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 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0 | 1.0 | 3068 | nan | 0.3545 | | 0.0 | 2.0 | 6136 | nan | 0.3545 | | 0.0 | 3.0 | 9204 | nan ... | 32ada6fb381514e723c3c75093885bf5 |
mit | ['af', 'fill-mask', 'pytorch', 'roberta', 'masked-lm'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("jannesg/takalane_afr_roberta") model = AutoModelWithLMHead.from_pretrained("jannesg/takalane_afr_roberta") ``` | fbd1b6b936f87707c15e77a7c49b03e8 |
apache-2.0 | ['generated_from_keras_callback'] | false | eliwill/stoic-generator-distil-gpt2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.3439 - Validation Loss: 3.7738 - Epoch: 19 | fe93ebd270ee145ee08c1a312f47412b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.2818 | 3.9629 | 0 | | 4.0906 | 3.9052 | 1 | | 3.9946 | 3.8684 | 2 | | 3.9239 | 3.8412 | 3 | | 3.8689 | 3.8316 | 4 | | 3.8185 |... | b231cfdb4df1e3c7e504521a1005ebb4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-greek-speech-emotion-recognition This model is a fine-tuned version of [lighteternal/wav2vec2-large-xlsr-53-greek](https://huggingface.co/lighteternal/wav2vec2-large-xlsr-53-greek) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7699 - Accuracy: 0.8168 | 29268fc19e03eb79082b763ccb02b324 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 3a6245ecc9ac50f310292e5311ac69be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5594 | 0.22 | 100 | 0.7689 | 0.7649 | | 0.4341 | 0.44 | 200 | 0.6557 | 0.8045 | | 0.2925 | 0.66 | 300 | 0.7060 | 0.... | fa9e128a9f9d0693e78fc6eeb00a2612 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-korean-demo-with-LM 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: 0.3015 - Wer: 0.2113 | e0c2970fe2240d1523b6144f76534ac2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.7496 | 1.08 | 400 | 3.1801 | 1.0 | | 1.4505 | 2.16 | 800 | 0.5090 | 0.5659 | | 0.566 | 3.23 | 1200 | 0.3600 | 0.403... | 00a748d69f2f7f3e6ce5710c7ec0789b |
apache-2.0 | ['translation'] | false | jpn-ara * source group: Japanese * target group: Arabic * OPUS readme: [jpn-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-ara/README.md) * model: transformer-align * source language(s): jpn_Hani jpn_Hira jpn_Kana * target language(s): acm apc ara arq arz * model: transformer-align... | eb05a7f5d4cdeddc22597440dc4d7f1e |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-ara - source_languages: jpn - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'ar'] - src_constituents: {'jpn_Hang', 'jpn', ... | e473f17ea7388789f4a3c013a810fc43 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_data_aug_rte_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.5485 - Accuracy: 0.5199 | cd34d34d6437eb1bf2badd52dd4d98c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.348 | 1.0 | 568 | 0.5499 | 0.4874 | | 0.2888 | 2.0 | 1136 | 0.5640 | 0.4982 | | 0.2849 | 3.0 | 1704 | 0.5618 | 0.... | 56127115d11fbd28a7eff2d4c616218d |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/bart-base-squadshifts-vanilla-nyt-qg` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https:... | 2a2e11f40ca9df97c77aacdaeb616fe0 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.co... | 1057d3c40ea2c831690f86fd70f42394 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/bart-base-squadsh... | ac4c9029c9b70973850a5f9bda074956 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/bart-base-squadshifts-vanilla-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | bdce63990713bcf1933f4fa06b93fc70 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 32 - epoch: 6 ... | f41fa63280906b2f3a0b34e29c403edc |
mit | [] | false | kaneoya sachiko on Stable Diffusion This is the `<Kaneoya>` 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 al... | c01903de26bce108d3cb786d71b62501 |
mit | ['generated_from_trainer'] | false | DeBERTa-v3-small fine-tuned on QNLI This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2143 - Accuracy: 0.9151 | c96d12d564d097ff1ff0473b9b4e8dcc |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 721e75f838e3bdd6d8893ee49af37480 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2823 | 1.0 | 6547 | 0.2143 | 0.9151 | | 0.1996 | 2.0 | 13094 | 0.2760 | 0.9103 | | 0.1327 | 3.0 | 19641 | 0.3293 ... | 82cd2156128752cfad70a31d00cdc904 |
apache-2.0 | ['translation'] | false | opus-mt-es-yua * source languages: es * target languages: yua * OPUS readme: [es-yua](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-yua/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | f03a006eff99195f097b4ce03a679cd7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/sentence-t5-xxl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks.
This model was converted from ... | ef9956e61e988c70ebb4dca75feb3d0f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | 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 = ["This is an... | 4d5c2315fbaf612f411db3bb414aadd9 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-xxl)
| 1f8b856820df1a4abc024e716c6ab0a9 |
mit | [] | false | model by joetoe This your the Stable Diffusion model fine-tuned the kamenridergeats concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of kamenridergeats** You can also train your own concepts and upload them to the library by using [this notebook](https:/... | 8b332bc10220ad23fa9c8c2d6209345d |
mit | ['russian', 'paraphrasing', 'paraphraser', 'paraphrase'] | false | This is a paraphraser for Russian sentences described [in this Habr post](https://habr.com/ru/post/564916/). It is recommended to use the model with the `encoder_no_repeat_ngram_size` argument: ``` from transformers import T5ForConditionalGeneration, T5Tokenizer MODEL_NAME = 'cointegrated/rut5-base-paraphraser' mode... | 71d277e5a4998221bdfd5b0484f1d529 |
mit | [] | false | shrunken head on Stable Diffusion This is the `<shrunken-head>` 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 ca... | 9db892d5c2e167eac07a8a1943db2767 |
openrail | [] | false | <span style="color:blue">FishNet: AI For Fish Stock Estimation</span> The attached model was trained on a 63000 dataset of fish images belonging to 163 species. First, we trained a detectron2 model to detect and segment fish and fiduciary markers on a board. The detectron2 model was written in PyTorch, and the final... | 9cc8026443598de9299bb8971262aca2 |
apache-2.0 | ['multiberts', 'multiberts-seed_8'] | false | MultiBERTs - Seed 8 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variation... | 7e54830655184fdf223f4b51ac93d65c |
apache-2.0 | ['multiberts', 'multiberts-seed_8'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_8') model = TFBertModel.from_pretrained("google/multiberts-seed_8... | 61cfaf102be2766e6d69b5094a4f0ee6 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Es - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Multilingual LibriSpeech dataset. It achieves the following results on the evaluation set: - Loss: 0.0969 - Wer: 4.0193 | 09b8b3e0944416b9722e37df7b62c238 |
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: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 5b454981fa95a66172b165a11cec4ece |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1438 | 0.2 | 1000 | 0.1414 | 6.4317 | | 0.1294 | 0.4 | 2000 | 0.1139 | 4.7176 | | 0.2289 | 0.6 | 3000 | 0.1048 | 4.3266 | |... | de5dca28a5e5eb62e38f7b185f8480f1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 403 | 3.2112 | | 3.3686 | 2.0 | 806 | 3.1667 | | 3.2407 | 3.0 | 1209 | 3.1426 | | 3.1842 | 4.0 | 1612 | 3.1277 ... | daaaa8a66f65cdb4d90e2b742068d64d |
apache-2.0 | ['generated_from_trainer'] | false | Graphcore/bert-large-uncased-squad Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Gr... | c07435ebba260767847ba71817fc43f3 |
mit | [] | false | rcrumb portraits style on Stable Diffusion This is the `<rcrumb-portraits>` 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) note... | 75a1d7410f0510379bd0719633146c51 |
apache-2.0 | ['classification', 'zero-shot'] | false | Erlangshen-UniMC-DeBERTa-v2-110M-Chinese - Main Page:[Fengshenbang](https://fengshenbang-lm.com/) - Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc/) - Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/) - API: [Fengshen-OpenAPI](https://fengshe... | 396a8b88d0fd9cf8ff9ed72ab722fc31 |
apache-2.0 | ['classification', 'zero-shot'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | DeBERTa-v2 | 110M | Chinese | | db8875672b1a2569b84eb7126c5ce81d |
apache-2.0 | ['classification', 'zero-shot'] | false | 使用 Usage ```shell git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git cd Fengshenbang-LM pip install --editable . ``` ```python3 import argparse from fengshen.pipelines.multiplechoice import UniMCPipelines total_parser = argparse.ArgumentParser("TASK NAME") total_parser = UniMCPipelines.piplines_args(total_... | 448059cecaf1e52ca31b42a01a1eae9e |
mit | ['vision'] | false | GIT (GenerativeImage2Text), base-sized, fine-tuned on VQAv2 GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on VQAv2. It was introduced in the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Wang et al. and first released in [... | 065131819035ddfaaa2391ac857f522b |
mit | ['vision'] | false | Training data From the paper: > We collect 0.8B image-text pairs for pre-training, which include COCO (Lin et al., 2014), Conceptual Captions (CC3M) (Sharma et al., 2018), SBU (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2016), Conceptual Captions (CC12M) (Changpinyo et al., 2021), ALT200M (Hu et al.,... | 33d6c3d3c8f008a27a911467ac6174cb |
apache-2.0 | ['generated_from_trainer'] | false | Xegho.30.4 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1814 - Bleu: 87.4768 | 947fad721a37b89665d91058b11e974b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 121 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 | 819f5348ff7d5533d14e09852bae5753 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.19 | 100 | 1.2331 | 23.9598 | | No log | 2.38 | 200 | 0.7943 | 39.0191 | | No log | 3.57 | 300 | 0.5889 | 42.081... | e7ec44fa8e2f214fdb47d8746d28d230 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst-2-english-zero-shot 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 an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 5.2284 ... | 13f1be8efefa2d0886544699baabf66e |
apache-2.0 | ['distilroberta-base'] | false | Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-distilroberta-base') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is drivi... | 0d2e353286e6f39b343d2cbcdb17f8c4 |
apache-2.0 | ['distilroberta-base'] | false | Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-distil... | 035b4ba3f3a31ae7c190cda804e6a14e |
apache-2.0 | ['distilroberta-base'] | false | Zero-Shot Classification This model can also be used for zero-shot-classification: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-distilroberta-base') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", ... | 8b7d64bc262595fa308b05044f495013 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013CKPlus-7e-05 This model is a fine-tuned version of [lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05](https://huggingface.co/lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05) on the image_folder dataset. It achieves the following resul... | e0e08b5775b8046a63048db9a9530f08 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 16 - eval_batch_size: 16 - 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_sch... | f15820706ed1a2b7633fe716d83349d5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9364 | 0.97 | 27 | 0.1873 | 0.9645 | | 0.3365 | 1.97 | 54 | 0.0951 | 0.9848 | | 0.2482 | 2.97 | 81 | 0.0562 | 0.... | 29d5e4423dcd4e7e499838c0fb69a822 |
cc-by-4.0 | ['Clinical notes', 'Discharge summaries', 'longformer'] | false | * Continue pre-training RoBERTa-base using discharge summaries from MIMIC-III datasets. * Details can be found in the following paper > Xiang Dai and Ilias Chalkidis and Sune Darkner and Desmond Elliott. 2022. Revisiting Transformer-based Models for Long Document Classification. (https://arxiv.org/abs/2204.06683) *... | 85f6c72bba14f4915f4ca667f74ab6d8 |
mit | ['generated_from_keras_callback'] | false | chanifrusydi/indobert-finetuned-ner This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1190 - Validation Loss: 0.1903 - Epoch: 2 | cf273d75e89b4355bdfd8b066265ef0b |
mit | ['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': 312, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | 28ec74d9b00d555c987b260b8d0e4b3b |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.4312 | 0.2224 | 0 | | 0.1706 | 0.1935 | 1 | | 0.1190 | 0.1903 | 2 | | 6f32a4598ccec2119243b3cdc8082681 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | d4673dcf898ffb20e9fa171354b721ba |
other | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Japanese Elite This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Elite35P-Server/EliteVoiceProject youtube dataset. It achieves the following results on the evaluation set: - Loss: 1.1596 - Wer: 31.5364 | 72dcb05058c602bcaa0b815a953a8365 |
other | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: constant_with_warmup - lr_scheduler_warmup_steps: 100 - training_steps: 10000 ... | 2e9005297f6f0c66760e4f4f649de3a3 |
other | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.0003 | 52.0 | 1000 | 0.8053 | 28.8410 | | 0.0 | 105.0 | 2000 | 0.8636 | 28.5714 | | 0.0 | 157.0 | 3000 | 0.9056 | 2... | 3492b63884f2322fd6981aa4d14cb16c |
mit | ['generated_from_trainer'] | false | CharlesDeGaulle-GPT This model is a fine-tuned version of [antoinelouis/belgpt2](https://huggingface.co/antoinelouis/belgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5619 | e7752bb11dcd7917a906db1ab510ff5c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 26 | 2.9939 | | No log | 2.0 | 52 | 2.7641 | | No log | 3.0 | 78 | 2.6621 | | No log | 4.0 | 104 | 2.6129 ... | af122f59c5b5338bbebb3a47b89803e7 |
apache-2.0 | ['translation'] | false | opus-mt-sv-pis * source languages: sv * target languages: pis * OPUS readme: [sv-pis](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-pis/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 697caadcf3a439fac89decb25a3f5144 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-ner This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0710 - Precision: 0.8924 - Recall: 0.9143 - F1: 0.9032 - Accuracy: 0.9787 | 1cfcd5794c888d4b157542544c256824 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 20 - optimizer: Adam with betas=(0.9,0.999) and... | 91fc80d80a4d0ffe0303ebede26eba79 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1109 | 1.0 | 219 | 0.0930 | 0.8663 | 0.8872 | 0.8766 | 0.9738 | | 0.1284 | 2.0 |... | 91d2601a2de7b5d0a17295644840a5c4 |
creativeml-openrail-m | ['text-to-image', 'v2.0', 'Embedding'] | false | Textual Inversion Embedding by ConflictX For SD 2.0 trained on 768x768 images from midjourney. Install by downloading the step embedding, and put it in the \embeddings folder Makes a cutaway from homes, structures, and with weighting some weirder stuff as well. Use keyword: CutAway Use Negative: "Isometric" for so... | 0f334a0b83ea0be16131f2b4e741a516 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-fce This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5307 | 35e83f241ba767537ca8c7d0865ebdbf |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9048 | 1.0 | 122 | 1.6691 | | 1.6505 | 2.0 | 244 | 1.5172 | | 1.5615 | 3.0 | 366 | 1.5019 | | d299e750b3a432e78a33b9d1154be2c1 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/libritts_tts_train_gst+xvector_conformer_fastspeech2_transformer_teacher_raw_phn_tacotron_g2p_en_no_space_train.loss` ♻️ Imported from https://zenodo.org/record/4418774/ This model was trained by kan-bayashi using libritts/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 8323801e6c065e593586b05ee95fc980 |
mit | ['generated_from_trainer'] | false | bert-base-german-cased-finetuned-subj_v1 This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1594 - Precision: 0.1875 - Recall: 0.0077 - F1: 0.0147 - Accuracy: 0.9508 | e9984b75b7fc3e5d30ecd3fe9d4246a1 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 136 | 0.1591 | 1.0 | 0.0051 | 0.0102 | 0.9523 | | No log | 2.0 |... | d7a9441f0f0d85cc484b39db949767da |
apache-2.0 | ['translation'] | false | opus-mt-de-ig * source languages: de * target languages: ig * OPUS readme: [de-ig](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-ig/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | ab4375940277fcbafeb86266f7cdd27c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Greek Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. | e3d6ae48d4a0b5e0d3d763838b3112c3 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice When using this model, make sure that your speech input is sampled at 16kHz. | ab5a34ff95364e22721efe872d482ac8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "el", split="test[:2%]") processor = Wav2Vec2Processor.from_pr... | 9b19b67b4126474be5649ee24543206e |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Greek test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "el", split="test") wer... | 695bb6114e93b41754fdfefc79a1e08a |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset =... | 1f48263c5ca5654c88e4efc614036643 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(log... | 26039a1771f6b594f3822346c569191c |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | whisper-NST2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NBAILAB/NST - NO-CLOSE dataset. It achieves the following results on the evaluation set: - Loss: 0.2990 - Wer: 7.7537 | 0c6f664e2c09f30efc1a10ae2746c23e |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 96 - eval_batch_size: 16 - 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: 10000 - mixed_preci... | 30dae11044ac1864aa1252b9bd2b9657 |
apache-2.0 | ['hf-asr-leaderboard', 'automatic-speech-recognition', 'NbAiLab/NST', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1846 | 0.1 | 1000 | 0.3460 | 14.9373 | | 0.1325 | 0.2 | 2000 | 0.3413 | 11.4025 | | 0.1135 | 0.3 | 3000 | 0.3428 | 1... | 2e7ffa3dee5edf63a80460c70d1babb7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6182 - Matthews Correlation: 0.0 | ff2494dbe634be20736d69580edcb901 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.6218 | 1.0 | 34 | 0.6182 | 0.0 | | 0.611 | 2.0 | 68 | 0.6194 | 0.0 | | 0.6... | 1c2544668313e5cc5215079746f6ff72 |
other | [] | false | "Stable-Diffusion compatible model trained on Naganohara Mio artwork on top of Waifu Diffusion for 24 Epochs" Codename: mio-wd-v1-e24-ex-ad Codename extended: Mio trained on top of WaifuDiffusion, Version 1, Epoch 24 Experimental, Additional package Information: conlaboro.xyz or https://discord.gg/PKmSuwTxQx Date: ... | e1ffe3582861c82b6e40c1ee4fab864f |
other | [] | false | 2171@discord.com / Chavinlo Dataset: Extracted from Danbooru on 23/09/2022 10PM GMT-5 / Exact replica available at data.conlaboro.xyz/datasets/ (soon) Training Method: Native Training, with Waifu Diffusion repository Inside the samples folder there are some generated samples (duh) This model cannot be used in any S... | ac4dac9ede1163a482fd36ff423a30b8 |
apache-2.0 | [] | false | Datasets used for training: - spanish [PAWS-X](https://huggingface.co/datasets/paws-x) - Custom database: "Poor-man's" translation of [duplicated questions in Quora](https://huggingface.co/datasets/quora) (translated with [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es)) | 2a1ef98ca6ff664bee0c9d5a0645e049 |
apache-2.0 | ['audio-classification', 'speechbrain', 'Emotion', 'Recognition', 'wav2vec2', 'pytorch'] | false | Emotion Recognition with wav2vec2 base on IEMOCAP This repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about [SpeechBrain](https://sp... | 6ce989dcfe46a9d881205489ae43d56e |
apache-2.0 | ['audio-classification', 'speechbrain', 'Emotion', 'Recognition', 'wav2vec2', 'pytorch'] | false | Pipeline description This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speak... | 8ce01a331753fbd19136f4dda200985b |
apache-2.0 | ['audio-classification', 'speechbrain', 'Emotion', 'Recognition', 'wav2vec2', 'pytorch'] | false | Install SpeechBrain First of all, please install the **development** version of SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | 2006a7fc8d2d131bce498467d65ed0aa |
apache-2.0 | ['audio-classification', 'speechbrain', 'Emotion', 'Recognition', 'wav2vec2', 'pytorch'] | false | Perform Emotion recognition An external `py_module_file=custom.py` is used as an external Predictor class into this HF repos. We use `foreign_class` function from `speechbrain.pretrained.interfaces` that allow you to load you custom model. ```python from speechbrain.pretrained.interfaces import foreign_class classi... | 844f3972e9af3c86e2268fe6497b5e23 |
apache-2.0 | ['audio-classification', 'speechbrain', 'Emotion', 'Recognition', 'wav2vec2', 'pytorch'] | false | Training The model was trained with SpeechBrain (aa018540). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/IEMO... | cb6c89513f35e7b272ee9a166be80bea |
apache-2.0 | ['vision'] | false | ImageGPT (small-sized model) ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first release... | d5a2b41f7db0f08fb0db721ce6297a85 |
apache-2.0 | ['vision'] | false | Model description The ImageGPT (iGPT) is a transformer decoder model (GPT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 32x32 pixels. The goal for the model is simply to predict the next pixel value, given the previous ones. By pre-training t... | 8c36663176e7eee1c245a8ecd057abd3 |
apache-2.0 | ['vision'] | false | Intended uses & limitations You can use the raw model for either feature extractor or (un) conditional image generation. See the [model hub](https://huggingface.co/models?search=openai/imagegpt) to all ImageGPT variants. | 58ff721d714b2902f892b0945c133058 |
apache-2.0 | ['vision'] | false | How to use Here is how to use this model in PyTorch to perform unconditional image generation: ```python from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling import torch import matplotlib.pyplot as plt import numpy as np feature_extractor = ImageGPTFeatureExtractor.from_pretrained('ope... | 44d5ccef30a11cb062957bd55390ecef |
apache-2.0 | ['vision'] | false | initialize with SOS token context = torch.tensor(context).to(device) output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40) clusters = feature_extractor.clusters n_px = feature_extractor.size samples = output[:,1:].cpu().detach().numpy() sampl... | 9cab96e628fa442cc011533c1ddf559c |
apache-2.0 | ['vision'] | false | Preprocessing Images are first resized/rescaled to the same resolution (32x32) and normalized across the RGB channels. Next, color-clustering is performed. This means that every pixel is turned into one of 512 possible cluster values. This way, one ends up with a sequence of 32x32 = 1024 pixel values, rather than 32x... | 0e20a10a006d610adf53687fa3098749 |
apache-2.0 | ['vision'] | false | BibTeX entry and citation info ```bibtex @InProceedings{pmlr-v119-chen20s, title = {Generative Pretraining From Pixels}, author = {Chen, Mark and Radford, Alec and Child, Rewon and Wu, Jeffrey and Jun, Heewoo and Luan, David and Sutskever, Ilya}, booktitle = {Proceedings of the 37th International Conf... | e627320912c2956de55a65b3a514b601 |
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