ['javanese-distilbert-small'] | false | As Masked Language Model ```python from transformers import pipeline pretrained_name = "w11wo/javanese-distilbert-small" fill_mask = pipeline( "fill-mask", model=pretrained_name, tokenizer=pretrained_name ) fill_mask("Aku mangan sate ing [MASK] bareng konco-konco") ``` | c9492eda38623c7882aec110bf47211b |
mit | ['javanese-distilbert-small'] | false | Feature Extraction in PyTorch ```python from transformers import DistilBertModel, DistilBertTokenizerFast pretrained_name = "w11wo/javanese-distilbert-small" model = DistilBertModel.from_pretrained(pretrained_name) tokenizer = DistilBertTokenizerFast.from_pretrained(pretrained_name) prompt = "Indonesia minangka nega... | 62d8eb729d6fe85eafce465dd1443ab1 |
mit | ['javanese-distilbert-small'] | false | Disclaimer Do remember that although the dataset originated from Wikipedia, the model may not always generate factual texts. Additionally, the biases which came from the Wikipedia articles may be carried over into the results of this model. | 7e896bbbc0d66e052badf1916910271d |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-wnli-from-scratch-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4922 | b360f0f487fc428fcd514e26d8d379bb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.1384 | 6.25 | 500 | 5.9999 | | 5.8428 | 12.5 | 1000 | 5.6581 | | 5.4846 | 18.75 | 1500 | 5.4843 | | 5.1716 | 25.0 | 2000 | 5.3955 ... | acb5af7e397f266aec773850dce82b65 |
apache-2.0 | ['generated_from_trainer'] | false | process-data This model is a fine-tuned version of [jhakaran1/bert-base-uncased-bert-mlm](https://huggingface.co/jhakaran1/bert-base-uncased-bert-mlm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8087 - Accuracy: 0.6792 | deac077ab70f004d38f308f10fa60ad7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6939 | 1.0 | 3907 | 0.7903 | 0.6660 | | 0.6155 | 2.0 | 7814 | 0.7929 | 0.6685 | | 0.5436 | 3.0 | 11721 | 0.8087 ... | af5276219b57d8ef07906a8f72e14404 |
mit | ['generated_from_trainer'] | false | trusting_swartz This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekk... | 7c09e49fd1c2e5475a955467da335519 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | 8fbf3b5a2d17824871c998ca6ac80a55 |
apache-2.0 | ['translation'] | false | opus-mt-en-run * source languages: en * target languages: run * OPUS readme: [en-run](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-run/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | cd01cfcb508f64e26803200797c344e7 |
apache-2.0 | ['generated_from_trainer'] | false | sentiment-analysis-browser-extension 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: 0.4233 - Accuracy: 0.8539 - F1: 0.8758 | e3e6c9952ad795439ca9bf093825bf5d |
apache-2.0 | ['robust-speech-event', 'automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 2.3347 - Wer: 1.0286 | b1d3458d1526e472ee4a7535dfe8da72 |
apache-2.0 | ['robust-speech-event', 'automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 80a0276ac6961d45af434e3260c09d02 |
apache-2.0 | ['robust-speech-event', 'automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.7838 | 0.01 | 5 | 14.5035 | 1.0 | | 13.0582 | 0.03 | 10 | 13.6658 | 1.0 | | 7.3034 | 0.04 | 15 | 9.7898 | 1.0 | |... | e666d954c9dc8681a4a0f1af2d434917 |
mit | ['generated_from_trainer'] | false | jovial_clarke This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-25000... | 0f72cb2927f8f6f5758564b16f3c1b49 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 9fd739a6ed091343fcdc8b7d216eab19 |
cc | [] | false | dvAuto is a custom tuned model built using the base SD v1.5, and trained on thirty-two 768x768px images of concept / sports / antique cars. Use the words "dvAuto" or "dvAuto style" near the beginning of the prompt. Sample images and prompt below. "dvAuto style, 85mm, telephoto, mountain background, low contrast, m... | c8a9c3f0a2b1076d5ed04557ed50aaed |
apache-2.0 | ['translation'] | false | opus-mt-ha-es * source languages: ha * target languages: es * OPUS readme: [ha-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ha-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 9e906d44be84a8fc21bf23f2b9662d82 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5162 | 0.7978 | | c400db48e654926d663ae6d625c966d8 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-glue-cola-custom-tokenizer-expand-vocab This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8843 | 73e88b326396ceba5d58d297bdc6e3fd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.6267 | 0.47 | 500 | 4.9363 | | 5.0496 | 0.94 | 1000 | 4.7414 | | 4.7524 | 1.4 | 1500 | 4.5982 | | 4.6772 | 1.87 | 2000 | 4.5334 ... | 6eb7b2362221d6600beaac6b4634ec39 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/Mex_Rbta_Opinion_Attraction This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0061 - Validation Loss: 0.0386 - Epoch: 2 | 6b605f93b7de593bf4988a3752151de8 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8979, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | 855ae9f76cade05e102f27a878c6d0ae |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.0863 | 0.0476 | 0 | | 0.0230 | 0.0353 | 1 | | 0.0061 | 0.0386 | 2 | | 78379805b98aeb54624645d72bfaecac |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1 (Legal BERTimbau) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. stjiris/bert-large-portuguese-cased-legal-mlm-sts-... | 67df5bfc87c7cda7ec7813830c71a722 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Isto é um exemplo", "Isto ... | 0be769c3f9718a6c3729dc59f353f1d6 |
mit | ['sentence-transformers', 'transformers', 'bert', 'pytorch', 'sentence-similarity'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1') model = AutoModel.from_pretrained('stjiris/bert-large-portuguese-cased-legal-mlm-sts-v1') | 447171f77ed90ca975a8533ed446af9b |
apache-2.0 | ['generated_from_keras_callback'] | false | nickmuchi/bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5685 - Epoch: 2 | f09dec4c3bf63ef5d7a40862441b98ad |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16635, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 206d486d85d009819832a45490fefbdb |
mit | ['labse', 'ner'] | false | This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats: 1. Wikiann (LOC, PER, ORG) 2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO) 3. SlavNER 17 (LOC, MISC, ORG, PER) 4. CNE5 (GEOPOLIT, LOC, MEDIA, PER,... | 40cf1893cbc204f36b68df75af4f8d9f |
apache-2.0 | ['translation'] | false | dan-rus * source group: Danish * target group: Russian * OPUS readme: [dan-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/dan-rus/README.md) * model: transformer-align * source language(s): dan * target language(s): rus * model: transformer-align * pre-processing: normalization + Sente... | 5c9d2b51b46c6e1838ec2ff38ef7fcc5 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: dan-rus - source_languages: dan - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/dan-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['da', 'ru'] - src_constituents: {'dan'} - tgt_const... | 01cee32c9dcbcd69291da9eb69e8faf5 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | MAXIM pre-trained on SIDD for image denoising MAXIM model pre-trained for image denoising. It was introduced in the paper [MAXIM: Multi-Axis MLP for Image Processing](https://arxiv.org/abs/2201.02973) by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released i... | 40a0aa71b5dd3241e9b647e435b7998d |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | Intended uses & limitations You can use the raw model for image denoising tasks. The model is [officially released in JAX](https://github.com/google-research/maxim). It was ported to TensorFlow in [this repository](https://github.com/sayakpaul/maxim-tf). | 93854fbfda21e448fdf257b765bdda54 |
apache-2.0 | ['vision', 'maxim', 'image-to-image'] | false | How to use Here is how to use this model: ```python from huggingface_hub import from_pretrained_keras from PIL import Image import tensorflow as tf import numpy as np import requests url = "https://github.com/sayakpaul/maxim-tf/raw/main/images/Denoising/input/0011_23.png" image = Image.open(requests.get(url, strea... | 6724d61ff5918f6b660090f0621b52d9 |
mit | [] | false | model by Bioskop This your the Stable Diffusion model fine-tuned the LucyEdge concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **LucyEdge from edgerunners, a cyberpunk anime from Cyberpunk 2077 universe** You can also train your own concepts and upload them to the... | d78fbb2683d00d7f3bf69b6a4e721b5e |
apache-2.0 | ['generated_from_trainer'] | false | dark-bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Precision: 0.9283 - Recall: 0.9478 - F1: 0.9380 - Accuracy: 0.9859 | dfc978c5444efe944c2e5089f87e1939 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0881 | 1.0 | 1756 | 0.0716 | 0.9172 | 0.9322 | 0.9246 | 0.9817 | | 0.0375 | 2.0 |... | 456513499892c3f278105d093ccb1ab2 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mt5-small-jaquad-qag` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417... | fe74b074f9015f78b0562b9c25f05c58 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ja - **Training data:** [lmqg/qag_jaquad](https://huggingface.co/datasets/lmqg/qag_jaquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi4... | ca6c3abcd8873ea80fee5201b780037c |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("フェルメールの作品では、17世紀のオランダの画家、ヨハネス・フェルメールの作品について記述する。フェルメールの作品は、疑問作も含め30数点しか現存しない。現存作品はすべて油彩画で、版画、下絵、素描などは残っていない。") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-jaquad-qag") output... | 3e6d0af60a167c1b722f2a737df29680 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-jaquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_jaquad.default.json) | | Score | Type | Dataset ... | 8ba527d794844deba8a1bb93870a3253 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_jaquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 256 - epoch: 18... | 6682246f6f05ea51069a2c076727a103 |
mit | ['generated_from_trainer'] | false | deberta-base-combined-squad1-aqa-1epoch This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9431 | 03b582339ccdf90ab336e73ded713db0 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-152-fv-finetuned-memess This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6281 - Accuracy: 0.7674 - Precision: 0.7651 - Recall: 0.7674 - F1: 0.7647 | 8ac693b4ba55a258480f9d58b0436c79 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.5902 | 0.99 | 20 | 1.5519 | 0.4938 | 0.3491 | 0.4938 | 0.3529 | | 1.4694 | 1.99 |... | 78ef87127a333152492f5c3568592841 |
mit | [] | false | Hours_Sentry_fade on Stable Diffusion This is the `<Hours_Sentry>` 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... | a46c8f5103a63b26701fce1f7ae708aa |
apache-2.0 | ['vision', 'simmim'] | false | Swin Transformer (base-sized model) Swin Transformer model pre-trained on ImageNet-1k using the SimMIM objective at resolution 192x192. It was introduced in the paper [SimMIM: A Simple Framework for Masked Image Modeling](https://arxiv.org/abs/2111.09886) by Xie et al. and first released in [this repository](https:/... | 659d97cf695bc3bbe208aaab5432e50a |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-wandb-week-3-complaints-classifier-512 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set: - Loss: 0.6004 - Accuracy: 0.8038 - F1: 0.7919 - Recall: ... | 0c53db2b9e6924132f6f6019de67f02a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7835312622444155e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 512 - num_epochs: 2 - mi... | 0aa33b9bd96b1767f8a2350a65da0e1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.7559 | 0.61 | 1500 | 0.7307 | 0.7733 | 0.7411 | 0.7733 | 0.7286 | | 0.6361 | 1.22 |... | d22a99b46c92a20050998cdf79e3cd06 |
mit | ['text-classification', 'generated_from_trainer'] | false | deberta-v3-xsmall-finetuned-review_classifier This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1441 - Accuracy: 0.9513 - F1: 0.7458 | b77526baf77d4249c1cc85442125aa39 |
mit | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.1518 | 1.0 | 6667 | 0.1575 | 0.9510 | 0.7155 | | 0.1247 | 2.0 | 13334 | 0.1441 | 0.9513 | 0.7458 | | 2c21f65699c9d5cde12e2fe3105006b7 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_gender_male-8_female-2_s26 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | c640a5d17631c6e395fd14a87522bc8a |
apache-2.0 | ['translation'] | false | opus-mt-sv-srn * source languages: sv * target languages: srn * OPUS readme: [sv-srn](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-srn/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | b32e1a8779147d182b3951c4d815e070 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_2_ternary_v1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8941 - F1: 0.7889 | bc2b11494df72a6845ce1fc5059caad7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 294 | 0.6025 | 0.7402 | | 0.5688 | 2.0 | 588 | 0.5025 | 0.7943 | | 0.5688 | 3.0 | 882 | 0.6102 | 0.7794 | |... | 4f2b9cdba8b64b416f297f1624c86f89 |
mit | ['text-classification', 'zero-shot-classification'] | false | Model description This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://g... | 970d9ad8780dfb270f4de0795a2e5a9f |
mit | ['text-classification', 'zero-shot-classification'] | false | How to use the model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary" tokenizer = AutoTokenizer.from_pretrained... | 3635b9073f1a88c3b9f5ea769b5e803d |
mit | ['text-classification', 'zero-shot-classification'] | false | device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "not_entailment"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` | b9ac01f9594a9d32408758c7a064bffe |
mit | ['text-classification', 'zero-shot-classification'] | false | Training data This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [ANLI](https://githu... | 310c7d2707a22e93398685eb2090faf8 |
mit | ['text-classification', 'zero-shot-classification'] | false | Training procedure DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=5, | 90aaa5fd9e1f386a490c59a2db018704 |
mit | ['text-classification', 'zero-shot-classification'] | false | Eval results The model was evaluated using the binary test sets for MultiNLI, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy. dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c --------|---------|----------|---------|... | 9f1397a5edf593bc6ef14a22c14956da |
mit | ['text-classification', 'zero-shot-classification'] | false | Debugging and issues Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues. | e61ca296f65f66e6d7f7fcb2a528ac22 |
bsd-2-clause | ['biology', 'chemistry'] | false | Dataset description An integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures labelled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature. | f21a43faa8dd68b695f665596d2c260a |
bsd-2-clause | ['biology', 'chemistry'] | false | Model description Morgan chemical fingerprint with an MLP decoder. Model is tuned with 100 runs using Ax platform. To load the pre-trained model, type ```python from tdc import tdc_hf_interface tdc_hf_herg = tdc_hf_interface("hERG_Karim_Morgan") | 895502bc6287947d3464908f96a02beb |
bsd-2-clause | ['biology', 'chemistry'] | false | References: [1] Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13, 60 (2021). https://doi.org/10.1186/s13321-021-00541-z | 54dd28441f6c7c8f30e2b520b95f1c3d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.1004 - Accuracy: 0.9410 | 444aacdd3f404303ea1d7554a0f01398 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9037 | 1.0 | 318 | 0.5745 | 0.7326 | | 0.4486 | 2.0 | 636 | 0.2866 | 0.8819 | | 0.2537 | 3.0 | 954 | 0.1794 | 0.... | d0daec9351b167e3b6e178dd6f4da504 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-greenplastics-2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0162 - Accuracy: 0.9958 - F1: 0.9958 | d8052f5ebc852a48ac29e04310d1072a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.0289 | 1.0 | 123 | 0.0238 | 0.9949 | 0.9949 | | 0.0112 | 2.0 | 246 | 0.0162 | 0.9958 | 0.9958 | | 9eaa30b03caf8787e98f3792a1421380 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-telugu-asr This model is a fine-tuned version of [henilp105/wav2vec2-large-xls-r-300m-telugu-asr](https://huggingface.co/henilp105/wav2vec2-large-xls-r-300m-telugu-asr) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1050 - Wer: 0.6656 | cea8774d1fae12b9339fc0e3449ccda8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.0506 | 2.3 | 200 | 0.8841 | 0.7564 | | 0.6354 | 4.59 | 400 | 0.7448 | 0.6912 | | 0.3934 | 6.89 | 600 | 0.8321 | 0.6929 | |... | d3c7d0baaa0b840afe9db5cd5ad531e1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 4em; text-align: center; color:black; font-family: Segoe UI"> <a href="https://huggingface.co/SweetLuna/Kenshi/blob/main/README.md" style="text-decoration: none; background-color: transparent;">Kenshi</a> </h1> <a href="https://lensdump.com/i/RL8CTQ"><img src="https://i1.lensdump.com/i/RXYEm2.... | 759c706938b240f1b2b9dd5ab697b6c1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 1.75em; font-family: Segoe UI">[CivitAI](https://civitai.com/models/3850) | [Download](https://huggingface.co/SweetLuna/Kenshi/tree/main/KENSHI%2001) | [Changelog](https://huggingface.co/SweetLuna/Kenshi/blob/main/Changelog.md)</h1> <hr> <style>▼-preamble { font-size: 2em; }</style> <details id... | 1a4af2a3e70435ea7901d37d877e8e33 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | disclaimer) </strong> </h1> </details> <hr> <details id="▼-preamble"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏮 What is Kenshi?</strong></summary> <hr> <h1> **Kenshi** is my personal merges which created by combining different models together. ***This includes m... | c74c5c9d3a279d7d985e57350743878f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 1.5em; text-align: center; color:black; font-family: Segoe UI"> These are the settings I always use it is recommended but not essential; | Settings | Value | | ----------------- | --------------------------------------... | bd375700822e0fefb418e1d02a7dd9b4 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | Unlike most models, Kenshi is known for its versatility, able to perform various styles with remarkable results. I've undergone testing with over 30 to 50 styles and most of the time I get remarkable results. I recommend using Lora and Embedding to improve this even further. <center><a href="https://i2.lensdump.com/i... | dfa89e430cf418cead97d682ff68f097 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | I recommend <a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**kl-f8-anime2.ckpt**</a> VAE from waifu-diffusion-v1-4 <a href="https://huggingface.co/hakurei">which is made by hakurei.</a> </h1> <a href="https://i2.lensdump.com/i/RbBe37.png"><img src="https://i2.lensd... | c7ac3d6d71670ea46dfda7ce70fa270e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 2.5em;"><a href="https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/vae/kl-f8-anime2.ckpt" >**VAE is important, please download it.**</h1></a> </details> <hr> <details id="▼-sample"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🏔️ Examples Images</strong></su... | 9318c3ce8a715a63d7f8641037de26db |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | 1girl, highly detailed face, bleak and dangerous atmosphere, moody, (dynamic pose:1.6), cataclysmic magic, dark blue wavy long hair, (glowing eyes:0.85), (reaching through a magic circle:1.35), extremely detailed 8k wallpaper, (highly detailed:1.1), [anime:Impasto:0.5], intricate, fantasy, clear sky, wind, beautiful s... | 2931e16d40e712370db74b4df9559d04 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | **KENSHI 00** </details> <hr> <details id="▼-chatgpt"> <summary style="font-size: 1.75em; font-family: monospace"><strong>ChatGPT Prompt ⚙️</strong></summary> <img src="https://i.lensdump.com/i/RLkz3v.png" alt=”2”> <img src="https://i1.lensdump.com/i/RLkFND.png" alt=”3”> <img src="https://i3.lensdump.com/i/RLkulr... | 1808a9db28d94e12217313c0fc67dcd1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | (A cursed knight, clad in black armor,) must journey through a desolate, haunted land to reach the Elden Ring and lift the (curse that plagues their soul.)Along the way, they encounter other travelers, (each struggling with their own demons and secrets), As they draw closer to the Elden Ring, they are confronted with ... | 375a6970f4ecad4b0dd5590c486cc088 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | **KENSHI 00** </details> <hr> <details id="▼-vivid"> <summary style="font-size: 1.75em; font-family: monospace"><strong>Vivid 🌈</strong></summary> <img src="https://i.lensdump.com/i/RXY1Fo.png" alt=”5”> ```c | 7f1c067191304e742ab4ec1f9299a674 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | close POV, young adult woman, blue purple green color palette, black hair with dark green shine, two symmetrical antennae on head, big blue eyes sparkling, rings around eyes, two-tone black and red, smiling at the camera, elegant pose, looking at the viewer, vivid stained glass window background, oil painting, charact... | 0a37a8cb1f02db25e96a77f731be952f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | **KENSHI 01** </details> <hr> <details id="▼-moon"> <summary style="font-size: 1.75em; font-family: monospace"><strong>Moon 🌙</strong></summary> <img src="https://i2.lensdump.com/i/RXYt7i.png" alt=”6”> ```c | 33830e65abe15faad84f95e0cf2fce7c |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | (on the moon, space, looking back into earth), white hair, black tank top, volumetric lighting, white jacket, glowing headphone, cyberpunk, futuristic, multi-color eyes, detailed eyes, hyper detailed,light smile, highly detailed, beautiful, small details, ultra detailed, best quality, intricate, hyperrealism, sharp, ... | f12ea676a19995c461f931e20384a081 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | garden_1-kenshi server</h1> <a href="https://discord.gg/pD9MKyBgNp"><img src="https://i.lensdump.com/i/RwAkqx.png" alt="RwAkqx.png" border="0" /></a> </details> <hr> <details id="▼-merge"> <summary style="font-size: 2.25em; font-family: Segoe UI"><strong>🍣 Merge Recipes</strong></summary> <hr> <h1><strong> <a href="... | 2d979a743c89cb3082c9d2b593e8e63f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 1.75em;">Trigger Words</h1> <hr> <h1 style="font-size: 1.5em;"> **Trigger Words are not required** but are meant to **enhance the effectiveness of the prompt** and improve the overall outcome. ```c | 28fdd686bb57c1a906308736ceb38b55 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 1.75em;">WebUI</h1> <hr> <h1 style="font-size: 1.5em;"> <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">AUTOMATIC1111</a> Grab it, a must-have. Have all the features you want and is easy to access. <hr> </h1> | 1719f7dfaf7c2b7449ba0b060d1f9ae3 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | <h1 style="font-size: 1.75em;">Embeddings</h1> <hr> <h1 style="font-size: 1.5em;"> I recommend grabbing ***all*** <a href="https://huggingface.co/Nerfgun3">Nerfgun3</a> embeddings ***and*** Sirveggie <a href="https://huggingface.co/SirVeggie/nixeu_embeddings">nixeu_embeddings</a> </h1> </details> ... | 02c17d2bc3973053672f457267a148f9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: ``` 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on t... | 7a9004715507d5fa0fa943097b74a1fe |
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