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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mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2651 - F1: 0.8355 | be7922389680d525525bcc77a86ed5db |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5954 | 1.0 | 191 | 0.3346 | 0.7975 | | 0.2689 | 2.0 | 382 | 0.2900 | 0.8347 | | 0.1821 | 3.0 | 573 | 0.2651 | 0.8355 | ... | fb333b01f29ab276c080dad435bbee24 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in English Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice). When using this model, make sure t... | 11237fdac48ec3d7da80a618d2c4ed3f |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'en', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-english, title={Fine-tuned {XLSR}-53 large model for speech recognition in {E}nglish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}}, year={2... | 85037d71df1a3c32ea59b6062eccc329 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-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: 0.4779 - Wer: 0.3453 | e3caa5e5e9c35442a7547643a81e1e05 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4307 | 4.0 | 500 | 1.4129 | 0.9980 | | 0.626 | 8.0 | 1000 | 0.4605 | 0.4499 | | 0.2199 | 12.0 | 1500 | 0.4457 | 0.3898 | |... | 2b168032950c52394e2957c83ff2c5a6 |
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: 3.6652 | 5c33011e6a45928c12249483ac6a3c64 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.9109 | 1.0 | 584 | 3.6956 | | 3.7555 | 2.0 | 1168 | 3.6712 | | 3.7002 | 3.0 | 1752 | 3.6652 | | eb7f839c1d3481df3c775f79629c06b7 |
apache-2.0 | ['generated_from_trainer'] | false | bert-tiny-finetuned-xglue-ner This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the xglue dataset. It achieves the following results on the evaluation set: - Loss: 0.2489 - Precision: 0.6308 - Recall: 0.6681 - F1: 0.6489 - Accuracy: ... | 26cb79006343a05cc67b91701b11f8c9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.4082 | 1.0 | 1756 | 0.3326 | 0.5600 | 0.5798 | 0.5697 | 0.9118 | | 0.2974 | 2.0 |... | 2d99ad2d61d8355c1b74bf7b8b8deb8f |
mit | [] | false | YB Anime on Stable Diffusion This is the `<anime-character>` 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 a... | fcba532151973baea816b278b9a5ab7b |
cc-by-4.0 | [] | false | Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of Catalan-Spanish datasets, up to 92 million sentences. Additionally, the model is evaluated on several public datasecomprising 5 different domains (general, adminstrative, te... | 7795a95f7cc24b1ba95632664fd4d3fb |
cc-by-4.0 | [] | false | Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/mt-aina-ca-es", revision="main") tokenizer=pyonmttok.Tokenizer(m... | e6775b681e1f9d3a8fbbf485b0f25784 |
cc-by-4.0 | [] | false | Training data The was trained on a combination of the following datasets: | Dataset | Sentences | Tokens | |-------------------|----------------|-------------------| | DOCG v2 | 8.472.786 | 188.929.206 | | El Periodico | 6.483.106 | 145.591.906 | | EuroP... | 8831daa1cb0d7d719e86ba450ebc17b8 |
cc-by-4.0 | [] | false | Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparamenters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|---... | 69352965c1b8edc456f2d596ae01d225 |
cc-by-4.0 | [] | false | Evaluation results Below are the evaluation results on the machine translation from Catalan to Spanish compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | mt-aina-ca-es | |----------------------|---... | 5ec0ed911be59fd5025baec2b5d89086 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | Roberta | 110M | 自然语言推理 NLI | | 9af8381b0cd0eade00c88ad74fcc6e40 |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 模型信息 Model Information 基于[chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext)，我们在收集的4个中文领域的NLI（自然语言推理）数据集，总计1014787个样本上微调了一个NLI版本。 Based on [chinese-roberta-wwm-ext-base](https://huggingface.co/hfl/chinese-roberta-wwm-ext), we fine-tuned an NLI version on 4 Chinese Natural Language Infe... | 299c1c95d5a3cb5a0d8875d7731a573e |
apache-2.0 | ['roberta', 'NLU', 'NLI', 'Chinese'] | false | 使用 Usage ``` python from transformers import BertForSequenceClassification from transformers import BertTokenizer import torch tokenizer=BertTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-NLI') model=BertForSequenceClassification.from_pretrained('IDEA-CCNL/Erlangshen-Roberta-110M-NLI') texta='今天的饭不好吃'... | 76b571bfc127450c6d8bdfd31926c65d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4170 - Wer: 0.4282 | 570cb998b4e06c27f3a1cba601d4a28f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | e9386cfb1347c4c4d9b28bdf9fe407c3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.7049 | 0.8 | 200 | 3.0234 | 0.9683 | | 2.9496 | 1.6 | 400 | 2.9348 | 0.9683 | | 2.6582 | 2.4 | 600 | 1.2843 | 0.9818 | |... | 6a5de14e0cad48ef13d36ed7944bff42 |
mit | ['generated_from_trainer'] | false | poem-gen-spanish-t5-small-d2 This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9027 | 5a7309fbe6f35c12f61a40de614d8f3b |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 | 5f83028ba19c558a93702a18fbc4d1a0 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 3.223 | 0.73 | 30000 | 3.1479 | | 3.0109 | 1.46 | 60000 | 3.0544 | | 2.8649 | 2.19 | 90000 | 2.9730 | | 2.7603 | 2.93 | 120000 | 2... | 56a9fa3609aefce95b13a3d4cec0c8ca |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-hi-wx1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6552 - Wer: 0.3200 Evaluation Commands ... | 437e2fe7a25f2fcb173274f9b9bd3c81 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - 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_sc... | 16ae1813dfc8df68e92b4879886b65b5 |
apache-2.0 | ['automatic-speech-recognition', 'hf-asr-leaderboard', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.2663 | 1.36 | 200 | 5.9245 | 1.0 | | 4.1856 | 2.72 | 400 | 3.4968 | 1.0 | | 3.3908 | 4.08 | 600 | 2.9970 | 1.0 | |... | 398e4a8ddba8a09ac6ca3218e5b6bcb7 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-IMDB 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: 0.1905 - Accuracy: 0.9295 | b537ff4f835bc7af3386da5a726afe3b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1928 | 1.0 | 2000 | 0.1905 | 0.9295 | | 25c7f9785785faf50f12a7be6370d657 |
creativeml-openrail-m | ['text-to-image'] | false | It can be used by modifying the `instance_prompt`: **ari** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`:... | 6f20fb9512ee3ad6afccdcf3034a7e5a |
apache-2.0 | ['bert', 'sst2', 'glue', 'torchdistill'] | false | `bert-large-uncased` fine-tuned on SST-2 dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb). The hyperparameters are the same as th... | 82753cc0b7d81c9934149c53ad64f604 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-analysis-en This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0792 - Accuracy: 0.9803 - F1: 0.9856 - Precision: 0.9875 - Recall: 0.9837 | 89e6a5301a27255a5d0f2d3e9b61cfa6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.426 | 1.0 | 1408 | 0.2718 | 0.8910 | 0.9201 | 0.9251 | 0.9151 | | 0.3247 | 2.0 |... | 41bf9785aacc4f1217f7cec496d29abe |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-torgo-demo-m02-nolm This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0260 - Wer: 0.4968 | 4c111ab712515b12309b3506c3a8498c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.4385 | 0.91 | 500 | 4.0160 | 1.0 | | 3.0413 | 1.81 | 1000 | 3.2881 | 1.0 | | 3.0011 | 2.72 | 1500 | 3.2401 | 1.0 ... | d9d5095328e90678a0c3352dc5cf4261 |
cc-by-sa-4.0 | ['asteroid', 'audio', 'DPRNNTasNet', 'audio-to-audio'] | false | Asteroid model `JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k` Description: This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid). It was trained on the `enh_single` task of the Libri1Mix dataset. Training config: ```yml data: n_src: 1 samp... | 937501e25f9370fe87e05b396a547589 |
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: 0.5657 - Matthews Correlation: 0.5470 | ddf0f6b101175d3df1761cc327874a43 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.521 | 1.0 | 535 | 0.5159 | 0.4152 | | 0.3445 | 2.0 | 1070 | 0.4905 | 0.5022 | | 0.2... | 41b75a9120fc32eb53b256f9ffccf0c5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small hi- HYDDCSEZ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.6357 - Wer: 18.7986 | bd0892f52d0c87aac787addbca9cb60a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0037 | 14.01 | 1000 | 0.4715 | 19.1786 | | 0.0001 | 28.01 | 2000 | 0.5589 | 18.5377 | | 0.0001 | 43.01 | 3000 | 0.6008 | 18.590... | cb3919ad013cbaf822929bfaf1d9086d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | pythia-6.9b-deduped for general QA <a href="https://colab.research.google.com/gist/pszemraj/351f04fc2afb6346c763885f127284ef/pythia-6-9b-deduped-for-general-qa.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> This model is a fine-tuned version of [EleutherAI/py... | 05675c92d9cfbc730802d058454a96ea |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Usage Install necessary packages for inference (_unless you have a big boi GPU_) ```bash pip install -U -q transformers bitsandbytes accelerate ``` Basic inference example: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/pythia-6.9b-HC3") ... | 208d676adb8d2da6c72dd6d1bbad5b23 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | shards are ~4GB each, there are eight total prompt = "I was wondering how much wood a woodchuck could chuck? <answer>" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate( **inputs, max_new_tokens=300 ) | b6632983815fa20bd793243905cf15d6 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | default generation config (+ 300 tokens) result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] result = result.split("<end_answer>")[0].strip() import pprint as pp pp.pprint(result) ``` The defautl `GenerationConfig` uses contrastive search with `top_k=4` and `penalty_alpha=0.6`. For more informatio... | bb2fa7df9891e490f6893f17ae054d5d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Intended uses & limitations - **Intended use:** research/exploration into comparing RLHF tuning vs. "guided"/specific tuning on "quality" datasets/responses of _"what the human would want as answer anyway"_ - This is **not** trained/fine-tuned with RLHF and therefore will not be as helpful/generalizable/safe as chatG... | 7cc2b35a0e83d1e1589362b43201dfd8 |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Training and evaluation data ```yaml model-index: - name: pythia-6.9b-hc3-qa-assistant results: - task: name: Causal Language Modeling type: text-generation dataset: name: pszemraj/HC3-textgen-qa metrics: - name: Accuracy type: accuracy value: 0.6768941789814655 ``` | 991ef5e74ecb7b631db55df82ceb091d |
apache-2.0 | ['generated_from_trainer', 'HC3', 'chatGPT', 'assistant'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2598 | 0.99 | 79 | 1.3291 | 0.6496 | | 0.7446 | 1.99 | 158 | 1.2372 | 0.6769 | | 76cfca513f5f902202bc5734cffedc40 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 | 17a15f5da81f4c33be2ed7a86ea41417 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3962 | 1.0 | 18050 | 3.3250 | | 3.2561 | 2.0 | 36100 | 3.2652 | | 3.1727 | 3.0 | 54150 | 3.2572 | | 406cff7225149c526e4284efbfe36669 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-whole-word-word-ids-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.6573 | 33404189ea823ba9553c75386797248f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7261 | 1.0 | 157 | 0.6532 | | 0.6766 | 2.0 | 314 | 0.6514 | | 0.6677 | 3.0 | 471 | 0.6555 | | 3813ce00fe2e63d53319cfdf5b08aa18 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | IT5 Base for Informal-to-formal Style Transfer 🧐 This repository contains the checkpoint for the [IT5 Base](https://huggingface.co/gsarti/it5-base) model fine-tuned on Informal-to-formal style transfer on the Italian subset of the XFORMAL dataset as part of the experiments of the paper [IT5: Large-scale Text-to-text... | 18c729ec00c8338d1de3365d904a4914 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'style-transfer', 'formality-style-transfer'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines i2f = pipeline("text2text-generation", model='it5/it5-base-informal-to-formal') i2f("nn capisco xke tt i ragazzi lo fanno") >>> [{"gener... | 8c9540c44530ee3b128a64686825dba1 |
cc-by-sa-4.0 | [] | false | This is a model for correcting spelling and grammar errors in Icelandic text. It is based on the pretrained ByT5 model (https://arxiv.org/abs/2105.13626) and finetuned on Icelandic error correction data along with synthetic error data. The model is trained using the HuggingFace and PyTorch libraries. The model is tra... | 5a355d9098dadffc3bd1dab5227984cc |
mit | ['generated_from_trainer'] | false | deberta-v3-base-finetuned-imdb This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 3.0016 | 624530b66d491b5bd5a3169fe04a8cb3 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.2666 | 1.0 | 6690 | 3.4001 | | 3.3574 | 2.0 | 13380 | 3.1174 | | 3.1715 | 3.0 | 20070 | 3.0034 | | 4e0c3e7ffbfccef476e7366a3cefbc0c |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-wikitext2-test-mlm This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8438 | b99d81469ed6fa3f6fd626e178b5ff23 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - total_eval_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and ... | 954116cb72064b6dc4f11195da25a0f2 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3097 - Accuracy: 0.8633 - F1: 0.8647 | dd45ff1ca97e7b6fe6a1163a6716fb43 |
mit | ['vision', 'image-to-text', 'image-captioning', 'visual-question-answering'] | false | BLIP-2, OPT-2.7b, fine-tuned on COCO BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv... | 7e73350f20221d16ee6f720037a32b7b |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 663380; padding-top:0px;" span title="Protogen x5.3 Raw Output"></center> <center><h1>Protogen x5.3 (Photorealism) Official Release</h1></center> <center><p><em>Research Model by <a href="https://instagram.com/officialvictorespinoza">darkstorm2150</a></em></p></center> </div> | 3a22bb12ffd1a7842e00bf3b9f046ce1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | General info Protogen x5.3 - One Step Closer to Reality by [darkstorm2150](https://instagram.com/officialvictorespinoza) Protogen was warm-started with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and continued fine-tuned from [darkstorm2150/Protogen_x3.4_Official_Release](https://h... | ac58c839834caf615878e67cef232354 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | Granular Adaptive Learning Granular adaptive learning is a machine learning technique that focuses on adjusting the learning process at a fine-grained level, rather than making global adjustments to the model. This approach allows the model to adapt to specific patterns or features in the data, rather than making ass... | c5bc22bff3ab3b16bb37536026cca5ba |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | CKPT [Download ProtoGen x5.3.ckpt (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_v5.3_Official_Release/blob/main/ProtoGen_X5.3.ckpt) [Download ProtoGen x5.3-pruned-fp16.ckpt (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.3-pruned-fp16.ckpt) | 6ba4609ed3a796dadff680ca7c4a08ad |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | Safetensors [Download ProtoGen x5.3.safetensors (4.27GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.3.safetensors) [Download ProtoGen x5.3-pruned-fp16.safetensors (1.89GB)](https://huggingface.co/darkstorm2150/Protogen_x5.3_Official_Release/resolve/main/ProtoGen_X5.... | e111623c17fd7b7e384c91aa2543340a |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). ```python from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler impor... | 257e0a1711a10bc023629cdfd4c85566 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | 663380; } </style> <table class="myTable"> <tr> <th>Models</th> <th>Protogen v2.2 (Anime)</th> <th>Protogen x3.4 (Photo)</th> <th>Protogen x5.3 (Photo)</th> <th>Protogen x5.8 (Sci-fi/Anime)</th> <th>Protogen x5.9 (Dragon)</th> <th>Protogen x7.4 (Eclipse)</th> <th>Protogen x8.0 (Nova)</th> <th>Protogen x8.6 (Infinity)<... | 75580006fc16f5b85dac904b6f11f4f3 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers', 'protogen'] | false | License By downloading you agree to the terms of these licenses <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">CreativeML Open RAIL-M</a> <a href="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md">Dreamlike License</a> <a href="https://huggingface.co/corec... | aebb0bc6198605d4af48a6c02cf1e45a |
mit | ['generated_from_trainer'] | false | roberta-base_mnli_bc This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.2125 - Accuracy: 0.9584 | ce5a261605e1f2bff65df8619f0a790c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2015 | 1.0 | 16363 | 0.1820 | 0.9470 | | 0.1463 | 2.0 | 32726 | 0.1909 | 0.9559 | | 0.0768 | 3.0 | 49089 | 0.2117 ... | 6e7e2350d1b5557bf370d2ff85cbc762 |
apache-2.0 | [] | false | Training Data This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. | caac9c8c4cc66f0fe09a0185e300ceed |
apache-2.0 | [] | false | Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and... | 2301e7bcbbdf6b3e0add56b8023846fd |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.1698 - Mae: 0.6090 | 0ecc67868930b5507bfc29faf90998d8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1662 | 1.0 | 333 | 1.2084 | 0.7068 | | 1.0122 | 2.0 | 666 | 1.1698 | 0.6090 | | c67abb113776801446ac628f7cc1e6dc |
mit | [] | false | Toho-pixel on Stable Diffusion This is the `<toho-pixel>` 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... | da7dbc6a0bde6df8eedca85e8a241b45 |
apache-2.0 | ['generated_from_trainer'] | false | littledataset This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 | ac7330a477ce730f70a833065f91fe1c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 169 | 0.0001 | | No log | 2.0 | 338 | 0.0000 | | 0.0036 | 3.0 | 507 | 0.0001 | | 0.0036 | 4.0 | 676 | 0.0000 ... | 11027173939671ff1460c3e8cc19a82a |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_xls-r_gender_male-5_female-5_s896 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure t... | fdd14278da48235b5997eb8029afd296 |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on COCO instance segmentation (large-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/M... | 5cd9317e6870479c9c0fa6d9d23f8230 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on COCO instance segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-large-coco-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-large-coco-instance") url = "http://images.cocodataset.org/val2017/0000000397... | 52b4c27137b231d842b89ec4385b5389 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Persian V2 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 55c54a16d99c48b067c4dc7533f5f3ba |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | requirement packages !pip install git+https://github.com/huggingface/datasets.git !pip install git+https://github.com/huggingface/transformers.git !pip install torchaudio !pip install librosa !pip install jiwer !pip install hazm ``` **Prediction** ```python import librosa import torch import torchaudio from transfor... | a1f67c7db445e3cc9d23b2758d535b61 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | In case of farsi chars_to_ignore = chars_to_ignore + list(string.ascii_lowercase + string.digits) chars_to_mapping = { 'ك': 'ک', 'دِ': 'د', 'بِ': 'ب', 'زِ': 'ز', 'ذِ': 'ذ', 'شِ': 'ش', 'سِ': 'س', 'ى': 'ی', 'ي': 'ی', 'أ': 'ا', 'ؤ': 'و', "ے": "ی", "ۀ": "ه", "ﭘ": "پ", "ﮐ": "ک", "ﯽ": "ی", "ﺎ": "ا", "ﺑ": "ب", "... | 391772096f3e1bbe4024988176f59686 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | "ها": " ها", "ئ": "ی", "a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ", "g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ", "m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ", "s": " اس ", "t": " تی ", "u": " ی... | 4fbeb63457186c93a67b212df9a94e83 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Persian (Farsi) test data of Common Voice. ```python import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import numpy as np import hazm import re import string ... | b8fa9293110f0346c4ff298dd22531e1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | "ها": " ها", "ئ": "ی", "a": " ای ", "b": " بی ", "c": " سی ", "d": " دی ", "e": " ایی ", "f": " اف ", "g": " جی ", "h": " اچ ", "i": " آی ", "j": " جی ", "k": " کی ", "l": " ال ", "m": " ام ", "n": " ان ", "o": " او ", "p": " پی ", "q": " کیو ", "r": " آر ", "s": " اس ", "t": " تی ", "u": " ی... | 3a6b1265945d8a87f5ec3a278a45bb69 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. You can see the training states [here](https://wandb.ai/m3hrdadfi/finetuned_wav2vec_xlsr_persian/reports/Fine-Tuning-for-Wav2Vec2-Large-XLSR-53-Persian--Vmlldzo1NjY1NjU?accessToken=pspukt0liicopnwe93wo1ipetqk0gzkuv8669g00wc6hcesk1fh0rfkb... | cbe50885d0f69606e03100791e610c55 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-Breton Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Breton Common Voice dataset](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz. | 7dfe979c8458b8ac51eaf6e0d2fdd53f |
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 import re test_dataset = load_dataset("common_voice", "br", split="test[:2%]") processor = Wav2Vec2Processo... | 57cda3928c87d3f887d00ded9c2715a8 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " batch["sentence"] = batch["sentence"].replace("ʼ", "'") batch["sentence"] = batch["sentence"].replace("’", "'") batch["sentence"] = batch[... | cb83d9a36a53d15f4aaa1bb54526cd36 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the Breton 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", "br", split="test") wer =... | 2be03aede43125bf49fcc3465bb211c7 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " batch["sentence"] = batch["sentence"].replace("ʼ", "'") batch["sentence"] = batch["sentence"].replace("’", "'") batch["sentence"] = batch[... | 954d8d0a728302b921a359f8a3b7d528 |
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... | 5521f7ed5ff64ba94099cf6cd47f0cfa |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-marc-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.9575 - Mae: 0.5488 | 2edf6cb8d9b297906ce9229865babefd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1253 | 1.0 | 235 | 0.9960 | 0.5366 | | 0.9708 | 2.0 | 470 | 0.9575 | 0.5488 | | da5f89d79af1d3e74095a7371cd43d1b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/gtr-t5-xl
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of sematic search.
This model was converted from the Tensorflow model [gtr-xl-1](https://tfhub.de... | c9fcdc7da8d325a18c4594734c71e32a |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an... | e0c57405ab18761664eaf84bd58efb88 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/gtr-t5-xl)
| 3044bca41a5b11f03a8b77b9c555189a |
mit | ['roberta-base', 'roberta-base-epoch_71'] | false | RoBERTa, Intermediate Checkpoint - Epoch 71 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly ... | 4d99004b59e2444684c522fc0be95122 |
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