license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 128 - eval_batch_size: 64 - 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: 3.0 - mixed_precision_training: Native AMP | 6b8ae12de7ccbf90581260cd4f0dc1ad |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.0265 | 1.0 | 122 | 1.0110 | 98.4608 | | 0.9208 | 2.0 | 244 | 0.9148 | 88.3812 | | 0.8169 | 3.0 | 366 | 0.8394 | 86.0500 | | 8e36125cc27b08181552499f34e85ffe |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Russian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 79204cd741abb5eebe186b6639b28a98 |
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", "ru", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian") resampler = torchaudio.transforms.Resample(48_000, 16_000) | e4bf7ed5892c3ca7f75bd32fbbc318dc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Russian test data of Common Voice. ```python import torch import torchaudio import urllib.request import tarfile import pandas as pd from tqdm.auto import tqdm from datasets import load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | d7b866ae163c472e0ed18abdc3eb6738 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Download the raw data instead of using HF datasets to save disk space data_url = "https://voice-prod-bundler-ee1969a6ce8178826482b88e843c335139bd3fb4.s3.amazonaws.com/cv-corpus-6.1-2020-12-11/ru.tar.gz" filestream = urllib.request.urlopen(data_url) data_file = tarfile.open(fileobj=filestream, mode="r|gz") data_file.extractall() wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian") model = Wav2Vec2ForCTC.from_pretrained("anton-l/wav2vec2-large-xlsr-53-russian") model.to("cuda") cv_test = pd.read_csv("cv-corpus-6.1-2020-12-11/ru/test.tsv", sep='\t') clips_path = "cv-corpus-6.1-2020-12-11/ru/clips/" def clean_sentence(sent): sent = sent.lower() | af5f07d2f78c9d29bca1a11dcc80bfd2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | remove repeated spaces sent = " ".join(sent.split()) return sent targets = [] preds = [] for i, row in tqdm(cv_test.iterrows(), total=cv_test.shape[0]): row["sentence"] = clean_sentence(row["sentence"]) speech_array, sampling_rate = torchaudio.load(clips_path + row["path"]) resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) row["speech"] = resampler(speech_array).squeeze().numpy() inputs = processor(row["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(logits, dim=-1) targets.append(row["sentence"]) preds.append(processor.batch_decode(pred_ids)[0]) | 4e5e43d5a40a08c3ae20ef88f6da7b42 |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8268 | 8cca638f1c3f92fa535d9a0a3fa065da |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 1.9963 | | 2.0972 | 2.0 | 500 | 1.8649 | | 2.0972 | 3.0 | 750 | 1.8268 | | 18e147e0e5b6a583d2516f5bd111e318 |
cc-by-4.0 | ['roberta', 'roberta-base', 'token-classification', 'NER', 'named-entities', 'BIO', 'movies', 'DAPT'] | false | Movie Roberta + Movies NER Task Objective: This is Roberta Base + Movie DAPT --> trained for the NER task using MIT Movie Dataset https://huggingface.co/thatdramebaazguy/movie-roberta-base was used as the MovieRoberta. ``` model_name = "thatdramebaazguy/movie-roberta-MITmovieroberta-base-MITmovie" pipeline(model=model_name, tokenizer=model_name, revision="v1.0", task="ner") ``` | a7458fe14ed7e00d703e95b8498dc25a |
cc-by-4.0 | ['roberta', 'roberta-base', 'token-classification', 'NER', 'named-entities', 'BIO', 'movies', 'DAPT'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** NER **Training data:** MIT Movie **Eval data:** MIT Movie **Infrastructure**: 2x Tesla v100 **Code:** See [example](https://github.com/adityaarunsinghal/Domain-Adaptation/blob/master/scripts/shell_scripts/movieR_NER_squad.sh) | cde1b927abdc1d5c5356751449724be2 |
cc-by-4.0 | ['roberta', 'roberta-base', 'token-classification', 'NER', 'named-entities', 'BIO', 'movies', 'DAPT'] | false | Eval on MIT Movie - epoch = 5.0 - eval_accuracy = 0.9472 - eval_f1 = 0.8876 - eval_loss = 0.2211 - eval_mem_cpu_alloc_delta = 3MB - eval_mem_cpu_peaked_delta = 2MB - eval_mem_gpu_alloc_delta = 0MB - eval_mem_gpu_peaked_delta = 38MB - eval_precision = 0.887 - eval_recall = 0.8881 - eval_runtime = 0:00:03.73 - eval_samples = 1955 - eval_samples_per_second = 523.095 Github Repo: - [Domain-Adaptation Project](https://github.com/adityaarunsinghal/Domain-Adaptation/) --- | 401de2af61b1c31ad47b343478750aa6 |
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: 5 | efca5cce0f9232c28eaf92ccbde6e9a5 |
mit | ['generated_from_trainer'] | false | wmt-mbart50-large-finetuned-en-to-pt This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2510 - Bleu: 62.7011 - Gen Len: 19.224 | 2ffde8e961c8b2c11e72a595a293c17a |
mit | ['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP | 8fdb5b284bafd36ab88e0176548fd7f4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 1.6426 | 1.0 | 433 | 0.5323 | 4.484 | 10.5635 | | 0.2571 | 2.0 | 866 | 0.1965 | 47.6449 | 19.164 | | 0.1043 | 3.0 | 1299 | 0.1723 | 53.6231 | 19.1455 | | 0.058 | 4.0 | 1732 | 0.1908 | 52.9831 | 18.5543 | | 0.0382 | 5.0 | 2165 | 0.1801 | 58.4418 | 19.0808 | | 0.0244 | 6.0 | 2598 | 0.2014 | 56.0197 | 20.0485 | | 0.0195 | 7.0 | 3031 | 0.2029 | 56.7903 | 18.642 | | 0.0138 | 8.0 | 3464 | 0.2015 | 57.6855 | 19.0 | | 0.0126 | 9.0 | 3897 | 0.2095 | 58.5733 | 18.7644 | | 0.0095 | 10.0 | 4330 | 0.1946 | 60.3165 | 19.6097 | | 0.0067 | 11.0 | 4763 | 0.2094 | 60.2691 | 18.9561 | | 0.0055 | 12.0 | 5196 | 0.2202 | 60.375 | 19.3025 | | 0.0046 | 13.0 | 5629 | 0.2153 | 60.7254 | 19.0855 | | 0.0035 | 14.0 | 6062 | 0.2239 | 61.458 | 19.0647 | | 0.0054 | 15.0 | 6495 | 0.2250 | 61.5297 | 19.164 | | 0.0025 | 16.0 | 6928 | 0.2458 | 61.263 | 19.0531 | | 0.002 | 17.0 | 7361 | 0.2354 | 62.4404 | 19.2102 | | 0.0015 | 18.0 | 7794 | 0.2403 | 62.0235 | 19.1293 | | 0.0011 | 19.0 | 8227 | 0.2477 | 62.6301 | 19.2494 | | 0.0009 | 20.0 | 8660 | 0.2510 | 62.7011 | 19.224 | | 5f9f2b1b1c2430dae36263697a9b3869 |
mit | ['generated_from_trainer'] | false | predict-perception-xlmr-focus-object This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1927 - Rmse: 0.5495 - Rmse Focus::a Su un oggetto: 0.5495 - Mae: 0.4174 - Mae Focus::a Su un oggetto: 0.4174 - R2: 0.5721 - R2 Focus::a Su un oggetto: 0.5721 - Cos: 0.5652 - Pair: 0.0 - Rank: 0.5 - Neighbors: 0.5518 - Rsa: nan | 0e6cb714b3e656f6897f3f9085cbf894 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Rmse Focus::a Su un oggetto | Mae | Mae Focus::a Su un oggetto | R2 | R2 Focus::a Su un oggetto | Cos | Pair | Rank | Neighbors | Rsa | |:-------------:|:-----:|:----:|:---------------:|:------:|:---------------------------:|:------:|:--------------------------:|:-------:|:-------------------------:|:-------:|:----:|:----:|:---------:|:---:| | 1.0316 | 1.0 | 15 | 0.6428 | 1.0035 | 1.0035 | 0.8806 | 0.8806 | -0.4272 | -0.4272 | -0.4783 | 0.0 | 0.5 | 0.5302 | nan | | 1.0005 | 2.0 | 30 | 0.4564 | 0.8456 | 0.8456 | 0.7078 | 0.7078 | -0.0134 | -0.0134 | 0.4783 | 0.0 | 0.5 | 0.4440 | nan | | 0.9519 | 3.0 | 45 | 0.4151 | 0.8063 | 0.8063 | 0.6797 | 0.6797 | 0.0784 | 0.0784 | 0.1304 | 0.0 | 0.5 | 0.4888 | nan | | 0.92 | 4.0 | 60 | 0.3982 | 0.7898 | 0.7898 | 0.6516 | 0.6516 | 0.1159 | 0.1159 | 0.2174 | 0.0 | 0.5 | 0.5036 | nan | | 0.8454 | 5.0 | 75 | 0.2739 | 0.6550 | 0.6550 | 0.5292 | 0.5292 | 0.3919 | 0.3919 | 0.6522 | 0.0 | 0.5 | 0.4160 | nan | | 0.7247 | 6.0 | 90 | 0.2413 | 0.6148 | 0.6148 | 0.5347 | 0.5347 | 0.4642 | 0.4642 | 0.4783 | 0.0 | 0.5 | 0.3453 | nan | | 0.6055 | 7.0 | 105 | 0.3109 | 0.6978 | 0.6978 | 0.6115 | 0.6115 | 0.3098 | 0.3098 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan | | 0.5411 | 8.0 | 120 | 0.3932 | 0.7848 | 0.7848 | 0.6712 | 0.6712 | 0.1271 | 0.1271 | 0.4783 | 0.0 | 0.5 | 0.4154 | nan | | 0.4784 | 9.0 | 135 | 0.1316 | 0.4540 | 0.4540 | 0.3750 | 0.3750 | 0.7079 | 0.7079 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.4039 | 10.0 | 150 | 0.2219 | 0.5896 | 0.5896 | 0.4954 | 0.4954 | 0.5074 | 0.5074 | 0.5652 | 0.0 | 0.5 | 0.4838 | nan | | 0.3415 | 11.0 | 165 | 0.1935 | 0.5505 | 0.5505 | 0.4443 | 0.4443 | 0.5704 | 0.5704 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.3369 | 12.0 | 180 | 0.2118 | 0.5761 | 0.5761 | 0.4554 | 0.4554 | 0.5296 | 0.5296 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.3083 | 13.0 | 195 | 0.1928 | 0.5496 | 0.5496 | 0.4368 | 0.4368 | 0.5718 | 0.5718 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2678 | 14.0 | 210 | 0.2205 | 0.5877 | 0.5877 | 0.4472 | 0.4472 | 0.5105 | 0.5105 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2199 | 15.0 | 225 | 0.2118 | 0.5760 | 0.5760 | 0.4689 | 0.4689 | 0.5297 | 0.5297 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2238 | 16.0 | 240 | 0.2461 | 0.6209 | 0.6209 | 0.5047 | 0.5047 | 0.4537 | 0.4537 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.2233 | 17.0 | 255 | 0.2307 | 0.6011 | 0.6011 | 0.4618 | 0.4618 | 0.4879 | 0.4879 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1903 | 18.0 | 270 | 0.2207 | 0.5880 | 0.5880 | 0.4432 | 0.4432 | 0.5100 | 0.5100 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1714 | 19.0 | 285 | 0.2146 | 0.5798 | 0.5798 | 0.4368 | 0.4368 | 0.5236 | 0.5236 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1759 | 20.0 | 300 | 0.1745 | 0.5228 | 0.5228 | 0.4152 | 0.4152 | 0.6126 | 0.6126 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1505 | 21.0 | 315 | 0.1944 | 0.5519 | 0.5519 | 0.4170 | 0.4170 | 0.5684 | 0.5684 | 0.5652 | 0.0 | 0.5 | 0.6247 | nan | | 0.1467 | 22.0 | 330 | 0.1802 | 0.5313 | 0.5313 | 0.3910 | 0.3910 | 0.5999 | 0.5999 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1441 | 23.0 | 345 | 0.2360 | 0.6081 | 0.6081 | 0.4755 | 0.4755 | 0.4760 | 0.4760 | 0.4783 | 0.0 | 0.5 | 0.4938 | nan | | 0.1553 | 24.0 | 360 | 0.2129 | 0.5774 | 0.5774 | 0.4539 | 0.4539 | 0.5274 | 0.5274 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1163 | 25.0 | 375 | 0.1780 | 0.5281 | 0.5281 | 0.3952 | 0.3952 | 0.6048 | 0.6048 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.1266 | 26.0 | 390 | 0.2163 | 0.5821 | 0.5821 | 0.4569 | 0.4569 | 0.5198 | 0.5198 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1416 | 27.0 | 405 | 0.1829 | 0.5352 | 0.5352 | 0.4082 | 0.4082 | 0.5939 | 0.5939 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1576 | 28.0 | 420 | 0.1930 | 0.5498 | 0.5498 | 0.4126 | 0.4126 | 0.5716 | 0.5716 | 0.6522 | 0.0 | 0.5 | 0.6622 | nan | | 0.118 | 29.0 | 435 | 0.2070 | 0.5694 | 0.5694 | 0.4378 | 0.4378 | 0.5405 | 0.5405 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | 0.1179 | 30.0 | 450 | 0.1927 | 0.5495 | 0.5495 | 0.4174 | 0.4174 | 0.5721 | 0.5721 | 0.5652 | 0.0 | 0.5 | 0.5518 | nan | | be48472c3add1b8dc50b087350b72c55 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8061 | 1.0 | 500 | 0.5023 | | 0.6521 | 2.0 | 1000 | 0.3094 | | 0.5033 | 3.0 | 1500 | 0.2751 | | ad8ea6eda7bcedb625917c3df7a16fc1 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 28.6 - GMACs: 13.1 - Activations (M): 39.5 - Image size: 384 x 384 - **Papers:** - A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545 - **Original:** https://github.com/facebookresearch/ConvNeXt - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-22k | f3ef871e98753ee5e6f94bb946bb0679 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('convnext_tiny.fb_in22k_ft_in1k_384', pretrained=True) model = model.eval() | 5744119d8e1e77f5e71c89b2cc1726c8 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k_ft_in1k_384', pretrained=True, features_only=True, ) model = model.eval() | 3788b445693d80fddb98a767dfa2f130 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'convnext_tiny.fb_in22k_ft_in1k_384', pretrained=True, num_classes=0, | 0146006a53b988db373b3da4aff2ce86 |
cc-by-4.0 | ['int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Post-training static quantization This is an INT8 PyTorch model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2). The calibration dataloader is the train dataloader. The default calibration sampling size 100 isn't divisible exactly by batch size 8, so the real sampling size is 104. The linear modules **roberta.encoder.layer.7.output.dense**, **roberta.encoder.layer.8.output.dense**, **roberta.encoder.layer.9.output.dense**, fall back to fp32 for less than 1% relative accuracy loss. | 3aeb5aca3471cb544119405ee163708b |
cc-by-4.0 | ['int8', 'Intel® Neural Compressor', 'PostTrainingStatic'] | false | Load with optimum: ```python from optimum.intel.neural_compressor.quantization import IncQuantizedModelForQuestionAnswering int8_model = IncQuantizedModelForQuestionAnswering.from_pretrained( 'Intel/roberta-base-squad2-int8-static', ) ``` | f7a0684a6086c4867f9628e08b6eba58 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_qqp_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.4568 - Accuracy: 0.7910 - F1: 0.7234 - Combined Score: 0.7572 | 14ceda07f66aa65eda995229396fc36a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.5339 | 1.0 | 1422 | 0.5031 | 0.7551 | 0.6484 | 0.7018 | | 0.4835 | 2.0 | 2844 | 0.4866 | 0.7650 | 0.6504 | 0.7077 | | 0.4587 | 3.0 | 4266 | 0.4792 | 0.7694 | 0.6422 | 0.7058 | | 0.4369 | 4.0 | 5688 | 0.4851 | 0.7745 | 0.6716 | 0.7230 | | 0.4155 | 5.0 | 7110 | 0.4705 | 0.7791 | 0.6970 | 0.7380 | | 0.3961 | 6.0 | 8532 | 0.4633 | 0.7858 | 0.7093 | 0.7476 | | 0.3772 | 7.0 | 9954 | 0.4572 | 0.7908 | 0.7176 | 0.7542 | | 0.3593 | 8.0 | 11376 | 0.4568 | 0.7910 | 0.7234 | 0.7572 | | 0.3422 | 9.0 | 12798 | 0.4661 | 0.7927 | 0.7227 | 0.7577 | | 0.3265 | 10.0 | 14220 | 0.4596 | 0.7983 | 0.7290 | 0.7636 | | 0.3119 | 11.0 | 15642 | 0.4635 | 0.7977 | 0.7255 | 0.7616 | | 0.2961 | 12.0 | 17064 | 0.4857 | 0.8008 | 0.7309 | 0.7659 | | 0.2831 | 13.0 | 18486 | 0.4987 | 0.8037 | 0.7314 | 0.7676 | | 346c45d0e81c53c36677ef17c6bc776a |
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: 0.7809 - Matthews Correlation: 0.5286 | 3a77cade122545fd02dfe641d6a9ed0b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5299 | 1.0 | 535 | 0.5040 | 0.4383 | | 0.3472 | 2.0 | 1070 | 0.5284 | 0.4911 | | 0.2333 | 3.0 | 1605 | 0.6633 | 0.5091 | | 0.1733 | 4.0 | 2140 | 0.7809 | 0.5286 | | 0.1255 | 5.0 | 2675 | 0.8894 | 0.5282 | | 78eeefc1eb170324e382fa6716acf3b0 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | Examples <img src="https://cdn.openart.ai/uploads/image_1675448197954_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675411612740_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675196635672_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1674581722334_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1674987795511_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1674932237434_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1673903295569_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1674064743430_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1673727870966_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1673979519921_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675283643707_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675277243663_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675018609128_1024.jpg" style="max-width: 600px;" width="100%"/> More examples: https://openart.ai/@raudemer_enchanting_8k | 1ea0ac846750b81c03e2bc6d96ca456b |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | rMadaArt UI: Requires AUTOMATIC1111 Stable Diffusion Webui --api (https://github.com/AUTOMATIC1111/stable-diffusion-webui) <img src="https://cdn.openart.ai/uploads/image_1675183856117_1024.jpg" style="max-width: 800px;" width="100%"/> https://www.youtube.com/watch?v=47OjMczhBpM&t=416s https://www.youtube.com/watch?v=o7hrptahjvI | 3cbcef27cc1fe65c799e230d297547b6 |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | Atmosphere and fine tunning variations https://www.youtube.com/watch?v=M_0DRfESzks <img src="https://cdn.openart.ai/uploads/image_1675514080826_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675515489063_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675514369201_1024.jpg" style="max-width: 600px;" width="100%"/> <img src="https://cdn.openart.ai/uploads/image_1675524026464_1024.jpg" style="max-width: 600px;" width="100%"/> | 11daa7dd73a0dcd6dc2c0b8f316cbcc6 |
apache-2.0 | salesken | false | This model evaluates the wellformedness (non-fragment, grammatically correct) score of a sentence. Model is case-sensitive and penalises for incorrect case and grammar as well. ['She is presenting a paper tomorrow','she is presenting a paper tomorrow','She present paper today'] [[0.8917],[0.4270],[0.0134]] ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("salesken/query_wellformedness_score") model = AutoModelForSequenceClassification.from_pretrained("salesken/query_wellformedness_score") sentences = [' what was the reason for everyone to leave the company ', ' What was the reason behind everyone leaving the company ', ' why was everybody leaving the company ', ' what was the reason to everyone leave the company ', ' what be the reason for everyone to leave the company ', ' what was the reasons for everyone to leave the company ', ' what were the reasons for everyone to leave the company '] features = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` | 91c0a4bcc39f41730b5df43f3f90f9a5 |
mit | ['generated_from_keras_callback'] | false | nlu_sherlock_model_20220220 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: | cb9bdbc6fc6718fc215ddf8453919340 |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -955, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 | 30d55cf4de47e065ee922b76e93df6fe |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/bart-base-squad-qag` This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) for question & answer pair generation task on the [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 5bfff39364a00f94cd97716cdf5995e0 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [facebook/bart-base](https://huggingface.co/facebook/bart-base) - **Language:** en - **Training data:** [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) | 122522ef0777706c893a248f209453ec |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-qag") output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` | 0213e9e17e4f61edfee495827ce8b912 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 84.49 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedF1Score (MoverScore) | 57.46 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (BERTScore) | 85.64 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedPrecision (MoverScore) | 60.01 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (BERTScore) | 83.38 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | QAAlignedRecall (MoverScore) | 55.26 | default | [lmqg/qag_squad](https://huggingface.co/datasets/lmqg/qag_squad) | | e8ebb3e0206f4aa0fc8ef5a4eaa242af |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_squad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/bart-base - max_length: 512 - max_length_output: 256 - epoch: 2 - batch: 16 - lr: 1e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 8 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-squad-qag/raw/main/trainer_config.json). | e734b8e0287df36540305e5e6738b7f7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | 892f51e2b6f059a941e64663fec31095 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | all-mpnet-base-v1 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 0e697a0f3a078d92d5c47a1d54faebf4 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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 example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-mpnet-base-v1') embeddings = model.encode(sentences) print(embeddings) ``` | 309fb83b0cc3df5f32a586ae60e07130 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | 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/all-mpnet-base-v1) ------ | c13f716d62dff6919661d3d8b3d1fa7b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. | 39d1ec10acbd9975d0e8bc83330f7093 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 128 word pieces is truncated. | 6aa2a779b222b9ef8e61ade90b69b6d7 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Pre-training We use the pretrained [`microsoft/mpnet-base`](https://huggingface.co/microsoft/mpnet-base). Please refer to the model card for more detailed information about the pre-training procedure. | 3f0c1c9d37462cf6033de183e4f9281c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | Hyper parameters We trained ou model on a TPU v3-8. We train the model during 920k steps using a batch size of 512 (64 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. | 16f20f7466daae509e3709a4b3a95d75 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/ | 47d6b5fe17d283b33432fa176578b5d5 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity'] | false | home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,124,818,467** | | b178f4a991ce17628f587ca77f4c5071 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_vp-nl_s833 Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 85f79a06d43d4201704c044924b8a5f2 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1328 - Accuracy: 0.9699 | a312c8637d44c67b8de78409d22d56a9 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | 6135bea8a80a4cab6826a632d98f4d24 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.49 | 1.0 | 65 | 0.9624 | 0.4050 | | 0.2769 | 2.0 | 130 | 0.9850 | 0.1862 | | 0.1441 | 3.0 | 195 | 0.9774 | 0.1554 | | 0.1661 | 4.0 | 260 | 0.9774 | 0.1333 | | 0.1754 | 5.0 | 325 | 0.9699 | 0.1328 | | 5cc2ad0f45157f719271efbec3fa7776 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | En-Nso_update2 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4199 - Bleu: 24.4776 | 29fc2db3c63eb041216acceffccd87d6 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 | ef910fce52ad5109e5651f4faccc2dab |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 3.6661 | 1.0 | 865 | 3.0081 | 17.6871 | | 2.7495 | 2.0 | 1730 | 2.7725 | 20.1475 | | 2.4533 | 3.0 | 2595 | 2.6433 | 22.5433 | | 2.3203 | 4.0 | 3460 | 2.5625 | 22.9963 | | 2.1356 | 5.0 | 4325 | 2.5190 | 23.5696 | | 2.0258 | 6.0 | 5190 | 2.4881 | 23.8367 | | 1.9481 | 7.0 | 6055 | 2.4641 | 24.0611 | | 1.8769 | 8.0 | 6920 | 2.4526 | 24.3214 | | 1.8211 | 9.0 | 7785 | 2.4392 | 24.5300 | | 1.7689 | 10.0 | 8650 | 2.4307 | 24.4627 | | 1.7314 | 11.0 | 9515 | 2.4254 | 24.4936 | | 1.7 | 12.0 | 10380 | 2.4243 | 24.4673 | | 1.6695 | 13.0 | 11245 | 2.4202 | 24.5613 | | 1.6562 | 14.0 | 12110 | 2.4200 | 24.4886 | | 1.6446 | 15.0 | 12975 | 2.4199 | 24.4711 | | 20a7fe8e5b12f31ab9bc52f0ffefb3af |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-ft-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0824 - Accuracy: 0.9826 | b54c3b87ca692d51347215e381b031ff |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 0 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 - mixed_precision_training: Native AMP | f76b83e18a05253eeb507353817df9a2 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8972 | 1.0 | 399 | 0.7023 | 0.8174 | | 0.3274 | 2.0 | 798 | 0.1634 | 0.9773 | | 0.1993 | 3.0 | 1197 | 0.1048 | 0.9788 | | 0.1777 | 4.0 | 1596 | 0.0824 | 0.9826 | | 0.1527 | 5.0 | 1995 | 0.0812 | 0.9810 | | f29839450da1903e680840d923ccd63f |
gpl-3.0 | ['generated_from_trainer'] | false | Description - The dataset consists of 148 Filipino storytelling books, 5,005 total sentences, 45,792 total tokens, and 5,646 unique tokens. - This NER model only supports the Filipino language and does not include proper nouns, verbs, adjectives, and adverbs as of the moment - The input must undergo preprocessing. Soon I will upload the code to GitHub for preprocessing the input - To replicate the preprocessed input use this example as a guide - Input: "May umaapoy na bahay " - Preprocessed Input: "apoy bahay" | 8751173b9050ed4be69a4cccaed7a953 |
gpl-3.0 | ['generated_from_trainer'] | false | bert-tagalog-base-uncased-ner-v1 This model is a fine-tuned version of [jcblaise/bert-tagalog-base-uncased](https://huggingface.co/jcblaise/bert-tagalog-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2824 - Precision: 0.9091 - Recall: 0.8988 - F1: 0.9039 - Accuracy: 0.9488 | 9d8f5b6b804991f170e4cedd4fb867ff |
gpl-3.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 205 | 0.5311 | 0.6465 | 0.5458 | 0.5919 | 0.8387 | | No log | 2.0 | 410 | 0.3052 | 0.7736 | 0.7811 | 0.7774 | 0.9110 | | 0.4693 | 3.0 | 615 | 0.2531 | 0.8493 | 0.8363 | 0.8427 | 0.9319 | | 0.4693 | 4.0 | 820 | 0.2384 | 0.8755 | 0.8715 | 0.8735 | 0.9402 | | 0.064 | 5.0 | 1025 | 0.2671 | 0.8909 | 0.8823 | 0.8866 | 0.9435 | | 0.064 | 6.0 | 1230 | 0.2527 | 0.8864 | 0.8920 | 0.8892 | 0.9459 | | 0.064 | 7.0 | 1435 | 0.2708 | 0.9088 | 0.9011 | 0.9049 | 0.9491 | | 0.0111 | 8.0 | 1640 | 0.2733 | 0.8992 | 0.8977 | 0.8984 | 0.9490 | | 0.0111 | 9.0 | 1845 | 0.2765 | 0.8991 | 0.8965 | 0.8978 | 0.9485 | | 0.0037 | 10.0 | 2050 | 0.2824 | 0.9091 | 0.8988 | 0.9039 | 0.9488 | | c1324d5c5827a431f2b90043401440b0 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.7758 - Accuracy: 0.92 | 1517ab36010c55210e0580edbe387faa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 4.295 | 1.0 | 318 | 3.2908 | 0.7448 | | 2.6313 | 2.0 | 636 | 1.8779 | 0.8384 | | 1.5519 | 3.0 | 954 | 1.1600 | 0.8981 | | 1.0148 | 4.0 | 1272 | 0.8585 | 0.9123 | | 0.7974 | 5.0 | 1590 | 0.7758 | 0.92 | | 313dd1e8f4ae8a3cf00fcdfc9b913450 |
creativeml-openrail-m | ['text-to-image'] | false | seif-1_5 Dreambooth model trained by HusseinHE 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/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! Sample pictures of: skseif (use that on your prompt) | 298a8fef696b5e7770306b95eb9eccb9 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-mi This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8606 | ff66bff250888eb742a8bf8926ee576f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1069 | 1.0 | 97 | 2.3524 | | 2.1677 | 2.0 | 194 | 1.9426 | | 1.9197 | 3.0 | 291 | 2.0536 | | c096c18f4eb3890272b9af7ab7517daf |
mit | ['azbert', 'pretraining', 'fill-mask'] | false | About Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31,061). This model is trained on 4 x 2 Tesla V100 with a total batch size of 64, using Math StackExchange data with 2.7 million sentence pairs trained for 7 epochs. | 87c789d67423a833142083b95e64ef2e |
mit | ['azbert', 'pretraining', 'fill-mask'] | false | Usage Download and try it out ```sh pip install pya0==0.3.2 wget https://vault.cs.uwaterloo.ca/s/gqstFZmWHCLGXe3/download -O ckpt.tar.gz mkdir -p ckpt tar xzf ckpt.tar.gz -C ckpt --strip-components=1 python test.py --test_file test.txt ``` | cbad895a02021b8a45d947ed10d6c6d7 |
mit | ['azbert', 'pretraining', 'fill-mask'] | false | Test file format Modify the test examples in `test.txt` to play with it. The test file is tab-separated, the first column is additional positions you want to mask for the right-side sentence (useful for masking tokens in math markups). A zero means no additional mask positions. | 1532f8b1c398e13498c5b059dba9c47b |
mit | ['azbert', 'pretraining', 'fill-mask'] | false | Upload to huggingface This repo is hosted on [Github](https://github.com/approach0/azbert), and only mirrored at [huggingface](https://huggingface.co/castorini/azbert-base). To upload to huggingface, use the `upload2hgf.sh` script. Before runnig this script, be sure to check: * check points for model and tokenizer are created under `./ckpt` folder * model contains all the files needed: `config.json` and `pytorch_model.bin` * tokenizer contains all the files needed: `added_tokens.json`, `special_tokens_map.json`, `tokenizer_config.json`, `vocab.txt` and `tokenizer.json` * no `tokenizer_file` field in `tokenizer_config.json` (sometimes it is located locally at `~/.cache`) * `git-lfs` is installed * having git-remote named `hgf` reference to `https://huggingface.co/castorini/azbert-base` | 7e870dffeeb923dbbe11028220afdf63 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-seed-42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 1.4364 | 15d30002ab5211a168f6809da879e9b6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1937 | 1.0 | 8235 | 1.2350 | | 0.9256 | 2.0 | 16470 | 1.3129 | | 0.7489 | 3.0 | 24705 | 1.4364 | | cf48a4de86e26cc0c9df9d6ac2ef4e2a |
apache-2.0 | ['translation'] | false | opus-mt-fi-gil * source languages: fi * target languages: gil * OPUS readme: [fi-gil](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-gil/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-gil/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-gil/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-gil/opus-2020-01-08.eval.txt) | 9d27f1a0f3e5a629c3e7115249babd22 |
mit | [] | false | Cat toy on Stable Diffusion This is the `<cat-toy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:     | 70b1fb41d9c7ac795c9ac26e8822ffc1 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_5 This model is a fine-tuned version of [husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4](https://huggingface.co/husnu/wav2vec2-large-xls-r-300m-turkish-colab_common_voice-8_4) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 - Wer: 0.3634 | eb858fa0112d4d8f1ed9a54a3e03eea9 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP | 24dcd3811c1c8605de6d2e606db28ba4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1243 | 0.51 | 400 | 0.4312 | 0.4202 | | 0.1956 | 1.02 | 800 | 0.4421 | 0.4498 | | 0.1816 | 1.53 | 1200 | 0.4012 | 0.4285 | | 0.1548 | 2.04 | 1600 | 0.3720 | 0.3845 | | 0.1171 | 2.55 | 2000 | 0.3439 | 0.3634 | | cea15870b18065edb275dda7497a5432 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-effectiveness-dagstuhl This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6418 - Accuracy: 0.6190 | 8168a75537b5d772decbcd8e70e67456 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 16 | 0.6729 | 0.5714 | | No log | 2.0 | 32 | 0.6418 | 0.6190 | | No log | 3.0 | 48 | 0.6719 | 0.5556 | | No log | 4.0 | 64 | 0.6386 | 0.6032 | | No log | 5.0 | 80 | 0.6559 | 0.5714 | | 0ea754f7e8a30e56af506217dbd348d7 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'lora'] | false | LoRA DreamBooth - simbatheoglion These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "a photo of simbatheog" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. Test prompt: A photo of simbatheog in a bucket     | bec9aa2f8b86f216edc36f3765708831 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HY-AM dataset. It achieves the following results on the evaluation set: - Loss: **0.4521** - Wer: **0.5141** - Cer: **0.1100** - Wer+LM: **0.2756** - Cer+LM: **0.0866** | 95bb563c72546a624144f89793176a5e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: tristage - lr_scheduler_ratios: [0.1, 0.4, 0.5] - training_steps: 1400 - mixed_precision_training: Native AMP | a7cca5ce591b3f7ebe24564d4605bb23 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 6.1298 | 19.87 | 100 | 3.1204 | 1.0 | 1.0 | | 2.7269 | 39.87 | 200 | 0.6200 | 0.7592 | 0.1755 | | 1.4643 | 59.87 | 300 | 0.4796 | 0.5921 | 0.1277 | | 1.1242 | 79.87 | 400 | 0.4637 | 0.5359 | 0.1145 | | 0.9592 | 99.87 | 500 | 0.4521 | 0.5141 | 0.1100 | | 0.8704 | 119.87 | 600 | 0.4736 | 0.4914 | 0.1045 | | 0.7908 | 139.87 | 700 | 0.5394 | 0.5250 | 0.1124 | | 0.7049 | 159.87 | 800 | 0.4822 | 0.4754 | 0.0985 | | 0.6299 | 179.87 | 900 | 0.4890 | 0.4809 | 0.1028 | | 0.5832 | 199.87 | 1000 | 0.5233 | 0.4813 | 0.1028 | | 0.5145 | 219.87 | 1100 | 0.5350 | 0.4781 | 0.0994 | | 0.4604 | 239.87 | 1200 | 0.5223 | 0.4715 | 0.0984 | | 0.4226 | 259.87 | 1300 | 0.5167 | 0.4625 | 0.0953 | | 0.3946 | 279.87 | 1400 | 0.5248 | 0.4614 | 0.0950 | | 1f29e0a3fbe45f4a2b2a0118fd799ebb |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_stsb This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 1.1918 - Pearson: 0.1864 - Spearmanr: 0.1859 - Combined Score: 0.1862 | 455132772124ed261d9d5c4aa06eb656 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 1.7465 | 1.0 | 45 | 1.2026 | 0.0588 | 0.0666 | 0.0627 | | 1.079 | 2.0 | 90 | 1.4599 | 0.0595 | 0.0691 | 0.0643 | | 1.0784 | 3.0 | 135 | 1.2063 | 0.0611 | 0.0707 | 0.0659 | | 0.9943 | 4.0 | 180 | 1.3534 | 0.0730 | 0.0730 | 0.0730 | | 0.9523 | 5.0 | 225 | 1.3943 | 0.1080 | 0.1010 | 0.1045 | | 0.8379 | 6.0 | 270 | 1.1918 | 0.1864 | 0.1859 | 0.1862 | | 0.7217 | 7.0 | 315 | 1.2542 | 0.2080 | 0.2144 | 0.2112 | | 0.6304 | 8.0 | 360 | 1.2209 | 0.1920 | 0.1979 | 0.1950 | | 0.5573 | 9.0 | 405 | 1.2925 | 0.1881 | 0.1814 | 0.1847 | | 0.5048 | 10.0 | 450 | 1.3943 | 0.1731 | 0.1877 | 0.1804 | | 0.4754 | 11.0 | 495 | 1.3058 | 0.1845 | 0.1817 | 0.1831 | | b9d88e88f6adb1c9501ce69dd566a7a4 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the pochita concept trained by Arch4ngel on the Arch4ngel/pochita_v2 dataset. This is a Stable Diffusion model fine-tuned on the pochita concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of pochita plushie** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! | 4cc54ff2a8d06c0b9a4b1ee4e4a65560 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2745 - Accuracy: 0.9346 | 4480aedab3804a6738c63249b4c507a3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1778 | 1.0 | 4210 | 0.3553 | 0.9060 | | 0.1257 | 2.0 | 8420 | 0.2745 | 0.9346 | | 0.0779 | 3.0 | 12630 | 0.3272 | 0.9300 | | 0.0655 | 4.0 | 16840 | 0.3412 | 0.9323 | | 0.0338 | 5.0 | 21050 | 0.3994 | 0.9300 | | 25a6c99afed030ae6bb4617dcc300e21 |
apache-2.0 | ['translation'] | false | opus-mt-fi-ilo * source languages: fi * target languages: ilo * OPUS readme: [fi-ilo](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ilo/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-ilo/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ilo/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-ilo/opus-2020-01-08.eval.txt) | 8543f70075293c511ecf027f26ed84d3 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.0001_pretrainedTrue_epochs1 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 2.8742 | 5515fe0b1c1d6187bae077665498ae1c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cloud1-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Precision: 0.9714 - Recall: 0.9855 - F1: 0.9784 - Accuracy: 0.9972 | 69a42234986ceaceab104318ec6e0c88 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 166 | 0.0160 | 0.9653 | 0.9420 | 0.9535 | 0.9945 | | No log | 2.0 | 332 | 0.0089 | 0.9623 | 0.9855 | 0.9737 | 0.9965 | | No log | 3.0 | 498 | 0.0074 | 0.9714 | 0.9855 | 0.9784 | 0.9972 | | 05326a07c1ff75066a6b06982226ae9c |
mit | [] | false | Model miniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency. Since miniALBERT is a compact model, it is trained using a layer-to-layer distillation technique, using the bert-base model as the teacher. Currently, this model is trained for one epoch on the English subset of Wikipedia. In terms of architecture, this model uses an embedding dimension of 128, a hidden size of 768, an MLP expansion rate of 4, and a reduction factor of 16 for bottleneck adapters. In general, this model uses 6 recursions and has a unique parameter count of 11 million parameters. | 3553276f2daf8ca6fde66b5300a141da |
mit | [] | false | For Sequence Classification use the below code model = MiniAlbertForTokenClassification.from_pretrained("nlpie/miniALBERT-128") ``` In addition, For efficient fine-tuning using the pre-trained bottleneck adapters use the below code: ```Python model.trainAdaptersOnly() ``` | 901c245c8ec62a114f8fdb361e3b243f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_qnli_256 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6564 - Accuracy: 0.6030 | 012ac9a001e6630795bfc371ab423d6e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.679 | 1.0 | 410 | 0.6614 | 0.5938 | | 0.6496 | 2.0 | 820 | 0.6564 | 0.6030 | | 0.6268 | 3.0 | 1230 | 0.6635 | 0.5978 | | 0.6055 | 4.0 | 1640 | 0.6714 | 0.5933 | | 0.5836 | 5.0 | 2050 | 0.6964 | 0.5913 | | 0.5602 | 6.0 | 2460 | 0.7319 | 0.5832 | | 0.5385 | 7.0 | 2870 | 0.7653 | 0.5718 | | 1642b8eb6f16836b9932795b803b5863 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 4 - mixed_precision_training: Native AMP | 772909d371a78a98ac08c476c31a7ce9 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-samsum-ElectrifAi_v3 This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8053 - Rouge1: 62.0348 - Rouge2: 41.9592 - Rougel: 49.1046 - Rougelsum: 59.4965 - Gen Len: 101.2747 | db6194c56ef74006429461a16ab664bf |
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