license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['translation'] | false | opus-mt-fi-ln * source languages: fi * target languages: ln * OPUS readme: [fi-ln](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ln/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](https://... | f7720c2edfdcbf14c1d49bd65b859e8a |
apache-2.0 | ['translation'] | false | opus-mt-fi-ee * source languages: fi * target languages: ee * OPUS readme: [fi-ee](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ee/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | 9f8971532bc44a9d1c4d22131decd0a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 80.0 | 7951a3c26c8c2f0f1977e24ea21db27b |
apache-2.0 | ['generated_from_trainer'] | false | first_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: 0.2883 - Accuracy: 0.87 - F1: 0.8713 | 4c0810b4c9146f869a0a681001bde25b |
mit | [] | false | Model Description <!-- Provide a longer summary of what this model is. --> ['To_Kill_a_Mockingbird', 'Dog', 'Pub', 'Paper', 'Brain', 'Wood', 'The_Times', 'Immunology', 'Animal', 'Beer', 'Emotion', 'Digestion', 'Adolescence', 'Poultry', 'Clothing', 'Chicago_Cubs', 'Professional_wrestling', 'Pesticide', 'Nutrition', '... | 03eb6c68b724622e82fafc9151b6bbb2 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.3172 - Accuracy: 0.86 - F1: 0.8609 | 172c38c45c746f86829ec83767230bf4 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | LoRA DreamBooth - lora-dreambooth-sample-dog These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "sksdog" using [DreamBooth](https://dreambooth.github.io/). You can find some example ... | 2cad713e1c20cb2e02083d92bd52c962 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2148 - Accuracy: 0.926 - F1: 0.9261 | 973fba61c39a5ce856bd30e24060162f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8297 | 1.0 | 250 | 0.3235 | 0.9015 | 0.8977 | | 0.2504 | 2.0 | 500 | 0.2148 | 0.926 | 0.9261 | | a5f3562cffed63dcbfd97dfc6d5f4e28 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 1ce1953eb7243339cead069aabda7975 |
apache-2.0 | ['generated_from_trainer'] | false | neuroscience-to-dev-bio-5 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0170 | 26b50597c46dee04760a190642b0d6cb |
apache-2.0 | ['generated_from_trainer'] | false | 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: 128 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | d3af5f95adadfe3c6cf126636519cfae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 19.0123 | 0.98 | 8 | 17.3893 | | 16.2978 | 1.98 | 16 | 14.4660 | | 13.2877 | 2.98 | 24 | 11.8149 | | 12.016 | 3.98 | 32 | 10.7524 ... | 85463c1ac2d6b370b1c91d399d2fc3fd |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-news-cad-v3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: | 923c26f31032951e080e4f6087cef351 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2163 - Accuracy: 0.9285 - F1: 0.9285 | dd44355d64975d6ee904a205a1fa3337 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8311 | 1.0 | 250 | 0.3113 | 0.9065 | 0.9023 | | 0.247 | 2.0 | 500 | 0.2163 | 0.9285 | 0.9285 | | 0db9356b9ca8dcd58297ebc77e4b8180 |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingDynamic'] | false | Post-training dynamic quantization This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [adasnew/t5-... | 65992cb22449fe64188f116705e2d854 |
apache-2.0 | ['int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingDynamic'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForSeq2SeqLM int8_model = IncQuantizedModelForSeq2SeqLM.from_pretrained( 'Intel/t5-small-xsum-int8-dynamic', ) ``` | 5185896f8da5c8a19ca521826813a220 |
mit | ['generated_from_trainer'] | false | roberta-base.CEBaB_confounding.observational.absa.5-class.seed_42 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset. It achieves the following results on the evaluation set: - Loss: 0.4927 - Accuracy: 0.8868 - Macro-f1: 0.8847 - Weighted-... | b55da23893e7c0f426e844a4e030281a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | DreamBooth model for the pai concept trained by 0xAnders. This is a Stable Diffusion model fine-tuned on the pai concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of pai symbol** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://h... | 6eea1e16cd597e258063ef8d5e9326f0 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'science'] | false | Description This is a Stable Diffusion model fine-tuned on `symbol` images for the science theme, for the Hugging Face DreamBooth Hackathon, from the HF CN Community, corporated with the HeyWhale. | 831fb19555556199b7cfdcc5671d288c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-6feb-5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5265 - Rouge1: 18.25 - Rouge2: 5.96 - Rougel: 17.96 | c322a9f815af8818aa852bf9bc6d627f |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 5 | 6799da8c6bacb70189c0b4fea99c1855 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 5.1138 | 1.0 | 311 | 2.7003 | 16.27 | 5.15 | 16.11 | | 3.358 | 2.0 | 622 | 2.5948 | 17.74 | 5.36 ... | 5d1a581df25237071aa3deaf8d9043a2 |
apache-2.0 | ['translation'] | false | opus-mt-bcl-es * source languages: bcl * target languages: es * OPUS readme: [bcl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/bcl-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-15.zip](http... | e9b7801e826b966bfba3c9f8ddfeafb4 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_r-wav2vec2_s732 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speec... | ca3b43ea5135b382ea039a763be47e30 |
creativeml-openrail-m | ['text-to-image'] | false | polisteps 768 Dreambooth model trained by multimodalart with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-768 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface... | 2f9f83416b258ba67e61fa89ba8576c0 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.2161 - Accuracy: 0.926 - F1: 0.9261 | 1aeefe51e5dcc0101c92a072d2afee5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8436 | 1.0 | 250 | 0.3175 | 0.9105 | 0.9081 | | 0.2492 | 2.0 | 500 | 0.2161 | 0.926 | 0.9261 | | d7ee74e53b5300f8e826295ebf42512c |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-squad2-with-ner-mit-movie-with-neg-with-repeat This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the mit_movie datasets. | aff18e1c21553021fe6bf599f7a91883 |
apache-2.0 | ['generated_from_keras_callback'] | false | evegarcianz/bert-finetuned-squad This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.4637 - Epoch: 1 | c9ac4666ef3268762a977b4e7b6fe999 |
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': 33276, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | df28217239b21e2c82d057b61925c3c4 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3 This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3) on the scientific_papers dataset. It achieves the following results on the evaluatio... | aeecc8095070aa7ea26448073750d994 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.185 | 1.0 | 33840 | 2.1825 | 42.2455 | 15.6488 | 24.4935 | 37.9427 ... | e1116dccc441a0602b5ba0ba631a7451 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-ca 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 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datasets... | 47ec490c166502b4ac31066badd395fb |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Intended uses & limitations As any model trained on crowdsourced data, this model can show the biases and particularities of the data and model used to train this model. Moreover, since this is a speech recognition model, it may underperform for some lower-resourced dialects for the catalan language. | 8f875ab978efa9751e5ece6bc6bb1461 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Training procedure The data is preprocessed to remove characters not on the catalan alphabet. Moreover, numbers are verbalized using code provided by [@ccoreilly](https://github.com/ccoreilly), which can be found on the text/ folder or [here](https://github.com/CollectivaT-dev/catotron-cpu/blob/master/text/numbers_ca... | 7db14975693ac29e68c00cd66f8c0d41 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-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_... | eeaf04a4ec8a2a5f5ea83b381ab814e9 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Training results Check the Tensorboard tab to check the training profile and evaluation results along training. The model was evaluated on the test splits for each of the datasets used during training. | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:--... | 1ffef6d5f0ee64ae133b2698959a30f1 |
apache-2.0 | ['automatic-speech-recognition', 'collectivat/tv3_parla', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'projecte-aina/parlament_parla', 'robust-speech-event'] | false | Thanks Want to thank both [@ccoreilly](https://github.com/ccoreilly) and [@gullabi](https://github.com/gullabi) who have contributed with their own resources and knowledge into making this model possible. | a17288dbc118b8a4d503c962aaa37bf3 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ru-finetuned-en-to-ru-PSUR This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5359 - Bleu: 63.0879 - Gen Len: 40.2272 | 9f6eca416370cdbbbceecb43c5c77c71 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 342 | 0.8518 | 51.2732 | 39.905 | | 1.0725 | 2.0 | 684 | 0.7179 | 55.1029 | 39.9693 | | 0.674 |... | 3ea6daa97f3f07074b4582681db6b446 |
apache-2.0 | [] | false | PaddlePaddle/uie-senta-nano Sentiment analysis is a research hotspot in recent years, aiming at analyzing, processing, summarizing and reasoning emotionally subjective texts. Sentiment analysis has a wide range of application scenarios and can be applied to consumer decision-making, public opinion analysis, personali... | a3e2ac8203fd7e5e18efcc071ce91cb6 |
apache-2.0 | [] | false | Available Models | Model Name | Model Config | | :---------------: | :-----------------------------: | | `uie-senta-base` | 12-layers, 768-hidden, 12-heads | | `uie-senta-medium` | 6-layers, 768-hidden, 12-heads | | `uie-senta-mini` | 6-layers, 384-hidden, 12-heads | | `uie-... | 6410afd1c5c29d3d3d48b6b5da718b94 |
apache-2.0 | [] | false | Performance on Text Dataset We conducted experiments to compare the performance different Models based on a self-built test set, which containing samples from multiple fields, such as hotel, restaurant，clothes and so. The comparison results are as follows. | Model Name | Precision | Recall | F1 ... | 66c209e0090097c33660b48db6069be1 |
apache-2.0 | [] | false | distilbert-base-th-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | c68e2092a76554bf7ea50a538a036e95 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-th-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-th-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 0decce8bb0df022cb9497ba8da5ff7ea |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | IT5 Small for Formal-to-informal Style Transfer 🤗 This repository contains the checkpoint for the [IT5 Small](https://huggingface.co/gsarti/it5-small) model fine-tuned on Formal-to-informal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-t... | abb27ac36d5d01b52c3935e61f8c2d16 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines f2i = pipeline("text2text-generation", model='it5/it5-small-formal-to-informal') f2i("Vi ringrazio infinitamente per vostra disponibilit... | c4aabc540dc65fc4cfbeec61ef6ee3e8 |
mit | ['punctuation prediction', 'punctuation'] | false | Classification report over all languages ``` precision recall f1-score support 0 0.99 0.99 0.99 47903344 . 0.94 0.95 0.95 2798780 , 0.85 0.84 0.85 3451618 ? 0.88 0.85 0.87 88876 ... | 7668069161c6868d2b678b613e8ff0d2 |
mit | ['punctuation prediction', 'punctuation'] | false | How to cite us ``` @article{guhr-EtAl:2021:fullstop, title={FullStop: Multilingual Deep Models for Punctuation Prediction}, author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim}, booktitle = {Proceedings of the Swiss Text Analytics Conference 2021}, month... | bd0d9e8bfbddcde8081e78b455570a78 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-Total2e-4_4 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2474 - Wer: 0.1951 | d45fb08d3e57669c655d7d887f2a46ce |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | a664cc87b95791df1db6138385ca3539 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.5015 | 0.1 | 200 | 2.9261 | 0.9707 | | 2.9197 | 0.2 | 400 | 2.7757 | 0.9707 | | 1.7594 | 0.3 | 600 | 0.6117 | 0.574... | 304e97bfb7aec6aa29df3a12c21487d6 |
apache-2.0 | ['generated_from_trainer'] | false | 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: 0.0599 - Precision: 0.9371 - Recall: 0.9530 - F1: 0.9450 - Accuracy: 0.9865 | af50b19e25aa35748605e1faec486f2f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0883 | 1.0 | 1756 | 0.0690 | 0.9181 | 0.9320 | 0.9250 | 0.9821 | | 0.0334 | 2.0 |... | 6da18e1a8f57fa827bae90947c21ba00 |
openrail | ['Angelcore', 'Devilcore', 'Dieselpunk', 'Steampunk', 'Clockpunk', 'Fantasy', 'Gothic Style', 'Nu-Gothic Style', 'Gothic Art', 'Dark Fantasy', 'Dark Art', 'Medieval', 'Modern', 'Futuristic', 'Cybernetic', 'Magic tech', 'Magic Circles', 'Cute Girls', 'Beautiful Women', 'Creepy Women', 'Creepy Girls', 'Evil', 'Wicked', '... | false | Gemini is a Dark Fantasy Anime focused merge of several models using various combination methods to attempt and extract specific styles. The first version of Gemini_Anime is for darker renders or more fantasy based.\ This particular model has a heavier lean on dark art, gothic art and scene based (action etc rather tha... | 8de47da5dfa27f7200a8eccb42b58842 |
cc | [] | false | To create a todo application with JavaScript, you will need to use HTML and CSS to build the user interface, and JavaScript to add functionality to the app. Here is an outline of the steps you can follow to build a simple todo app: Create an HTML page with a textarea element and a button element. The textarea will be... | 439f4c4a65305eaef014a513d139714a |
cc-by-4.0 | [] | false | HindBERT HindBERT is a Hindi BERT model. It is a multilingual BERT (google/muril-base-cased) model fine-tuned on publicly available Hindi monolingual datasets. [project link] (https://github.com/l3cube-pune/MarathiNLP) More details on the dataset, models, and baseline results can be found in our [<a href='https://ar... | 6d2c87813d25d592ccb1689dcb93a188 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Welsh - Robust This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 cy dataset. It achieves the following results on the evaluation set: - Loss: 0.4569 - Wer: 23.0736 | c5738ff03cce541e80414516e764cd40 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0597 | 6.1 | 1000 | 0.4690 | 29.4666 | | 0.0107 | 12.2 | 2000 | 0.4707 | 26.2671 | | 0.0026 | 18.29 | 3000 | 0.4643 | 24.676... | 6326e5ad20f17c00423c733b102aad0f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 - Precision: 0.9251 - Recall: 0.9370 - F1: 0.9310 - Accuracy: 0.9839 | e3afef267f12658033af680aa782fee3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2435 | 1.0 | 878 | 0.0685 | 0.9182 | 0.9221 | 0.9202 | 0.9816 | | 0.0515 | 2.0 |... | 57998fc2ffe4d72014ef4559a4689b5f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3](https://huggingface.co/DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UR dataset. It achieves the following results on the evaluation set: - Loss: 1.5443 - Wer: 0.7030 | cadad88050272be27017937440002a0f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000388 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | ba8c140486e664055061fa71df3cd12f |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 10.7052 | 1.96 | 100 | 3.4683 | 1.0 | | 3.2395 | 3.92 | 200 | 3.1489 | 1.0 | | 2.9951 | 5.88 | 300 | 2.9823 | 1.0007 | |... | fe0672be05af29d39e8e11747568ad89 |
apache-2.0 | ['object-detection', 'vision'] | false | Deformable DETR model with ResNet-50 backbone, single scale Deformable DEtection TRansformer (DETR), single scale model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/... | a56d8c39de8d94bbb357c36ad88aa683 |
apache-2.0 | ['object-detection', 'vision'] | false | Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queri... | 5782fa4a241226ba1861641fa8aee884 |
apache-2.0 | ['object-detection', 'vision'] | false | Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=sensetime/deformable-detr) to look for all available Deformable DETR models. | 714c47c029ac7b5bcc034a9e4fb324c0 |
apache-2.0 | ['object-detection', 'vision'] | false | How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, DeformableDetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = Auto... | 7bc7e6a612b1b7b8f5f9fcbf16959b70 |
apache-2.0 | ['object-detection', 'vision'] | false | let's only keep detections with score > 0.7 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.... | 1781e33c4f75014d53f226281a4e7328 |
apache-2.0 | ['object-detection', 'vision'] | false | BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2010.04159, doi = {10.48550/ARXIV.2010.04159}, url = {https://arxiv.org/abs/2010.04159}, author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, keywords = {Computer Vision and Pattern Re... | 5bb2f1200ca874c32a6a142d9ecdddd1 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2715 - Rouge1: 0.8783 - Rouge2: 0.8348 - Rougel: 0.8739 - Rougelsum: 0.8746 | dc258e1ed8aaa4b8137395fddc504a99 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | b28166ce17e0559348242fd252985a91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 21 | 0.6229 | 0.7109 | 0.6524 | 0.7061 | 0.7071 | | No log | 2.0 | 42 ... | c8c0c147f900bdabf62d19e2e9b9aef3 |
unknown | ['stable-diffusion', 'text-to-image'] | false | Novelai-Diffusion Novelai-Diffusion is a latent diffusion model which can create best quality anime image. Here is the diffusers version of the model. Just to make it easier to use Novelai-Diffusion for all. | 155efc443369a9290bec0124d8b5d502 |
unknown | ['stable-diffusion', 'text-to-image'] | false | Gradio & Colab Demo There is a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run Novelai Diffusion: [](https://colab.research.google.com/drive/1fNscA4Xqga8DZVPYZo17OUyzk7tzOZEw) Run Novelai Diffusion on TPU... | d051c7b561c7c424f6b63399a7404cdb |
unknown | ['stable-diffusion', 'text-to-image'] | false | pytorch ```python from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("animelover/novelai-diffusion", custom_pipeline="waifu-research-department/long-prompt-weighting-pipeline", torch_dtype=torch.float16) pipe.safety_checker = None | 941bc6bad1e73b0bfac97ca2f6412170 |
unknown | ['stable-diffusion', 'text-to-image'] | false | we don't need safety checker. you can add not safe words to negative prompt instead. pipe = pipe.to("cuda") prompt = "best quality, masterpiece, 1girl, cute, looking at viewer, smiling, open mouth, white hair, red eyes, white kimono, sakura petal" neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, ... | e101a0aa08787139a4eb099690924aa5 |
unknown | ['stable-diffusion', 'text-to-image'] | false | we don't need autocast here, because autocast will make speed slow down. image = pipe.text2img(prompt,negative_prompt=neg_prompt, width=512,height=768,max_embeddings_multiples=5,guidance_scale=12).images[0] image.save("test.png") ``` | 3b0fe3ac67802ecd950acbf6cf4d2f0c |
unknown | ['stable-diffusion', 'text-to-image'] | false | onnxruntime ```python from diffusers import DiffusionPipeline pipe = DiffusionPipeline.from_pretrained("animelover/novelai-diffusion", revision="onnx16", custom_pipeline="waifu-research-department/onnx-long-prompt-weighting-pipeline", ... | 23fa931670dbc41667a085572cf0edb0 |
unknown | ['stable-diffusion', 'text-to-image'] | false | we don't need safety checker. you can add not safe words to negative prompt instead. prompt = "best quality, masterpiece, 1girl, cute, looking at viewer, smiling, open mouth, white hair, red eyes, white kimono, sakura petal" neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands,... | 2b2acf84bf9818692fb6e5e782c419d0 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9825 - Mae: 0.4956 | 53ce31afe306d915e14ec1328217cef0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1432 | 1.0 | 308 | 1.0559 | 0.5133 | | 0.9883 | 2.0 | 616 | 0.9825 | 0.4956 | | 5ebbb046c9e60e36bdfcff7b0e3f5d37 |
apache-2.0 | ['generated_from_trainer'] | false | correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3097 - ... | dff3788fd4150a941a399163abeccba6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.4573 | 0.0094 | 0.0027 | 0.0042 | 0.7702 | | No log | 2.0 |... | 2094de2b1c01429a0361d0e12360af6d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | fad4358c48d78c8494f40b40da913c60 |
afl-3.0 | [] | false | Model Description We release all models introduced in our [paper](https://arxiv.org/pdf/2206.11147.pdf), covering 13 different application scenarios. Each model contains 11 billion parameters. | Model | Description | Recommended Application | ----------- | ----------- |----------- | | rst-all-11b ... | fcda07e904f391b787edb54590500bd3 |
afl-3.0 | [] | false | Sample | Use in DataLab | Some Applications | | --- | --- | --- | --- | --- | | [Rotten Tomatoes](https://www.rottentomatoes.com/) | (review, rating) | 5,311,109 | `load_dataset("rst", "rotten_tomatoes_sentiment")` | Sentiment classification | | [Daily Mail](https://www.dailymail.co.uk/home/index.html) | (text, categ... | d6d6e64d4668eb54391f1d4a702eb5ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.1553 | 0.57 | | 4f3cdc1dbadd658bc0f3857a13dda6ad |
mit | [] | false | jozef-tominc2 on Stable Diffusion This is the `<jozef-tominc>` 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... | be65e5ed9f2a8c0ee1f4685ff069e54b |
apache-2.0 | ['generated_from_trainer'] | false | Wav2Vec2_xls_r_300m_hi_cv7 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6567 - Wer: 0.6273 - Cer: 0.2093 | 7e76ac898af68aadd7cb27376221fcca |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | c8031be156cf5c620c0ec24f83da74ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 5.6969 | 9.52 | 400 | 3.3092 | 1.0 | 0.9800 | | 1.7721 | 19.05 | 800 | 0.7769 | 0.7045 | 0.2367 | | 0.6384 | 28.57 |... | bc152232ddd17d49f88e31f1ea32a143 |
apache-2.0 | ['generated_from_keras_callback'] | false | javilonso/Mex_Rbta_Opinion_Augmented_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: 0.0078 - Validation Loss: 0.0606 - Epoch: 2 | f7570ec6fa88930d9a965217c1d7a883 |
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': 11565, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 20ed4239379a63e1a9d5eca7a118afa2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1193 | 0.0700 | 0 | | 0.0317 | 0.0572 | 1 | | 0.0078 | 0.0606 | 2 | | 08da100cafc7c741d693012bb6880046 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5436 - Wer: 0.3401 | 2772653d969ce0bd0f05784e90c11034 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5276 | 1.0 | 500 | 1.9983 | 1.0066 | | 0.8606 | 2.01 | 1000 | 0.5323 | 0.5220 | | 0.4339 | 3.01 | 1500 | 0.4697 | 0.451... | 367fd6cd7a8210ccc6a0857fdfca9fcb |
mit | ['timelms', 'twitter'] | false | Twitter December 2020 (RoBERTa-base, 107M) This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020. More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829). Below, we provide some usage examples using the standard Transformers interface... | ffd9b61b17c563a7ae5ef589dae49030 |
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