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 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | No log | 1.0 | 439 | 0.0547 | 0.9251 | 0.9291 | 0.9271 | | 0.1451 | 2.0 | 878 | 0.0531 | 0.9315 ... | 246f0784082d1a6b27527ee51a2b7ce9 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qqp-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: 5.2614 | bbf5feae0f163fb2234eba9aa9d59cbe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.3866 | 0.4 | 500 | 6.2514 | | 6.1051 | 0.8 | 1000 | 5.9205 | | 5.7552 | 1.2 | 1500 | 5.7346 | | 5.5838 | 1.6 | 2000 | 5.6074 ... | e9da20bdec6d8ccfa6c8d6fee7a6a4fb |
agpl-3.0 | ['generated_from_trainer'] | false | XLMR-ENIS-finetuned-ner-finetuned-conll_ner This model is a fine-tuned version of [vesteinn/XLMR-ENIS-finetuned-ner](https://huggingface.co/vesteinn/XLMR-ENIS-finetuned-ner) on the mim_gold_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.0770 - Precision: 0.8720 - Recall: 0.8430 - F1: ... | 309e20b58b6f7748bfd8d58f63c7d6f8 |
agpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0461 | 1.0 | 2904 | 0.0647 | 0.8588 | 0.8107 | 0.8341 | 0.9842 | | 0.0244 | 2.0 |... | fc638687665fd37fd18108bc2e551784 |
apache-2.0 | ['generated_from_trainer'] | false | roberta-base-bne-finetuned-recores-long 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: - Loss: 2.2599 - Accuracy: 0.4525 | 8bad81f9a64844ad4aef5a69fc1f4d99 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 2da9ad1f4e321a31cb9ad73573b5d5ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5728 | 1.0 | 653 | 1.4938 | 0.3846 | | 0.9036 | 2.0 | 1306 | 1.9815 | 0.4615 | | 0.4161 | 3.0 | 1959 | 2.2599 | 0.... | 0afba99ea1e2e40cbb66a2e2048ca89c |
apache-2.0 | ['generated_from_trainer'] | false | model-960hfacebook-2022.06.08 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2907 - Wer: 0.1804 | f35cce89e5c3c89db3e7b117e19d4031 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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... | 6ce2f2b9e8b1cfbf883f356974450e3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.7634 | 0.21 | 300 | 2.9743 | 0.9998 | | 1.6536 | 0.43 | 600 | 0.8605 | 0.7529 | | 0.9823 | 0.64 | 900 | 0.6600 | 0.628... | 3d54d8710a052193a87d177dcbc75e9c |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on Cityscapes instance segmentation (base-IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresea... | 2175cce3e900098d0bc9c61232be34c7 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on Cityscapes instance segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-cityscapes-instance") url = "http://images.cocoda... | 59523c45402356fae38018ae2ded1052 |
openrail | [] | false | <h1>ITRobo2022 model. Trained on SD 1.5.</h1>  <br> I really like the Robo-Diffusion model (https://huggingface.co/nousr/robo-diffusion), but most of what you can get with it is robot heads. :)<br> In my model I ... | fa169ae484c87871dd4c2a0a335ad8e0 |
openrail | [] | false | Wholesale, Abstract Metal Sculpture. i'm leaving a bad review.</i><br>  Best results on:<br> DDIM<br> steps:20<br> CFG scale 7<br> 512x512 . The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/hugg... | a46d96136508ca5cedf76f43579785f6 |
bsd-3-clause | [] | false | Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models a... | 84c4e1fcbcffc837f0d2fbeda61303da |
bsd-3-clause | [] | false | Training data This checkpoint (CodeGen-Multi 350M) was firstly initialized with *CodeGen-NL 350M*, and then pre-trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github-repos), a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B... | d6ddecb8620cd2023e69e12cd9926bb9 |
bsd-3-clause | [] | false | How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi") t... | 1158c0823fa4208b75a96c90c12f6126 |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-mnli 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: 0.6020 - Accuracy: 0.7618 | 9e7ec084504e6eda099390178a3ec79a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9249 | 0.04 | 500 | 0.8197 | 0.6419 | | 0.8154 | 0.08 | 1000 | 0.7776 | 0.6651 | | 0.7747 | 0.12 | 1500 | 0.7455 ... | 7927e75cfadcae4e6980617e164cb94d |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-10_sixties-0_s632 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | 2d341ebda2f35281961089fd19356396 |
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: 0.2140 - Accuracy: 0.924 - F1: 0.9241 | 334e83156a9dbe9bcc2a066fa8b16a0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8278 | 1.0 | 250 | 0.3099 | 0.9055 | 0.9032 | | 0.251 | 2.0 | 500 | 0.2140 | 0.924 | 0.9241 | | 6140305eeb07382f29e650893186690e |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide... | fadd069ba67a0ff12af338627091450a |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-1B-deduped has not been fine-tuned fo... | 532aec5885695623d6b9ddd4307eddee |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-1B-deduped to produce factually accurate output. This model was tr... | 6d9860d458f97f40a029d1e2c05721d3 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data Pythia-1B-deduped was trained on the Pile **after the dataset has been globally deduplicated**. [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse source... | 7b8e5d30d150cf461ab4fab9596494b1 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_sst2 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.7778 - Accuracy: 0.8016 | bec270b7ca5b4da775422ade3396c33c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5405 | 1.0 | 527 | 1.4225 | 0.5539 | | 1.3567 | 2.0 | 1054 | 1.4707 | 0.5482 | | 1.2859 | 3.0 | 1581 | 1.4661 | 0.... | 7f61167f737eb7848c8aeaa78b8ee08e |
cc-by-sa-4.0 | ['erzya', 'mordovian', 'fill-mask', 'pretraining', 'embeddings', 'masked-lm', 'feature-extraction', 'sentence-similarity'] | false | This is an Erzya (`myv`, cyrillic script) sentence encoder from the paper [The first neural machine translation system for the Erzya language](https://arxiv.org/abs/2209.09368). It is based on [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) ([license here](https://tfhub.dev/google/La... | 02e57e4eed1bd57427246dc8991782fb |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ncbi_disease dataset. It achieves the following results on the evaluation set: - Loss: 0.0591 | 342fc15abedd4a8d030b93918e4ec87e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1127 | 1.0 | 680 | 0.0593 | | 0.0442 | 2.0 | 1360 | 0.0557 | | 0.0181 | 3.0 | 2040 | 0.0591 | | 8090b355275248a759a680ee17144230 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | whisper_malayalam_largev2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2913 - Wer: 41.6986 | b1468f0d0ea10f64ee01b784624e16ec |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precisio... | 5eb0bd58c20528dfe4b424da4d745107 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0769 | 2.07 | 1000 | 0.3657 | 53.8314 | | 0.0089 | 4.14 | 2000 | 0.2913 | 41.6986 | | 24e9ce2cf8e8ef092809f07cbf4da61d |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-base_small 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 imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3962 - Accuracy: 0.8942 - F1: 0.9441 | 06472578c1024e9ab1eefdce6e90b3c3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.3459 | 0.64 | 500 | 0.1283 | 0.9505 | 0.9746 | | 0.2389 | 1.28 | 1000 | 0.1054 | 0.9595 | 0.9793 | | 0.1927 |... | 246003d7c888570f6069310ef718484d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_mnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5167 - Accuracy: 0.6142 | 5b9d791438f340c7311c2b2663f39b04 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6009 | 1.0 | 1534 | 0.5752 | 0.4973 | | 0.5601 | 2.0 | 3068 | 0.5468 | 0.5395 | | 0.5323 | 3.0 | 4602 | 0.5259 ... | f3791adfe2dc22f18295e95cae2ff142 |
afl-3.0 | ['generated_from_trainer'] | false | covid-twitter-bert-v2-struth This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on the [COVID-19 Fake News Dataset NLP by Elvin Aghammadzada](https://www.kaggle.com/datasets/elvinagammed/covid19-fake-news-dataset-nl... | 453c88343e1e3d9a7b8133a59c72764b |
afl-3.0 | ['generated_from_trainer'] | false | Model description This model is built on the work on Digital Epidemiology Lab and their COVID Twitter BERT model. We have extended their model by training it for Sequence Classification tasks. This is part of a wider project for True/Fake news by the [Struth Social Team](https://github.com/Struth-Social-UNSW/ITProjec... | 8972f83e5da73b4419b0371126bc76cd |
afl-3.0 | ['generated_from_trainer'] | false | Intended uses & limitations This model is intended to be used for the classification of Tweets as either true or fake (0 or 1). The model can also be used for relatively complex statements regarding COVID-19. A known limitation of this model is basic statements (e.g. COVID is a hoax) as the Tweets used to train the ... | a0d89438549af182fe8175e3e88f7a79 |
afl-3.0 | ['generated_from_trainer'] | false | Training and evaluation data Training and Testing data was split 80:20 for the results listed above. Training/Testing Set: - Samples Total: 8437 - Samples Train: 6749 - Samples Test: 1687 Evaluation Set: - Samples Total: 100 | 148dc293e6e9a2723682f22cd87ef204 |
afl-3.0 | ['generated_from_trainer'] | false | Training procedure 1. Data is preprocessed through custom scripts 2. Data is passed to the model training script 3. Training is conducted 4. Best model is retrieved at end of training and uploaded to the Hub | b9cef25e3c4e06a8c5903272e916ea9f |
afl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.1719 | 1.0 | 422 | 0.1171 | 0.9662 | 0.9813 | 0.9493 | 0.9650 | | 0.0565 | 2.0 |... | a0b1cbba4e66f08aa78d557c27df4277 |
mit | [] | false | model by 4ff3nbr0t This your the Stable Diffusion model fine-tuned the Sneaker concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks sneaker** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.re... | 56711f282029282aeff3abe2d057a1e5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Latvian - 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 lv dataset. It achieves the following results on the evaluation set: - Loss: 0.5621 - Wer: 33.1120 | 2a7e711c845465c8e426a4e5edeb1753 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0219 | 16.67 | 1000 | 0.6129 | 41.1575 | | 0.0029 | 33.33 | 2000 | 0.5975 | 36.1480 | | 0.0003 | 50.0 | 3000 | 0.5626 | 33.681... | 244d6f975ac4be5a827aae71dc7986fb |
cc-by-4.0 | [] | false | HindTweetBERT-Hateful A HindBERT (l3cube-pune/hindi-bert-v2) model finetuned on Hateful Hindi Tweets.<br> More details on the dataset, models, and baseline results can be found in our [paper] (<a href='https://arxiv.org/abs/2210.04267'> link </a>)<br> ``` @article{gokhale2022spread, title={Spread Love Not Hate: Unde... | e2dff092c8873dd688c1786a13c6a4f2 |
apache-2.0 | ['generated_from_trainer'] | false | qqp This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.3695 - Accuracy: 0.9050 - F1: 0.8723 - Combined Score: 0.8886 | 8e4a08e6309e38c71b38e0af6d754fc6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | da69f7c979807f89ef91dbe59ea5f1c4 |
mit | [] | false | Description A pre-trained model for clinical decision support, for more details, please see https://github.com/NtaylorOX/Public_Prompt_Mimic_III A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes ([MIMIC-III](https://mimic.physionet.org/)). We try combining two domains that have... | e58384cef3efe50efbb7b9cac95f5343 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'efficient', 'wits'] | false | IT5 Cased Small Efficient EL32 for Wikipedia Summarization 📑 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) mod... | 444431e0af5307f4c159f2ed7f5bd83b |
apache-2.0 | ['italian', 'sequence-to-sequence', 'wikipedia', 'summarization', 'efficient', 'wits'] | 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 hg = pipeline("text2text-generation", model='it5/it5-efficient-small-el32-wiki-summarization') hg("Le dimensioni dell'isola sono di 8 km... | b9c14654528879c709a04fd9a4784d7f |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ar-evaluated-en-to-ar-4000instances-un_multi-leaningRate2e-05-batchSize8-11-action-1 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the un_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.1850 - Bleu:... | d66d5a5381d037d2f934526033605db0 |
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: 11 | ae9028572a7a29892b04de7f8dffa40d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 0.6999 | 0.25 | 100 | 0.1959 | 50.1492 | 0.508 | 25.2788 | | 0.1994 | 0.5 | 200 | 0.1931 | 51.003 | ... | ca886011660355ea18d350e2d463d06e |
apache-2.0 | ['translation', '文言文', 'ancient'] | false | From modern Chinese to Ancient Chinese > This model translate modern Chinese to Classical Chinese, so I guess who's interested in the problemset can speak at least modern Chinese, so... let me continue the documentation in Chinese * 从现代文到文言文的翻译器, 欢迎前往[github文言诗词项目页面:渊, 讨论&加⭐️ ](https://github.com/raynardj/yuan) * 还有... | d30088477c07fc83c1a456b2736dc49a |
apache-2.0 | ['translation', '文言文', 'ancient'] | false | 推荐的inference 通道 **注意**， 你必须将```generate```函数的```eos_token_id```设置为102就可以翻译出完整的语句， 不然翻译完了会有残留的语句(因为做熵的时候用pad标签=-100导致)。 目前huggingface 页面上compute按钮会有这个问题， 推荐使用以下代码来得到翻译结果🎻 ```python from transformers import ( EncoderDecoderModel, AutoTokenizer ) PRETRAINED = "raynardj/wenyanwen-chinese-translate-to-ancient" token... | 778295a5b7e895f127bd71fbe75f8ea0 |
apache-2.0 | ['translation', '文言文', 'ancient'] | false | 目前版本的案例 > 大家如果有好玩的调戏案例， 也欢迎反馈 ```python >>> inference('你连一百块都不肯给我') ['不 肯 与 我 百 钱 。'] ``` ```python >>> inference("他不能做长远的谋划") ['不 能 为 远 谋 。'] ``` ```python >>> inference("我们要干一番大事业") ['吾 属 当 举 大 事 。'] ``` ```python >>> inference("这感觉，已经不对，我努力，在挽回") ['此 之 谓 也 ， 已 不 可 矣 ， 我 勉 之 ， 以 回 之 。'] ``` ```python >>> infere... | 7b01aeb3b831df799fe6169231e1451e |
apache-2.0 | ['translation', '文言文', 'ancient'] | false | 其他文言诗词的资源 * [项目源代码 🌟, 欢迎+star提pr](https://github.com/raynardj/yuan) * [跨语种搜索 🔎](https://huggingface.co/raynardj/xlsearch-cross-lang-search-zh-vs-classicical-cn) * [现代文翻译古汉语的模型 ⛰](https://huggingface.co/raynardj/wenyanwen-chinese-translate-to-ancient) * [古汉语到现代文的翻译模型, 输入可以是未断句的句子 🚀](https://huggingface.co/raynardj/w... | ff0de007d81a0d95c9ccc21845e1f094 |
other | [] | false | Dryer Vent Cleaning Richardson TX https://carpetcleaning-richardson.com/dryer-vent-cleaning.html (972) 454-9815 Additionally, if your vents are clogged, we can assist you in preventing dryer fires.If your clothes get too hot in your dryer or if it is too hot, this means that the hot air vents are blocked.When we remov... | 979d92de20f9a3ce6b3c0bd81735f4ea |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Shinji Watanabe/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4630406/ This model was trained by Shinji Watanabe using gigaspeech/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 620fa4a222f9027ee01e674ab465d27e |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | t5-small-finetuned-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 3.1632 | 7efc9f04d75ae729397005bf460fed8b |
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: 0.2238 - Accuracy: 0.9285 - F1: 0.9285 | af824c44a614cd9aaba69fad50b762c5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8543 | 1.0 | 250 | 0.3398 | 0.899 | 0.8958 | | 0.267 | 2.0 | 500 | 0.2238 | 0.9285 | 0.9285 | | ceea2de0e0a78c9e635d1a6488bb498d |
bsd-3-clause | ['generated_from_trainer'] | false | data This model is a fine-tuned version of [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3658 | 93bfc82b6f91f00cc59845db2bb9bbbd |
bsd-3-clause | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 6 | 0.7794 | | No log | 2.0 | 12 | 0.4634 | | No log | 3.0 | 18 | 0.3658 | | 62c748416f82d268cbbcfc83dc043887 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 7.5773 - eval_wer: 99.3995 - eval_runtime: 561.7733 - eval_samples_per_sec... | af6a2cb35aea02aae74d5fa939d235c7 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 40 - mixed_precision_t... | 31bddb56b3fbba7711e8cf3785466041 |
openrail | [] | false | A cool dispersion effect hypernet. A sample prompt is: photo of beautiful woman standing, 8k,4k,highres,masterpiece,in the style of dispersion I have not tried it on non-human generations. It didn't work as well in a few genertions involving a cat. Simply copy the download the file into your hypernetworks folder, s... | 21a96efae00c7e05e3af221792a72e87 |
mit | [] | false | Goku on Stable Diffusion This is the `<goku>` 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 ... | ef3021aefd8d7483868c8e85cd91483b |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Tiny it 4 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7126 - Wer: 41.3547 | 1a0dbffa6c8cdc5483385bfeab21e0f5 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Model description This model is the openai whisper small transformer adapted for Italian audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting. The learning rate has been set to 5e-5 in the hyperparameter tuning process and it improved the performance on the evaluation set. | 5ca464007d3f67ed070b5061966a0aa6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precisi... | 762159922951d869106a0e36733f0ac4 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5919 | 0.95 | 1000 | 0.8049 | 56.4823 | | 0.3181 | 1.91 | 2000 | 0.7393 | 44.8142 | | 0.1417 | 2.86 | 3000 | 0.7067 | 42.748... | b6035db37ccfdcc187a9d7be974d4fb4 |
mit | ['generated_from_trainer'] | false | bert-base-portuguese-cased_harem-selective-CRF-first-ner This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset. It achieves the following results on the evaluation set: - Loss: 0.2045 - Precision: 0.5352 - Recal... | 54658940aa2afa8480a787e208128723 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.102 | 1.0 | 2517 | 0.2498 | 0.4367 | 0.3817 | 0.4073 | 0.9332 | | 0.0614 | 2.0 |... | 314d887bef0a7175075e69bbcf35d18e |
mit | [] | false | mate on Stable Diffusion This is the `<mate>` 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 ... | b65b9dba38ed8ca657323443b7ef89b2 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-sound2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5012 - Accuracy: 0.5357 | e4b1c3d1312c26a3b824631a420faacb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-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_sc... | 815329869ee513b752efc94575c5451d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 2.0762 | 0.0714 | | No log | 2.0 | 2 | 2.0638 | 0.1429 | | No log | 3.0 | 3 | 2.0387 | 0.... | 7fdaf7daf026a2695ce417010d79cb95 |
isc | ['flair', 'token-classification'] | false | What is YODA YODA is a series of models for Google Feed product optimization. We aim to increase the market reach for ecommerce by augmenting and improving certain metadata like short titles, colors, measures and more. YODA is being used in production by +300 companies with +3.5M products. | 966ea2b076aca45fdf3ca5971330ef19 |
isc | ['flair', 'token-classification'] | false | What we use NER for We have trained a NER model for product feature extraction. We retrieve data like colors, sizes, brands and energy labels. Trained with +3M lines of product metadata, the model returns the next scores: Results: - F-score (micro) 0.972 - F-score (macro) 0.9692 - Accuracy 0.9461 By class: | ... | 7a6f61831896db9dd3281d2f0ccdd423 |
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 data_set dataset. It achieves the following results on the evaluation set: - Loss: 0.3395 - Precision: 0.2081 - Recall: 0.1950 - F1: 0.2013 - Accuracy: 0.9194 | c8cdb803586d4f2c7d96a8f2df4d5f95 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 100 | 0.3796 | 0.125 | 0.0755 | 0.0941 | 0.9152 | | No log | 2.0 |... | 700bf90b937f75200b5e15d1087a57a0 |
apache-2.0 | ['automatic-speech-recognition', 'uk'] | false | exp_w2v2t_uk_vp-100k_s791 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (uk)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 247d2a8ec4cae198f16bc58e52c266b4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | d4dd5c1c2eca5e6b0c658606b2527a9e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 1 | nan | 33.8462 | 31.746 | 30.7692 | 30.7692 | 86.0 ... | 77381ac512190748661181684a77da7f |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Model Card of `lmqg/t5-large-squad-qg-ae` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation and answer extraction jointly on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-quest... | d857068335cfbcb3f3476b6adbaee37d |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-qg-ae") | b9f379208be1e8c2e036bcfce727dfdd |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-large-squad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-------... | 45bcebf083d9fbc5f84598d73b031506 |
cc-by-4.0 | ['question generation', 'answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: t5-large - max_length: 512 - max_len... | 535af4434fd04f5341c713752fcbfbb9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola-3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Matthews Correlation: 1.0 Label 0 : "AIMX" Label 1 : "OWNX" Label 2 ... | 54a4649a7d6ac0bd0a15fac9d03b8185 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 192 | 0.0060 | 1.0 | | No log | 2.0 | 384 | 0.0019 | 1.0 | | 0.0... | e52ea60adacf7806b417dceadbb22d68 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-dapt-lm-ai 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: | 9784859466a8402c03c8fe23a1a42129 |
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... | 69a83c32daff1aa859f509d4fae19ae4 |
apache-2.0 | ['exbert'] | false | How to use You can use this model directly with a pipeline for masked language modeling: In tf_transformers ```python from tf_transformers.models import BertModel from transformers import BertTokenizer tokenizer = BertTokenizer.from_pretrained('bert-base-cased') model = BertModel.from_pretrained("bert-base-cased") ... | 34a0d72836d13b4446bd5709e6e6de78 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_logit_kd_pretrain_mnli This model is a fine-tuned version of [gokuls/mobilebert_add_pre-training-complete](https://huggingface.co/gokuls/mobilebert_add_pre-training-complete) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.352... | 4c2793f8d44a642b3ff30c49c5d5a301 |
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