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 | ['speech', 'xls_r', 'xls_r_translation', 'automatic-speech-recognition'] | false | Demo The model can be tested on [**this space**](https://huggingface.co/spaces/facebook/XLS-R-300m-EN-15). You can select the target language, record some audio in English, and then sit back and see how well the checkpoint can translate the input. | bfe274ca60ea5713ec6f0766f208b13d |
apache-2.0 | ['speech', 'xls_r', 'xls_r_translation', 'automatic-speech-recognition'] | false | replace following lines to load an audio file of your choice librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") audio_file = librispeech_en[0]["file"] asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-300m-en-to-15", feature_extractor="face... | dbd4f654d10dd272e9edadd6dec0a695 |
apache-2.0 | ['speech', 'xls_r', 'xls_r_translation', 'automatic-speech-recognition'] | false | Results `en` -> `{lang}` See the row of **XLS-R (0.3B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model.  | 20476bd124c86bf121405cee9b2bd629 |
mit | ['generated_from_trainer', 'deberta-v3'] | false | DeBERTa v3 (small) fine-tuned on MNLI This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4985 - Accuracy: 0.8746 | f63291212c642d7f616ff57e25dfe833 |
mit | ['generated_from_trainer', 'deberta-v3'] | false | Model description [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. Please check the [official repository](https://github... | 36984cfb1d583f3379947e8e491e8b0a |
mit | ['generated_from_trainer', 'deberta-v3'] | false | Training and evaluation data The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothe... | 3c1d622a15cc3965a708dc2dd6041172 |
mit | ['generated_from_trainer', 'deberta-v3'] | 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 | 9e825812eadae1a9ce6de3c9089cf6d3 |
mit | ['generated_from_trainer', 'deberta-v3'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7773 | 0.04 | 1000 | 0.5241 | 0.7984 | | 0.546 | 0.08 | 2000 | 0.4629 | 0.8194 | | 0.5032 | 0.12 | 3000 | 0.4704 ... | 0c18b535eb97a9ac7d048773cdfa6b4c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'dreambooth-hackathon', 'food'] | false | Dreambooth Model for Food trained on a custom dataset. This is a Stable Diffusion model fine-tuned on the food concept with DreamBooth. It can be used by modifying the `instance_prompt`: **A photo of ddahi puri** This model was created as part of the DreamBooth Hackathon 🔥. | ba35f164aef62153d802ca374a650e85 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'dreambooth-hackathon', 'food'] | false | Examples Some examples of images generated by the model are shown below, with their prompts.  on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1959 - Accuracy: 0.9783 - F1: 0.9783 | bc0460e8fc803755a65852d74f2deee0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.1777 | 1.0 | 3180 | 0.2118 | 0.9563 | 0.9566 | | 0.1409 | 2.0 | 6360 | 0.1417 | 0.9736 | 0.9736 | | 0.1035 ... | a892660f8f207e7266a7c57be09c1758 |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Large Czech CV11 This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_11_0 cs dataset. It achieves the following results on the evaluation set: - Loss: 0.2528 - Wer: 10.8278 | e23ff1a76825d19fc64277d3e95f120a |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0058 | 4.02 | 1000 | 0.2097 | 11.9563 | | 0.0012 | 8.04 | 2000 | 0.2210 | 10.9751 | | 0.001 | 13.01 | 3000 | 0.2405 | 11.348... | 12f912f180d679ce328aceef1334bebc |
mit | ['Keyphrase Generation'] | false | Usage ```python !pip install KeyBartAdapter from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from models import KeyBartAdapter model = KeyBartAdapter.from_pretrained('Adapting/KeyBartAdapter', revision = '3aee5ecf1703b9955ab0cd1b23208cc54eb17fce',adapter_hid_dim =32) tokenizer = AutoTokenizer.from_pret... | faa01ffaaca0fa2729f651a4091c45b3 |
cc | ['token-classification', 'named-entity-recognition', 'multi_class_classification'] | false | Limitations: Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and the base model card should be reviewed before using the model - - [NCBI Disease](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655/pdf/nihms557856.pdf) - [disti... | 26aa1c40be52f7acab0f01a4853cbe4b |
cc | ['token-classification', 'named-entity-recognition', 'multi_class_classification'] | false | How to use: Load the model from the library using the following checkpoints: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-ncbi-disease") model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-ncbi-... | ea21cbad00817067a216b969d87a094a |
mit | ['text-classification'] | false | Multi2ConvAI-Quality: finetuned MBert for German
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Quality (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: German (de)
- model type:... | f1b93f39ffb7a738a2bd5543465e0934 |
mit | ['text-classification'] | false | Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-quality-de-mbert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-quality-de-mbert")
````
| bf928b0da8d575779ec6418151798e34 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | <style> img { display: inline; } </style>    | 4e36f0f9a142c771bc8397eadf70ec2b |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Fine-tuned whisper-medium model for ASR in French This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium), trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and the validation splits of [Common Voice 11.0](https://h... | f1f31b8f27efe8ec4eeb333c38248f58 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'whisper-event'] | false | Load model model = AutoModelForSpeechSeq2Seq.from_pretrained("bofenghuang/whisper-medium-french").to(device) processor = AutoProcessor.from_pretrained("bofenghuang/whisper-medium-french", language="french", task="transcribe") | 9cd984d21ddefdfdf1cc3a8ae0b5ab62 |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-finetuned-tapt-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: | 030add9f5e945a5a615f2c776f2df8c1 |
other | ['vision', 'image-segmentation'] | false | MobileViT + DeepLabV3 (small-sized model) MobileViT model pre-trained on PASCAL VOC at resolution 512x512. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repos... | dacedc722b292a5b2b27730f2fd2b4b5 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = Mo... | b68b87726a89a389f100e91892d0a62b |
other | ['vision', 'image-segmentation'] | false | params | URL | |------------------|-----------------|-----------|-----------------------------------------------------------| | MobileViT-XXS | 73.6 | 1.9 M | https://huggingface.co/apple/deeplabv3-mobilevit-xx-small | | MobileViT-XS | 77.1 ... | dcc2b2d2b5d1d80e76266940925026c1 |
apache-2.0 | ['translation'] | false | opus-mt-fr-lua * source languages: fr * target languages: lua * OPUS readme: [fr-lua](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-lua/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | a9cb220ba988f2bbf266b01e90974f00 |
apache-2.0 | ['translation'] | false | fra-cat * source group: French * target group: Catalan * OPUS readme: [fra-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-cat/README.md) * model: transformer-align * source language(s): fra * target language(s): cat * model: transformer-align * pre-processing: normalization + Sente... | a3669b0e5c664577059af9fd0a962c92 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: fra-cat - source_languages: fra - target_languages: cat - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-cat/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fr', 'ca'] - src_constituents: {'fra'} - tgt_const... | 01804f18f3646bc773baae69adce59a2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6096 - Matthews Correlation: 0.5129 | 6963cde232f44d0a41915e97195d3491 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.146600743522182e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | 411401bee812b63874a1b8cb2b0de6aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5107 | 1.0 | 1069 | 0.4877 | 0.4213 | | 0.3124 | 2.0 | 2138 | 0.6096 | 0.5129 | | e9ece8127fad78bdfe9a3e782595ed29 |
mit | ['generated_from_trainer'] | false | mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2 This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Bleu: ... | 20a7c0c068298a7a75b7812cf46f669a |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 - mixed_precision_training: Native AMP | 6a916f2c95088b959a9be32264f43d3a |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 8.1731 | 0.25 | 100 | 2.8417 | 0.9599 | 0.028 | 230.885 | | 0.6743 | 0.5 | 200 | 0.4726 | 6.4055 | ... | 71a61315c72e98d655333386e097b97d |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'citrinet', 'pytorch', 'NeMo'] | false | Model description This model transcribes audio samples in Catalan to lowercase text without punctuation. The model was fine-tuned from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model using the [NeMo](https://github.com/NVIDIA/NeMo) to... | 8eb34211bb097fe56153d576626885b8 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'citrinet', 'pytorch', 'NeMo'] | false | Usage Requiered libraries: ```bash pip install nemo_toolkit['all'] ``` Clone the repository to download the model: ```bash git clone https://huggingface.co/projecte-aina/stt-ca-citrinet-512 ``` Given that `NEMO_PATH` is the path that points to the downloaded `stt-ca-citrinet-512.nemo` file, to do inference over a... | 517359756b005130563864680e8f149d |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'citrinet', 'pytorch', 'NeMo'] | false | Data preparation We have processed [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets) using the NeMo toolkit. We used [get_commonvoice_data.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/get_commonvoice_data.py) to process the manifests and made a subsequent data cleaning step. ... | 47b19ff900803f9ccb69f4f67a47c281 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'citrinet', 'pytorch', 'NeMo'] | false | Training procedure This model was trained starting from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model. The initial learning rate was set to 0.005 and the minimum lr for weight decay was set to 1e-7. The model was trained for 90 ste... | 42bdb34b4fe38d02fb4de74cc5041869 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  T5-Efficient-LARGE-NH32 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint ... | 244c60773426a2f0f319e187e84d32c0 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-nh32** - is of model type **Large** with the following variations: - **nh** is **32** It has **1039.72** million parameters and thus requires *ca.* **4158.86 MB** of memory in full precision (*fp32*) or **2079.43 MB** of memory in half precisi... | 2bb7baec849c876185c4e54b0d3b22f7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb 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: 2.4454 | 8e3ce9fdf225fb027479da38139f33b8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6763 | 1.0 | 313 | 2.4484 | | 2.5402 | 2.0 | 626 | 2.4312 | | 2.5194 | 3.0 | 939 | 2.3894 | | 5b6ae0150fc3cb732641ab9229a988c5 |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Text-to-Speech (TTS) with Tacotron2 trained on LJSpeech This repository provides all the necessary tools for Text-to-Speech (TTS) with SpeechBrain using a [Tacotron2](https://arxiv.org/abs/1712.05884) pretrained on [ALLFA Public](https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/SWAHILI). The pre-trained model... | a4a5a236e8d9534d83ee3b673d47fac9 |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Intialize TTS (tacotron2) and Vocoder (HiFIGAN) tacotron2 = Tacotron2.from_hparams(source="aioxlabs/tacotron-swahili", savedir="tmpdir_tts") hifi_gan = HIFIGAN.from_hparams(source="aioxlabs/hifigan-swahili", savedir="tmpdir_vocoder") | 419d2d8d99187f2ab8c824ae9b95d900 |
apache-2.0 | ['text-to-speech', 'TTS', 'speech-synthesis', 'Tacotron2', 'speechbrain'] | false | Save the waverform torchaudio.save('example_TTS.wav',waveforms.squeeze(1), 16000) ``` If you want to generate multiple sentences in one-shot, you can do in this way: ``` from speechbrain.pretrained import Tacotron2 tacotron2 = Tacotron2.from_hparams(source="aioxlabs/tacotron-swahili", savedir="tmpdir") items = [ ... | 2b6bdfe62e9a49ee23c6a4644a6617e1 |
apache-2.0 | ['translation'] | false | opus-mt-es-iso * source languages: es * target languages: iso * OPUS readme: [es-iso](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-iso/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | 5c1a2e034f6e396db754540c8df258dd |
mit | ['generated_from_trainer'] | false | lilt-en-funsd This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 1.8731 - Answer: {'precision': 0.8688915375446961, 'recall': 0.89228886168... | 96712199a863e7821025936df8abb992 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question ... | f76c226cfdcf887177634cfb2a400948 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | Optimized and Quantized [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) with a custom pipeline.py This repository implements a `custom` task for `sentence-embeddings` for 🤗 Inference Endpoints for accelerated inference using [🤗 Optimum](https://huggingface.co... | 97df264b2ad4732a02092eaf59f32aad |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | how-to-create-your-own-optimized-and-quantized-model) you will learn how the model was converted & optimized, it is based on the [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers) blog post. It also includes how to create your custom pipeline and test ... | e321dd2755028fb32f06a4dd80cd8e2f |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | Run Request ```python import json from typing import List import requests as r import base64 ENDPOINT_URL = "" HF_TOKEN = "" def predict(document_string:str=None): payload = {"inputs": document_string} response = r.post( ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload ... | f2e99bb23156db1d1d8ea44274c1b23c |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | 3-create-custom-handler-for-inference-endpoints) Helpful links: * [Accelerate Sentence Transformers with Hugging Face Optimum](https://www.philschmid.de/optimize-sentence-transformers) * [Create Custom Handler Endpoints](https://link-to-docs) | 4fa8c34a20665d40cf889138b4eae284 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | 1. Convert model to ONNX ```python from optimum.onnxruntime import ORTModelForFeatureExtraction from transformers import AutoTokenizer from pathlib import Path model_id="sentence-transformers/all-MiniLM-L6-v2" onnx_path = Path(".") | 32104a9900dd862bd302dee36451e1bd |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | create ORTOptimizer and define optimization configuration optimizer = ORTOptimizer.from_pretrained(model_id, feature=model.pipeline_task) optimization_config = OptimizationConfig(optimization_level=99) | 3a4bf149ff950b178d484a7af44a10b1 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | apply the optimization configuration to the model optimizer.export( onnx_model_path=onnx_path / "model.onnx", onnx_optimized_model_output_path=onnx_path / "model-optimized.onnx", optimization_config=optimization_config, ) | f89adbd8f3b9c3a463eb8256f9fe4bef |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | create ORTQuantizer and define quantization configuration dynamic_quantizer = ORTQuantizer.from_pretrained(model_id, feature=model.pipeline_task) dqconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False) | cba6135fe51260f58e7f22ec63802140 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | apply the quantization configuration to the model model_quantized_path = dynamic_quantizer.export( onnx_model_path=onnx_path / "model-optimized.onnx", onnx_quantized_model_output_path=onnx_path / "model-quantized.onnx", quantization_config=dqconfig, ) ``` | d70b80f9cc375f2f56c4937103ffecd9 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | 3. Create Custom Handler for Inference Endpoints ```python %%writefile pipeline.py from typing import Dict, List, Any from optimum.onnxruntime import ORTModelForFeatureExtraction from transformers import AutoTokenizer import torch.nn.functional as F import torch | b3ef20971a241f1771c26558cfb36839 |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) class PreTrainedPipeline(): def __init__(se... | c435ef6363e1ee921ed8deb62ad6c6ea |
mit | ['sentence-embeddings', 'endpoints-template', 'optimum'] | false | load the optimized model self.model = ORTModelForFeatureExtraction.from_pretrained(path, file_name="model-quantized.onnx") self.tokenizer = AutoTokenizer.from_pretrained(path) def __call__(self, data: Any) -> List[List[Dict[str, float]]]: """ Args: data (:obj:): ... | 1642da33f81a82b3bfa28f7a024b180e |
creativeml-openrail-m | ['text-to-image'] | false | dndcoverart-v1 Dreambooth model trained by abesmon with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebo... | 39ce9369bfaa1530fbe8d1537dc59792 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1605 | 4eac5ef7480945717046967725e0a6a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2172 | 1.0 | 5533 | 1.1532 | | 0.9446 | 2.0 | 11066 | 1.1184 | | 0.7671 | 3.0 | 16599 | 1.1605 | | 1ef8d3b60037c9b0635916eabb0c9b0e |
mit | ['generated_from_trainer'] | false | Bio_ClinicalBERT_fold_1_binary_v1 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7063 - F1: 0.8114 | 865c794a97deec0f5500b9ab3c0ecae5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.4168 | 0.7949 | | 0.3981 | 2.0 | 576 | 0.4124 | 0.8137 | | 0.3981 | 3.0 | 864 | 0.6691 | 0.8002 | |... | ca963ca975fd6a3beab7265f23957633 |
apache-2.0 | ['generated_from_keras_callback'] | false | recipe-improver This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.5570 - Epoch: 0 | 229d05a03795fec5cbb6182514b0d139 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5539, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | 01bd0758a8a999782b0e2f38e74c1721 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-burak-new-v10-small This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3345 - Wer: 0.2030 | 0456d4188770d81dd7883a9cae449a87 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - 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... | 815146ed38adf14504ab19078a9ebd74 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 6.1239 | 9.43 | 500 | 3.1263 | 1.0 | | 1.7776 | 18.87 | 1000 | 0.3793 | 0.4838 | | 0.5275 | 28.3 | 1500 | 0.2654 | ... | 7ed4107d10e0c3c0545a665ba5b17ca2 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/ms_indic_is18/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/chai_microsoft_indian_langs_te ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 4b1d3e89ac275f0c8538c39075817832 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue Mar 22 13:38:24 EDT 2022` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1+cu111` - Git hash: `f91410f712d1287cd6809c5bf26b54c5a40fe314` - Commit date: `Mon Mar 14 22:32:17 2022 -0400` | 824e2cf8614636d7a31a0dddfc9e103f |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|28413|78.0|19.5|2.5|2.4|24.4|80.1| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test... | 0cdf5c2a9f299bc301e3b2a31cd77605 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|229419|95.6|2.2|2.2|1.6|6.1|80.1| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_... | bea6d5e2b25e57f91a7a5f11c22e15f0 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.ave_asr_model_valid.acc.ave/test_te|3040|146657|92.7|4.7|2.6|1.6|8.9|80.1| |decode_transformer5_lm_lm_train_lm_rnn_te_bpe150_valid.loss.best_asr_model_valid.acc.ave/test_... | 441c0feec543e19f6aee756903b6c1b7 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_xlsr53_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_xlsr53_conformer_raw_te_bpe150_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_met... | dab4e3c67310851879b628de96f53413 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | caafe0ea9b7a0611bf56a4e603f71ccd |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | rovio Dreambooth model trained by koonoo 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-stable-diffusio... | cbaeb5f0a997533346e684ba99fc4629 |
cc-by-4.0 | ['answer extraction'] | false | Model Card of `lmqg/mbart-large-cc25-ruquad-ae` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for answer extraction on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi41... | 12c200f359a880a9688ab2aeefe5da09 |
cc-by-4.0 | ['answer extraction'] | false | model prediction answers = model.generate_a("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.") ``` - With `transformers` ```python from transformers import pipeline ... | bf90eefe3cd578a3c235834a7a015843 |
cc-by-4.0 | ['answer extraction'] | false | Evaluation - ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset ... | 878967c71c3273f22ce9f700e4385722 |
cc-by-4.0 | ['answer extraction'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_sentence'] - output_types: ['answer'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 32 - epoc... | 23eea1870e14a6c662ea6351163ca689 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr5e05-wd0.08-bs16 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: 0.2769 - Rmse: 0.5263 - Mse: 0.2769 - Mae: 0.4297 | 9daab0b16ee9b1d275c10d56c5e33243 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 254659718a2ca5039f66df7187e14dcb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2779 | 1.0 | 1245 | 0.2758 | 0.5252 | 0.2758 | 0.4113 | | 0.2742 | 2.0 | 2490 | 0.2762 | 0.5256 | 0.2762 ... | 7d8293025ba5672a22f803b9feb91afe |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_allagree3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset. It achieves the following results on the evaluation set: - Loss: 0.0937 - Accuracy: 0.9779 - F1: 0.9780 | 11b17e2a4cd2d8e4bdd99b6bade38a4c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 | | 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 | | 0.0795 |... | 67e262e881e5317963f2746b7306dfcc |
mit | ['generated_from_trainer'] | false | Goodreads_Books_Reviews_Roberta_51 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8343 - F1: 0.6514 - Accuracy: 0.6601 | 4da139679010b45affa7ff06fb14d503 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:------:|:--------:| | 0.8728 | 1.0 | 12160 | 0.8448 | 0.6425 | 0.6504 | | 0.793 | 2.0 | 24320 | 0.8343 | 0.6514 | 0.6601 | | f7904c7cef0681e855c976e6e7610e8f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 『LimeMix V1 & V2』 <img src="https://i.imgur.com/RGcmq84.jpg" width="1024" height=""> <img src="https://i.imgur.com/14goJA5.png" width="1024" height=""> - "LimeMix" is a model created by hierarchical merging based on "anything-v4.5"([andite/anything-v4.0 · Hugging Face](https://huggingface.co/andite/anything-v4.0)). ... | 925e89a498b913940559c22a1f5f15ce |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆About - "LimeMix" is a model with more emphasis on composition and illustration than "DateMix". - Sampler: DDIM or DPM++ SDE Karras - Steps: 20~ - Clipskip: 2 - CFG Scale: 5-8 - Denoise strength: 0.5-0.7(As you like) - Negative prompts should be as few as possible. - vae: As you wish. (Any etc. If not used, c... | b37eac4bbd82ec7469d524f0b5dfe275 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆Colab Note [](https://colab.research.google.com/drive/10-FYdd2xl4f9ugybZrKM3B3DWUx5twQo?usp=sharing) - (I have not checked the operation but it probably works.) ---- | e55412e9cae3cddb89c2bac6f90d1a12 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆Comparison <img src="https://i.imgur.com/astFr0y.jpg" width="1700" height=""> ``` (morning), (school), 1girl, solo, looking at viewer, cowboy shot, (school uniform), smile, stockings ``` ---- <img src="https://i.imgur.com/LVFWSal.jpg" width="1700" height=""> ``` kawaii, winter, ((street)), ((building)), (noon), ... | 4141975a32a564c2d7268e7e07ffca04 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outp... | 275c452dae7c8b0e5c3a5741c1331fe7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_headline_padded_overfit 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: - Train Loss: 2.7906 - Validation Loss: 3.0025 - Epoch: 19 | 0712a47d25f7e573652bf390b02975e4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.9428 | 2.8153 | 0 | | 2.8642 | 2.7500 | 1 | | 2.8393 | 2.7033 | 2 | | 2.8209 | 2.8135 | 3 | | 2.7471 | 2.7677 | 4 | | 2.6914 |... | f29d39f78635d4578c2d107e7ad892ce |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image', 'image-to-image'] | false | **Mirror's Edge Diffusion** A Stable Diffusion model trained on Mirror's Edge screenshots using Dreambooth. Use the prompt **MirrorsEdge style**. Other notable tokens are **abstract**, **floating shapes**, **cityscape**, **pristine**, **sterile**, and **back rooms**. If your outputs are too random and geometric, try... | 7bcdcd2be7c9a8203d25ecd5f78f8bc5 |
cc-by-sa-4.0 | ['capitalization', 'punctuation', 'token-classification'] | false | ✨ vibert-capitalization-punctuation This a [viBERT](https://huggingface.co/FPTAI/vibert-base-cased) model finetuned for punctuation restoration on the [OSCAR-2109](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset. The model predicts the punctuation and upper-casing of plain, lower-cased text. An examp... | 7d0bb4808d8e643b890a946267ea0aa3 |
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