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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creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Chinese Digital Art Diffusion **Trigger Words: CNDigitalArt Style** This is a fine-tuned Stable Diffusion model trained on some of the **Chinese Digital Arts** style that usually uses on Chinese Interactive Reading (Visual Novel) platforms such as **Orange Light** [66rpg.com](https://66rpg.com) or **NetEase Interact... | 7bf21751272789432c2aa42be445e70e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Lithuanian-ALKSNIS | Feature | Description | | --- | --- | | **Name** | `lt_udv25_lithuanianalksnis_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exp... | 8c65efc69c7e62e2d3598e8efff6166b |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (3674 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `.`, `akr.`, `bdv.aukšt.mot.dgs.K.`, `bdv.aukšt.mot.dgs.V.`, `bdv.aukšt.mot.dgs.Vt.`, `bdv.... | e35cffd4723593ee80cc1c6784f513c5 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.96 | | `TOKEN_P` | 99.94 | | `TOKEN_R` | 99.98 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 95.65 | | `SENTS_P` | 96.84 | | `SENTS_R` | 94.49 | | `TAG_ACC` | 95.43 | | `POS_ACC` | 98.07 | | `MORPH_ACC` | 95.50 | | `DEP_UAS` | 88.11 | | `DEP_LAS` | 83.62 | | `LEMMA_... | 1199a4ea0792abd149c6f394d11160d0 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy:... | 31f8d59d00b4eb614e73de71b96be1a9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-digits This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0605 - Accuracy: 0.9846 | 4cd762fa31bf363f2a93eafdc260fb62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4808 | 1.0 | 620 | 0.3103 | 0.9696 | | 0.1877 | 2.0 | 1240 | 0.1043 | 0.9791 | | 0.1478 | 3.0 | 1860 | 0.0727 | 0.... | c8070499196bc3f6f3b8da8389ac55e4 |
apache-2.0 | ['generated_from_trainer'] | false | qnli This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.9138 | 22e91c26a37bc6eb058ad84c14f2ecb0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | 500371e9e2073711b5aec790830e9169 |
apache-2.0 | [] | false | Model description
The REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
| 740d3e62cfa2674fd4c04965a614551c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Swedish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice). The training data amounts to 402 MB. When using this model, make sure that your speech input is sampled... | 1fd4925c3caf5a004dc808bb3a1d77a2 |
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", "sv-SE", split="test[:2%]"). processor = Wav2Vec2Processor.fr... | 339e8b3aacf6d1e63686d9deb05667f9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sv-SE", split="test... | 07fcda392888878714ad47232f061167 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logi... | 43087675ab748c1c7bce65bed6444365 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1KkD4PeZwnIwxxxOP1bUE7XTZMK7-SzRj?usp=sharing) | 1c6bfbb18b297e789dbeb4f649ddbece |
apache-2.0 | ['generated_from_trainer'] | false | SEED0042 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1754 - Accuracy: 0.9507 | 3c13b04c4b84f241cb2517eaa4638403 |
apache-2.0 | ['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: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - n... | 247fdf6c025a0afbc3c4c8cb6e5e7f16 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 2105 | 0.2056 | 0.9358 | | 0.2549 | 2.0 | 4210 | 0.1850 | 0.9438 | | 0.1162 | 3.0 | 6315 | 0.1754 | 0.... | 391fca8f87439049e99d2134410837ef |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Precision: 0.9301 - Recall: 0.9376 - F1: 0.9338 - Accuracy: 0.9852 | bedd8eca2d1c4c591dfdcfc9552c0aba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 | | 0.0481 | 2.0 |... | e86c15ce55741b13532ca8d08637f99d |
mit | [] | false | Description A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes ([MIMIC-III](https://mimic.physionet.org/)). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs and biomedical papers. We hope this model can serve better results on clini... | 3d8677761a7ca47698bb325fb42aea14 |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | wav2vec 2.0 base model pretrained on librispeech 960h This HuggingFace repository provides all the necessary tools to extract wav2vec2 embeddings from a pretrained model. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The wav2vec2 model has entirely been pr... | 6289d4c0c76c449a8f3650a80b679afb |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Pipeline description This w2v2 system is composed of 2 different but linked blocks: - A convolutional backend to extract features from the raw waveform. - A latent encoder made of a transformer network. The obtained embeddings are the output of the transformer after going through each block. | 0caf4b6007d791ba2498d88765b4b9f6 |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Extracting embeddings for your own audio files ```python from speechbrain.pretrained import WaveformEncoder ssl_model = WaveformEncoder.from_hparams(source="speechbrain/ssl-wav2vec2-base-librispeech", savedir="speechbrain/ssl-wav2vec2-base-librispeech") ssl_model.encode_file("mywavfile.wav") ``` | d4c090ab65cfecb7d3c32630fc5983cf |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LibriSpee... | f74b29e93dc17bd60446bb9209de512e |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and... | 9652655d3a77562fc0563ff40ac12032 |
apache-2.0 | ['argumentation'] | false | Generate the conclusion of an argument This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of M... | f8febbfb4e75e521bafd92c8fc8aca1e |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2 This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training data: data.retriever.nq-adv-hn-train(facebookresearch/DPR). It achieves the following results on the evaluation set: | e3d3563c850239d6eeb1155e941cf35b |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | Evaluation data evaluation dataset: facebook-dpr-dev-dataset from official DPR github |model_name|data_name|num of queries|num of passages|R@10|R@20|R@50|R@100|R@100| |---|---|---|---|---|---|---|---|---| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our)|nq-dev dataset|6445|199795|60.53%|68.28%|76.07%|80.9... | 5417faa81ae81cb907a7b0bda93b9f96 |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | Usage (HuggingFace Transformers) ```python passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2") query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2") p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ct... | 2d8039f50ae6c25add6118dd331f7c6b |
mit | [] | false | HD Emoji on Stable Diffusion This is the `<HDemoji-object>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can al... | ad56818671abb27d5aab99b4300f0cdd |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-libirClean-vs-commonNative-en This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 2.3358 - Wer: 85.5379 | fe25334b49c545d7b30b62a3c5ad284f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 50 - mixed_precision_t... | 6056646906995f64277c770a75a736b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.2481 | 0.08 | 10 | 3.5688 | 21.1895 | | 0.7793 | 0.16 | 20 | 2.8307 | 38.9990 | | 0.5443 | 0.24 | 30 | 2.4196 | 67.045... | b20238477af73342e92cbb6de7bf6752 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | plng_f222 Dreambooth model trained by andylive 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-di... | 1adb3765d9f6fa28d48f93eefdbef3ca |
mit | [] | false | Liminal spaces 2.0 on Stable Diffusion This is the `liminal image` 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... | 35e11a62cdb91ab552e7d08596fc023b |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-squad2-ta-qna-roberta3e This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4671 | 26ccb4c8c0a0d864bee87a4125ea0cd6 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 42942f4d5fcb70ee04744f426071d5e9 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 87 | 0.5221 | | No log | 2.0 | 174 | 0.4408 | | No log | 3.0 | 261 | 0.4671 | | ca6b3a74bd73430da4dda587e4652814 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-uncased-label_sexist 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: 0.0519 - F1: 0.9825 | 96a89fab8ef4034ed85cd6f7a5966d9f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4448 | 0.57 | 400 | 0.3022 | 0.8232 | | 0.3283 | 1.14 | 800 | 0.2306 | 0.8786 | | 0.2588 | 1.71 | 1200 | 0.1721 | 0.9149 | |... | d82e52f090be8bd6aec1ec27f8d7f126 |
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: 1.1453 | c9eb6dc9d71a1bf8f53f13c460cee924 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2065 | 1.0 | 5577 | 1.1289 | | 0.9226 | 2.0 | 11154 | 1.1019 | | 0.7411 | 3.0 | 16731 | 1.1453 | | f1b295431a9d4159170dfb744cfb9aaa |
mit | ['generated_from_keras_callback'] | false | nandysoham16/Catalan_language-clustered This model is a fine-tuned version of [nandysoham16/13-clustered_aug](https://huggingface.co/nandysoham16/13-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6225 - Train End Logits Accuracy: 0.8646 - Train Start Log... | c8100b258d65f0e571a9a44fd5377f97 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | c7795627551888b6859ee32ac4f1f42d |
apache-2.0 | ['generated_from_keras_callback'] | false | tw-sentiment-finetuned This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2039 - Train Accuracy: 0.9171 - Validation Loss: 0.4805 - V... | 21a6fec8243dead68e8126c974865a24 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4620 | 0.7977 | 0.3893 | 0.8332 | 0 | | 0.3238 | 0.8596 | 0.4674 | 0.8362 ... | 7e5ff72d04bb13d20e9e2f0aafc9c6c7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 4 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsil... | 72cf936fde0577fb550fa704a5466c64 |
apache-2.0 | ['generated_from_trainer'] | false | w2v2-xlsr-indonesian This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2496 - Wer: 0.2116 | 5c0fee926821afc298a465a5d97f57ff |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | 9615c12e577576d56c111241a69d1cfe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.828 | 3.23 | 100 | 3.3112 | 1.0 | | 2.99 | 6.45 | 200 | 2.8944 | 1.0 | | 2.8597 | 9.68 | 300 | 2.8430 | 1.0 | |... | 4b5c9c4db16ae43286f480be1513def6 |
apache-2.0 | ['translation'] | false | eng-cus * source group: English * target group: Cushitic languages * OPUS readme: [eng-cus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md) * model: transformer * source language(s): eng * target language(s): som * model: transformer * pre-processing: normalization + Sente... | 083b333048195b70bebefe9980991c76 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-cus - source_languages: eng - target_languages: cus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'so', 'cus'] - src_constituents: {'eng'} - tg... | 6e6b74b98e861fecc6349288ee23ebbb |
apache-2.0 | [] | false | Enformer Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enf... | 1720bd42a75563097282ee886c997900 |
mit | [] | false | model by Mizuzora This your the Stable Diffusion model fine-tuned the Fang yuan-002 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **an anime art of sks Fang_Yuan** You can also train your own concepts and upload them to the library by using [this notebook](http... | e7b072c931e84b8d1df72384af53b3c0 |
apache-2.0 | ['generated_from_trainer'] | false | test-trainer 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: 0.5802 - Accuracy: 0.8505 - F1: 0.8935 | 656e879fc021c8d4955247b94d4e11e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.4443 | 0.8039 | 0.8485 | | 0.5584 | 2.0 | 918 | 0.3841 | 0.8431 | 0.8810 | | 0.3941 |... | 26a1a0291e4c7f882b800b54d8331493 |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-google-ds-imdb This model is a fine-tuned version of [jestemleon/bert-nlp-project-google](https://huggingface.co/jestemleon/bert-nlp-project-google) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2646 - Accuracy: 0.9455 - F1: 0.9446 | 84c2d86c3354fb7cb2b8a22d5cad446f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2684 | 0.38 | 750 | 0.1850 | 0.9315 | 0.9295 | | 0.2105 | 0.75 | 1500 | 0.1734 | 0.9407 | 0.9385 | | 0.1767 |... | 136796fc45a9f3197ab4986ef28bbb7b |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-100k_s364 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make s... | b95a801b3f2f8522f3905bcdd20f9ace |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_qnli_192 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: 0.6649 - Accuracy: 0.5949 | c9ce91cb66af93f7f7de7134eba0a0f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 1.0 | 410 | 0.6930 | 0.5054 | | 0.6793 | 2.0 | 820 | 0.6684 | 0.5823 | | 0.6511 | 3.0 | 1230 | 0.6650 | 0.... | 13eaf76d99f5e0b5a47741e1a60bd49e |
apache-2.0 | [] | false | Details of byt5-is-ocr-post-processing-modern-texts *Note: This model is almost the same as [atlijas/byt5-is-ocr-post-processing-old-texts](https://huggingface.co/atlijas/byt5-is-ocr-post-processing-old-texts/). The only difference is the amount of epochs trained.* This model generates a revised version of a given I... | 5932d5fb069731e8a207d556d6e91e80 |
apache-2.0 | [] | false | Evaluation results The test set for this model consists of various Icelandic texts from the the 80's and 90's. On it, the model achieves a chrF error rate reduction of 30.1%, with the original text's score being 95.2, and the processed one's 96.7. The model achieves a proportional BLEU improvement of 19.8%, with the o... | c0764db55ccd89aa0ba178d66040c3b0 |
apache-2.0 | ['translation'] | false | opus-mt-af-sv * source languages: af * target languages: sv * OPUS readme: [af-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 52418547c22450601be3507299796af2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6862 - Validation Loss: 0.8050 - Epoch: 2 | 6af4ddf6581aa101fc0cc39df483a821 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0615 | 0.8832 | 0 | | 0.7983 | 0.8211 | 1 | | 0.6862 | 0.8050 | 2 | | 300161f4734eb9cac235608b6238adcb |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-rte This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8377 - Accuracy: 0.6390 | 585670051f571398707b4d06d6151703 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4103 | 6.41 | 500 | 1.1929 | 0.6318 | | 0.0583 | 12.82 | 1000 | 2.3816 | 0.6029 | | 0.0263 | 19.23 | 1500 | 2.7340 | 0.... | 01c65a81438e822262cdb7567cb80b1c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4604023/ This model was trained by Emiru Tsunoo using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | df47a048bccaf70f37ff0e2f0894d474 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-eurosat 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 imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3394 - Accuracy: 0.9802 | b67ad68690dd8de7189a078aa354e97d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7076 | 0.98 | 33 | 0.6119 | 0.9696 | | 0.4469 | 1.98 | 66 | 0.4190 | 0.9788 | | 0.3497 | 2.98 | 99 | 0.3555 | 0.... | a812fa5b004ff3fde7a745a009cb2115 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_r-wav2vec2_s957 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) 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 speec... | 5d5acb6cd9a0f8eed2ba84c3f4894596 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | SI-SNR Estimator introduced for the REAL-M dataset This repository provides the Separator models to be able to train a blind SI-SNR estimator with the recipe provided in the SpeechBrain repository to complement the REAL-M real-life speech separation dataset. The SI-SNR estimator is trained with a recordings from th... | ae7cad9b07ea906895ea8898883d6965 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Install SpeechBrain First of all, currently you need to install SpeechBrain: ```bash pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | f0d478e6fae6b4b9a7edbde40a06b743 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Training The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd... | 11a53240a01009817ae88ca59dac252d |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Referencing REAL-M ```bibtex @misc{subakan2021realm, title={REAL-M: Towards Speech Separation on Real Mixtures}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and François Grondin}, year={2021}, eprint={2110.10812}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```... | 0c19264cff3dffd177e02a2310899a60 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5155 - Wer: 0.3388 | 52a052cba2771e330993feab01cccf6f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5822 | 1.0 | 500 | 2.4127 | 1.0 | | 0.9838 | 2.01 | 1000 | 0.5401 | 0.5363 | | 0.4308 | 3.01 | 1500 | 0.4380 | 0.459... | b9bfc5240375b7dab11c8eac3f805382 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Te - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2455 - Wer: 42.6485 | de196edc76ae750657ca4e2a6c9f2ddc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6341 | 0.1 | 500 | 0.3894 | 60.7108 | | 0.349 | 0.2 | 1000 | 0.3081 | 52.0935 | | 0.2792 | 0.3 | 1500 | 0.2874 | 49.707... | 9d1e17760186c5fba3a5583337b5a9c7 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0171 - Rouge1: 16.778 - Rouge2: 8.0849 - Rougel: 16.5329 - Rougelsum: 16.4302 | 0793bd37afe020ad3b82a7b6867c8129 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.4297 | 1.0 | 1209 | 3.1211 | 17.6479 | 8.1669 | 17.1554 | 17.0276 | | 3.4217 | 2.0 |... | 3d4cbfdc0ded89c2e673635fdb3b8144 |
apache-2.0 | [] | false | bert-base-fr-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the sam... | 1d514e2ca3aace99f4cd830c8a93f479 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-fr-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-fr-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https:/... | 158cb9624da8dfddce1ccb359be6828e |
cc-by-sa-4.0 | ['long-documents'] | false | Disclaimer 🚧 ⚠️ This is an experimental version of HAT, trying to make HAT a native part of Transformers library. Please use ONLY [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096) for the moment. | be2991c92ce1498a67f8e5e6d68033ec |
openrail | [] | false | Usage DialoGPT small version, finetuned on Morty's sequences (Rick and Morty Cartoon character). Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-small-morty') model... | 8fdef385b5ae2b02facd077fc2ef6aad |
mit | [] | false | galah on Stable Diffusion This is the `<galah>` 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 you... | 2022248b6e708c5e6350a32e23642dd4 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_r-wav2vec2_s44 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your ... | 97d6d1c14b5deb30d422a0bbdb48fcfb |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aaureeliaav1 Dreambooth model trained by akahnn 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-d... | 327872ae11e6052058080dced98f5d36 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | yelanlan Dreambooth model trained by jiaheillu Sample pictures of this concept:   on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.9982 - Pearson: 0.2057 - Spearmanr: 0.2115 - Combined Score: 0.2086 | 86a3bdb661ace299c6c1f7a7af371395 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 0.7165 | 1.0 | 1259 | 2.9982 | 0.2057 | 0.2115 | 0.2086 | | 0.1449 | 2.0 | 2518 ... | 8afbb49fb94324363959623e0a60e609 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | wav2vec2-xls-r-300m-tr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3179 - Wer: 0.2863 - Cer: 0.0681 | d4a593c9a944f5f3bfc12caf368593aa |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_... | b40bdcb8afe5291057ab294e51f61cdb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | No log | 0.71 | 400 | 1.7290 | 0.9804 | 0.4797 | | 4.5435 | 1.42 | 800 | 0.4810 | 0.5774 | 0.1450 | | 0.523 | 2.1... | 6116ee108ae7fa4ae0c89c152e0992a7 |
apache-2.0 | ['whisper-event'] | false | Whisper Telugu Small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Telugu data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 3e8dd121a365d52bbbb02cdc7c43e465 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15000 - training_steps: 26856 (terminated upon convergence. Initially ... | 9266451da076959bea22d541428e9611 |
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: 1.6908 | f05d3721b7df42e98b78611e8f3fbf54 |
mit | [] | false | Dancing cactus on Stable Diffusion This is the `<dancing-cactus>` 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 ... | e0355049698aa98fd2d2ea5849d18e19 |
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