license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | berturk-keyword-extractor This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4306 - Precision: 0.6770 - Recall: 0.6899 - Accuracy: 0.9169 - F1: 0.6834 | 80fa58cc3d0e7e9eb277c02a5e749fb8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:| | 0.1845 | 1.0 | 1875 | 0.1964 | 0.6380 | 0.6743 | 0.9164 | 0.6557 | | 0.1338 | 2.0 ... | 5239ad0427c9c41731e961fa692e782f |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | hku-nlp/instructor-large This is a general embedding model: It maps **any** piece of text (e.g., a title, a sentence, a document, etc.) to a fixed-length vector in test time **without further training**. With instructions, the embeddings are **domain-specific** (e.g., specialized for science, finance, etc.) and **task... | 1161468f8f72d12e2432d2685fccd6fe |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Compute your customized embeddings Then you can use the model like this to calculate domain-specific and task-aware embeddings: ```python from sentence_transformers import SentenceTransformer sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments" instruction = "Represent the Science title; In... | ee6fc7da4798d67a9072b75ab3013ab1 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Calculate Sentence similarities You can further use the model to compute similarities between two groups of sentences, with **customized embeddings**. ```python from sklearn.metrics.pairwise import cosine_similarity sentences_a = [['Represent the Science sentence; Input: ','Parton energy loss in QCD matter',0], ... | cb2253e47821965b4413d2ebdcc339fe |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-timit-demo 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: 0.4847 - Wer: 0.3462 | 4c6e5d558a988ac29448d4843dcfa194 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.487 | 4.0 | 500 | 1.3466 | 1.0153 | | 0.6134 | 8.0 | 1000 | 0.4807 | 0.4538 | | 0.2214 | 12.0 | 1500 | 0.4684 | 0.3984 | |... | 95a9d2aeb020e57124b3992f6ad0fe86 |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Wav2Vec2-XLSR-300m-es This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the spanish common_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge. It achieves the following results on th... | 7256e6739eff941899b0a97561898d5e |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Cleaning characters def remove_extra_chars(batch): chars_to_ignore_regex = '[^a-záéíóúñ ]' text = batch["translation"][target_lang] batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower()) return batch | 6aca71bc7d77378037853bc052cf484c |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Preparing dataset def prepare_dataset(batch): audio = batch["audio"] batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"],return_tensors="pt",padding=True).input_values[0] with processor.as_target_processor(): batch["labels"] = processor(batch["sentence"]).inpu... | bb536e78e9dbeb705a44bf40018040f0 |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Testing first sample inputs = torch_tensor(common_voice_test[0]["input_values"]) with torch.no_grad(): logits = model(inputs).logits pred_ids = torch.argmax(logits, dim=-1) text = processor.batch_decode(logits.numpy()).text print(text) | 865b9cddc848311370b837ec542541a1 |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | 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... | b2e3b4b2340f86efdb5f1f8aa4053b3d |
apache-2.0 | ['common_voice_8_0', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.6747 | 0.3 | 400 | 0.6535 | 0.5926 | | 0.4439 | 0.6 | 800 | 0.3753 | 0.3193 | | 0.3291 | 0.9 | 1200 | 0.3267 | 0.2721 | |... | 3ca19d9890ad8208346e4b8f42227763 |
apache-2.0 | ['SEAD'] | false | SEAD-L-6_H-384_A-12-wnli This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **wnli** task. For weights initialization, we used [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) | 35234c325fb033da43a4ca220a3943f3 |
apache-2.0 | ['SEAD'] | false | Evaluation results | eval_accuracy | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples | |:-------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:| | 0.5775 | 1.2959 | 54.787 | 2.315 ... | 277e8fed674537729e74185388e10a41 |
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: 0.2983 - Accuracy: 0.87 - F1: 0.8696 | 91e11613a1854296ac000063828ca5d7 |
mit | ['spacy', 'token-classification'] | false | zh_core_web_md Chinese pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `zh_core_web_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ru... | b8a70ce6343f86d8462dacc2ab9bf7f8 |
mit | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (100 labels for 3 components)</summary> | Component | Labels | | --- | --- | | **`tagger`** | `AD`, `AS`, `BA`, `CC`, `CD`, `CS`, `DEC`, `DEG`, `DER`, `DEV`, `DT`, `ETC`, `FW`, `IJ`, `INF`, `JJ`, `LB`, `LC`, `M`, `MSP`, `NN`, `NR`, `NT`, `OD`, `ON`, `P`, `PN`, `PU`,... | 860bc45ec7e899746ec5da9724c0f4a0 |
mit | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 95.85 | | `TOKEN_P` | 94.58 | | `TOKEN_R` | 91.36 | | `TOKEN_F` | 92.94 | | `TAG_ACC` | 90.04 | | `SENTS_P` | 78.89 | | `SENTS_R` | 72.80 | | `SENTS_F` | 75.72 | | `DEP_UAS` | 70.50 | | `DEP_LAS` | 65.22 | | `ENTS_P` | 71.88 | | `ENTS_R` | 67.90 | | `ENTS_F` | 6... | a718a238b51cd9a3679c616c5ca0fcec |
apache-2.0 | ['translation'] | false | opus-mt-ceb-fi * source languages: ceb * target languages: fi * OPUS readme: [ceb-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ceb-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 1671e44010165cfddec4110842fb7816 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-sv_s320 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 1822efc957f6c1f5e177fe51c0afb54a |
apache-2.0 | ['tapas'] | false | reader-models). It is described in Herzig et al.'s (2021) [paper](https://aclanthology.org/2021.naacl-main.43/) _Open Domain Question Answering over Tables via Dense Retrieval_. This model has 2 versions that can be used differing only in the table scoring head. The default one has an adapted table scoring head in ord... | 04383a191667ec542c52580108cf086e |
apache-2.0 | ['tapas'] | false | In Haystack If you want to use this model for question-answering over tables, you can load it in [Haystack](https://github.com/deepset-ai/haystack/): ```python from haystack.nodes import TableReader table_reader = TableReader(model_name_or_path="deepset/tapas-large-nq-hn-reader") ``` | e107215393eb140780a5cf83b8f7b923 |
apache-2.0 | ['vision', 'image-classification'] | false | Swin Transformer v2 (large-sized model) Swin Transformer v2 model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at resolution 384x384. It was introduced in the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Liu et al. and first released in [this repos... | 4197774b4bb373529c401fc0efa3ced0 |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" i... | f119f6140c2f7e86e5a8afcc923e5192 |
apache-2.0 | [] | false | t511) includes the following improvements compared to the original T5 model- GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202). - Dropout was turned off in pre-training (quality win). Dropout should be re-enabled during fine-tuning. - Pre-trained on C4 only... | 91b408a9f91dff8385a6fc01f5e53ef9 |
apache-2.0 | [] | false | Keyphrase Boundary Infilling with Replacement (KBIR) The KBIR model as described in "Learning Rich Representations of Keyphrases from Text" from Findings of NAACL 2022 (https://aclanthology.org/2022.findings-naacl.67.pdf) builds on top of the RoBERTa architecture by adding an Infilling head and a Replacement Classific... | 1f639c552ea409e842bdf994036a4771 |
apache-2.0 | [] | false | Keyphrase Extraction ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bloomberg/KBIR") model = AutoModelForTokenClassification.from_pretrained("bloomberg/KBIR") from datasets import load_dataset dataset = load_dataset("midas/semeval2017_ke_tagged... | d4a876bd7a556b89dddcbb88ea26a734 |
apache-2.0 | [] | false | Named Entity Recognition ``` from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bloomberg/KBIR") model = AutoModelForTokenClassification.from_pretrained("bloomberg/KBIR") from datasets import load_dataset dataset = load_dataset("conll2003") ``` Report... | da16f2dc2df234ebf38c89d2058a2a8f |
apache-2.0 | [] | false | Question Answering ``` from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("bloomberg/KBIR") model = AutoModelForQuestionAnswering.from_pretrained("bloomberg/KBIR") from datasets import load_dataset dataset = load_dataset("squad") ``` Reported Results: | ... | 6815ad5049eb40806be76da695d1a94d |
apache-2.0 | [] | false | Any other classification task As mentioned above since KBIR is built on top of the RoBERTa architecture, it is compatible with any AutoModel setting that RoBERTa is also compatible with. We encourage you to try fine-tuning KBIR on different datasets and report the downstream results. | 481ce8fc07414e95306341abb66d92e9 |
apache-2.0 | [] | false | Citation Please cite this work using the following BibTeX entry: ``` @inproceedings{kulkarni-etal-2022-learning, title = "Learning Rich Representation of Keyphrases from Text", author = "Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi", booktitle = "Fin... | 79fb4b9ad4b7fe4d72a60dd4af8a5598 |
creativeml-openrail-m | [] | false | THE Model is forked from ClueAI's PromptClue for easily deployment only. Please visit ClueAI's space on huggingface.co. Thank you. <a href="https://colab.research.google.com/drive/1noyBA_JrYO6Lk6cwxsNZ_jdJ-Jtaf82G?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg"></a> PromptCLUE：全中文任务... | 0745ee84a9cfa46ac8e51294b0984b6b |
creativeml-openrail-m | [] | false | scrollTo=Nk2tSi3vnSN0'>Colab试用</a> 加载模型： ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ClueAI/PromptCLUE-base") model = AutoModelForSeq2SeqLM.from_pretrained("ClueAI/PromptCLUE-base") ``` 使用模型进行预测推理方法： ```python import torch | ab9a66a55491cd060a25d1bd84f3ad6d |
creativeml-openrail-m | [] | false | device = torch.device('cpu') device = torch.device('cuda') model.to(device) def preprocess(text): return text.replace("\n", "_") def postprocess(text): return text.replace("_", "\n") def answer(text, sample=False, top_p=0.6): '''sample：是否抽样。生成任务，可以设置为True; top_p：0-1之间，生成的内容越多样''' text = preprocess(text) e... | ef0cde8ad892ae9e2ec78ff3a6ea2450 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-xls-r-300m-italian 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_7_0 - IT dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.1710 | ffb648317d32c670b6c957085d062ac0 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5.0 - mixed_precision_t... | 50204828bc8fe2e7531a037ad83f820e |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.04 | 100 | inf | 1.0 | | No log | 0.09 | 200 | inf | 0.9983 | | No log | 0.13 | 300 | inf | 0.767... | ac8103d5eee8f5c4c455015b252669ee |
apache-2.0 | question answering | false | BatteryBERT-uncased for QA
**Language model:** batterybert-uncased
**Language:** English
**Downstream-task:** Extractive QA
**Training data:** SQuAD v1
**Eval data:** SQuAD v1
**Code:** See [example](https://github.com/ShuHuang/batterybert)
**Infrastructure**: 8x DGX A100
| 68074468a7a77b1ba855103bc4350053 |
apache-2.0 | question answering | false | a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'What is the electrolyte?',
'context': 'The typical non-aqueous electrolyte for commercial Li-ion cells is a solution of LiPF6 in linear and cyclic carbonates.'
}
res = nlp(QA_input)
| b6324fcd750c65af111675b8943d69f3 |
['apache-2.0'] | ['vision'] | false | Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M) (300 million images, 21,841 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transforme... | 7f2c015de2d253d8e4479486c6dba34f |
['apache-2.0'] | ['vision'] | false | Model description The Vision Transformer (ViT) is a transformer model pretrained on a large collection of images in a supervised fashion, namely [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M), at a resolution of 224x224 pixels. Images are presented to the model as... | f0d6a4b2ac2b6f95c441918d882efca2 |
['apache-2.0'] | ['vision'] | false | Intended uses & limitations You can use weights from ViT models for image classification, downstream. Codes for reproduction are also provided. Please see this [github repository](https://github.com/kakaobrain/coyo-vit) for pretraining and finetuning code. | 3b2c55197b8ba6506c41febec847c3ae |
['apache-2.0'] | ['vision'] | false | How to use Here is how to use this model in PyTorch: ```python WIP ``` Here is how to use this model in JAX/Flax: ```python WIP ``` Here is how to use this model in Tensorflow: ```python WIP ``` | 8c8c1c55ce3cb0dbb822e50ee43768b2 |
['apache-2.0'] | ['vision'] | false | Training data The ViT model was pretrained on [COYO-Labeled-300M](https://github.com/kakaobrain/coyo-dataset/tree/main/subset/COYO-Labeled-300M), a dataset consisting of 300 million images and 21k classes. | 4185e050835433f38d4c1853aa2f4b33 |
['apache-2.0'] | ['vision'] | false | Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/kakaobrain/coyo-vit). Images are inception-cropped to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). | 12b018125c36c11545d4845d4a36586c |
['apache-2.0'] | ['vision'] | false | Pretraining The model was trained on TPUv3 hardware. All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. Pre-training resolution is 224. More detail, please see [here](https://github.com/kakaobrain/coyo-vit) | 33c78c7fd556873f007469cc4e8285dd |
['apache-2.0'] | ['vision'] | false | Evaluation results | Model | Upstream Dataset | Resolution | ImageNet (downstream) | ImageNet-ReaL (dwonstream) | Public | |---------- |------------------- |------------ |----------------------- |---------------------------- |-------- | | ViT-L/16 | JFT-300M ... | d10b35b0c792961190bbbed29c516efc |
['apache-2.0'] | ['vision'] | false | Citation ```bibtex @misc{kakaobrain2022coyo-vit, title = {COYO-ViT}, author = {Lee, Sungjun and Park, Beomhee}, year = {2022}, howpublished = {\url{https://github.com/kakaobrain/coyo-vit}}, } ``` ```bibtex @misc{kakaobrain2022coyo-700m, title = {COYO-700M: Image-Text Pair Dat... | d9651207a332410e5c4517c5c0517a6f |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-agrivision This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3605 - Accuracy: 0.9203 | 5c713dab37b749c5316dc17d974f9e26 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5913 | 1.0 | 31 | 0.7046 | 0.7175 | | 0.1409 | 2.0 | 62 | 0.8423 | 0.6788 | | 0.0825 | 3.0 | 93 | 0.6224 | 0.... | 14b7c400b544ff32e39950bf9fb93f5b |
apache-2.0 | ['translation'] | false | ukr-rus * source group: Ukrainian * target group: Russian * OPUS readme: [ukr-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-rus/README.md) * model: transformer-align * source language(s): ukr * target language(s): rus * model: transformer-align * pre-processing: normalization + Se... | f610147d2a245de77a9f679d1f5a6917 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: ukr-rus - source_languages: ukr - target_languages: rus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-rus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['uk', 'ru'] - src_constituents: {'ukr'} - tgt_const... | ef00ed61d154c3acbcc9df2421e2461d |
mit | ['vision', 'video-classification'] | false | X-CLIP (base-sized model) X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=16) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https... | 4fc12add07c53910955461f242b6bc3e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab2 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: 3.1899 - Wer: 1.0 | 6fbbebeba5b75b0ae2232b9bdf8bf8b9 |
apache-2.0 | ['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: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_tr... | 577dc1e6f6eb52096154341feddc3ac4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 8.0486 | 13.89 | 500 | 3.6570 | 1.0 | | 3.2905 | 27.78 | 1000 | 3.1899 | 1.0 | | 7b7fc09957b91c4989aeb33dd89b58b5 |
other | [] | false | Upholstery Cleaning Fort Worth TX https://txfortworthcarpetcleaning.com/upholstery-cleaning.html (817) 523-1237 When you sit on your upholstery, you inhale allergens, dirt, and dust that are trapped in its fibers.Therefore, if you want to ensure the safety of your upholstery—especially if you have children or pets—you ... | 22f6872c030e2eef41e691a85edb0ae9 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_age_teens-5_sixties-5_s197 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 649cb8bd07a79cbf6980ef9eedc20047 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Fantasy Gun on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Gun concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of fantasy_gun** | 01fe4a785c87891863e18e30bc98d9c1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model:  | 1b384972adddf566d6f1628c1aea4e7f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [](https://discord.gg/9B2Pu2bEvj) [Image Source | 8b39e42650ef08ce4660d138ef496dee |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the bhapa concept trained by nahidalam on the nahidalam/bhapa dataset. This is a Stable Diffusion model fine-tuned on the bhapa concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of bhapa cake** This model was created as part of the DreamBooth Hackathon 🔥. Vis... | 57f129f3a3448678c6aeec9fbcbe149b |
apache-2.0 | ['generated_from_trainer'] | false | SimpleDataset This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6762 | d394bcc410e362c5cffae33f74532b52 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 4 | 3.3454 | | No log | 2.0 | 8 | 3.8818 | | No log | 3.0 | 12 | 3.6762 | | b5175ca221eea8e3a2aed048794ffc35 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1380 - F1: 0.8591 | ec08a51f529c728da203e165b65333fe |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2642 | 1.0 | 525 | 0.1624 | 0.8251 | | 0.1315 | 2.0 | 1050 | 0.1445 | 0.8508 | | 0.0832 | 3.0 | 1575 | 0.1380 | 0.8591 | ... | 0a1a2bcbbf703f6f81a8aff609ae3ee6 |
apache-2.0 | ['classification', 'zero-shot'] | false | Erlangshen-UniMC-DeBERTa-v2-330M-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... | 5c83724a437c45ab91630aebbbe0cd25 |
apache-2.0 | ['classification', 'zero-shot'] | false | 简介 Brief Introduction UniMC 核心思想是将自然语言理解任务转化为 multiple choice 任务，并且使用多个 NLU 任务来进行预训练。我们在英文数据集实验结果表明仅含有 2.35 亿参数的 [ALBERT模型](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Albert-235M-English)的zero-shot性能可以超越众多千亿的模型。并在中文测评基准 FewCLUE 和 ZeroCLUE 两个榜单中，13亿的[二郎神](https://huggingface.co/IDEA-CCNL/Erlangshen-UniMC-Megatr... | d354205be6e0c1b3f1746646986d0ab5 |
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 | 330M | Chinese | | 27ca1bb051763d4976fc4e2ac0348cf6 |
apache-2.0 | ['classification', 'zero-shot'] | false | 模型信息 Model Information 我们为零样本学习者提出了一种与输入无关的新范式，从某种意义上说，它与任何格式兼容并适用于一系列语言任务，例如文本分类、常识推理、共指解析、情感分析。我们的方法将零样本学习转化为多项选择任务，避免常用的大型生成模型（如 FLAN）中的问题。它不仅增加了模型的泛化能力，而且显着减少了对参数的需求。我们证明了这种方法可以在通用语言基准上取得最先进的性能，并在自然语言推理和文本分类等任务上产生令人满意的结果。更多详细信息可以参考我们的[论文](https://arxiv.org/abs/2210.08590)或者[GitHub](https://github.com/IDEA-CCNL/Fe... | 2a9eb9b326c49dd9bff0d8c4521a375a |
apache-2.0 | ['classification', 'zero-shot'] | false | 下游效果 Performance 我们使用全量数据测评我们的模型性能，并与 RoBERTa 进行对比 We use full data to evaluate our model performance and compare it with RoBERTa | Model | afqmc | tnews | iflytek | ocnli | |------------|------------|----------|-----------|----------| | RoBERTa-110M | 74.06 | 57.5 | 60.36 | 74.3 ... | d5e243e26bb63f2fbb0335c1e6ca42bb |
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_... | 85b1985da06c739f188198f8ece868f2 |
apache-2.0 | ['classification', 'zero-shot'] | false | 引用 Citation 如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)： If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590): ```text @article{unimc, author = {Ping Yang and Junjie Wang and Ruyi Gan and Xiny... | 37b790563f3f124a1edfa15b8c8a13b1 |
other | [] | false | Carpet Stain Removal Richardson TX https://carpetcleaning-richardson.com/carpet-stain-removal.html (972) 454-9815 One of the reasons our carpet stain cleaning is so popular with customers is that it is eco-friendly.Our products are safe for the home, pets, and children.We are able to quickly clean tough stains that you... | 02b0f7839bc3475926d228b3c22697da |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-wikinewssum-test 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: 2.9354 - Rouge1: 6.8433 - Rouge2: 2.5498 - Rougel: 5.6114 - Rougelsum: 6.353 | 47f9b9cdbcfe7c86154ad8d72dc11eb5 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 | 224af29d976e7798ffbffa51583d7c36 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 661 | 3.2810 | 6.4161 | 2.403 | 5.3674 | 6.0329 | | No log | 2.0 | 1322 ... | 2324c1631b2b197fb05e514781262a3c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-to-text'] | false | Wav2Vec2-Large-100k-VoxPopuli-Català Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Catalan language using the [Common Voice](https://huggingface.co/datasets/common_voice) and [ParlamentParla](https://www.openslr.org/59/) datasets. **Attention:**... | 6e07e479f6267cc561f88a0190759435 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-to-text'] | false | Results Word error rate was evaluated on the following datasets unseen by the model: | Dataset | WER | | ------- | --- | | [Test split CV+ParlamentParla]((https://github.com/ccoreilly/wav2vec2-catala/blob/master/test-filtered.csv)) | 5.98% | | [Google Crowsourced Corpus](https://www.openslr.org/69/) | 12.14% | | Aud... | a5ab44233e706b3af4694959e1be35b5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'speech-to-text'] | 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", "ca", split="test[:2%]") processor = Wav2Vec2Processor.from_p... | 5f55731f43931ad30282569308d2aae4 |
mit | ['generated_from_keras_callback'] | false | nst-sat/GlossBERT-finetunedTRAIN This model is a fine-tuned version of [kanishka/GlossBERT](https://huggingface.co/kanishka/GlossBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 10.7424 - Epoch: 0 | 4e53d5035b8f8f5a5a3802431a8ab59c |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | 08e1c7dae031d55067215197fedfa4bb |
apache-2.0 | ['translation'] | false | cau-eng * source group: Caucasian languages * target group: English * OPUS readme: [cau-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cau-eng/README.md) * model: transformer * source language(s): abk ady che kat * target language(s): eng * model: transformer * pre-processing: normaliz... | 82210201a18afa44c3860c66710258c5 |
apache-2.0 | ['translation'] | false | Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.abk-eng.abk.eng | 0.3 | 0.134 | | Tatoeba-test.ady-eng.ady.eng | 0.4 | 0.104 | | Tatoeba-test.che-eng.che.eng | 0.6 | 0.128 | | Tatoeba-test.kat-eng.kat.eng | 18.6 | 0.366 | | Tatoeba-test.multi.eng ... | 15a7966a20bf3c475c3867344a4e66d2 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: cau-eng - source_languages: cau - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/cau-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ab', 'ka', 'ce', 'cau', 'en'] - src_constituents: {... | 29079ad89a906557729c06af2e6fa25d |
apache-2.0 | ['generated_from_trainer'] | false | hf_fine_tune_hello_world This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.9796 - Accuracy: 0.616 | 5965fafd2317ea7845f7f13fa1df2c06 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 1.1352 | 0.504 | | No log | 2.0 | 250 | 1.0559 | 0.572 | | No log | 3.0 | 375 | 0.9796 | 0.... | d2c26cff15f4522327d8e8bbd07b55c8 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_wnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 2.3011 - Accuracy: 0.1549 | fb12bb1338c5d5449ac992d9903e0256 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6528 | 1.0 | 435 | 2.3011 | 0.1549 | | 0.4834 | 2.0 | 870 | 3.5400 | 0.0986 | | 0.4353 | 3.0 | 1305 | 5.1022 | 0.... | e091dfeb31ac3db88524f4844aa8980f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | bvidtassn Dreambooth model trained by brunadamiani with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stabl... | ae12b49d667c476d2cb1671c56c263f2 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the pssg concept trained by pharmapsychotic **Sugar gliders** are adorable creatures! I've never had one as a pet but I've been tempted. Imagine having one in your shirt pocket and feeding it snacks as you work. 😍 Anyway, I created a few AI renders of sugar gliders and mixed in with some photos... | e16728bae36bac685f7a6cd8a85d925a |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | Examples | | | | | ------------------------- | -------------------------- | ---------------------------- | |  |  |  | |  |   This model was trained using the diffusers based Dreambooth tra... | 3e730156858bf9aa041508c2ae8ee3b9 |
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: 0.0619 - Precision: 0.9347 - Recall: 0.9522 - F1: 0.9434 - Accuracy: 0.9869 | 9fce01387340ccad3e35a4dcfe9be428 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0847 | 1.0 | 1756 | 0.0696 | 0.9086 | 0.9281 | 0.9182 | 0.9817 | | 0.0338 | 2.0 |... | 403c88469106f69591f0c559cc8b2025 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-banking-4-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2920 - Accuracy: 0.3982 | 108e65cc44d19c4590d890e0359aee62 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | TTS model trained with Montreal Forced Aligner. To replicate or continue training from the given checkpoint, download [LJSpeech](https://keithito.com/LJ-Speech-Dataset/), install [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) and follow the steps [here](https://github.com/espnet/espnet/blob/mas... | 1d63ac003e86ff8fde25ae34f7bbcaee |
apache-2.0 | ['translation'] | false | opus-mt-fi-mos * source languages: fi * target languages: mos * OPUS readme: [fi-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-mos/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](http... | 5046bdd25c68abade0dc14f0ee63b20e |
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