license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 | 4893826ce410f104347587726ae95269 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4988 | 1.0 | 535 | 0.6503 | 0.4420 | | 0.3345 | 2.0 | 1070 | 0.5180 | 0.5756 | | 0.2... | 04c280449062f894f7523800286ceac1 |
apache-2.0 | ['generated_from_trainer'] | false | <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/> | 2ca9e7d8405294851b59ba116970f045 |
apache-2.0 | ['generated_from_trainer'] | false | This model is used for french summarization - Problem type: Summarization - Model ID: 980832493 - CO2 Emissions (in grams): 0.10685501288084795 This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM](https://huggingface.co/Chemsseddine/bert2gpt2SUMM) on the None dataset. It achieves the following results o... | b61f3b76e9af830a40924f418290aac4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 33199 | 4.03749 | 28.8384 | 10.7511 | 27.0842 | 27.5118 | 22... | 86f23127e39845b722d1760db109e8e7 |
mit | [] | false | Hello! This is a 14,000 step trained model based on the famous Where's Waldo / Where's Wally art style. (I'm American so I named the style Waldo, if you're familiar with Wally instead, my apologies!) The keyword to invoke the style is "Wheres Waldo style" and I've found it works best when you use it in conjunction w... | 4bd484ad518fa99500b8de33311161c1 |
apache-2.0 | ['generated_from_trainer'] | false | question-paraphraser This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the adamlin/question_augmentation dataset. It achieves the following results on the evaluation set: - Loss: 3.5901 - Rouge1: 0.5385 - Rouge2: 0.0769 - Rougel: 0.5586 - Rougelsum: 0.5586 - Gen Len:... | 57c14bb2a2a983973413eede7feab7ad |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | 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 the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 2.3557 - Bleu: 37.1286 | 36221b8c9699f977fa09198d0c91b1ff |
apache-2.0 | ['SEAD'] | false | Abstract With the widespread use of pre-trained language models (PLM), there has been increased research on how to make them applicable, especially in limited-resource or low latency high throughput scenarios. One of the dominant approaches is knowledge distillation (KD), where a smaller model is trained by receiving... | 31d27091d787488f96d05254249a0f7b |
apache-2.0 | ['SEAD'] | false | SEAD-L-6_H-256_A-8-sst2 This is a student model distilled from [**BERT base**](https://huggingface.co/bert-base-uncased) as teacher by using SEAD framework on **sst2** task. For weights initialization, we used [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) | 95efccf57af94dc074e5ec03471f1ca6 |
apache-2.0 | ['SEAD'] | false | Evaluation results | eval_accuracy | eval_runtime | eval_samples_per_second | eval_steps_per_second | eval_loss | eval_samples | |:-------------:|:------------:|:-----------------------:|:---------------------:|:---------:|:------------:| | 0.9266 | 1.3676 | 637.636 | 20.475 ... | fc96a18155f95fbba15163aca2e0a141 |
apache-2.0 | ['SEAD'] | false | Framework versions - Transformers >=4.8.0 - Pytorch >=1.6.0 - TensorFlow >=2.5.0 - Flax >=0.3.5 - Datasets >=1.10.2 - Tokenizers >=0.11.6 If you use these models, please cite the following paper: ``` @article{article, author={Mei, Moyan and Sroch, Rohit}, title={SEAD: Simple Ensemble and Knowledge Dis... | b0a6ff639ad5a76409e1d6d7d6f0fa0b |
apache-2.0 | ['generated_from_trainer'] | false | results This model is a fine-tuned version of [linydub/bart-large-samsum](https://huggingface.co/linydub/bart-large-samsum) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.0158 | 7d8d17b2926f35743332da3c61ffa65d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 0.9563 | | No log | 2.0 | 2 | 0.9877 | | No log | 3.0 | 3 | 1.0158 | | 45ea35d59b70eced8cef95b0804d594f |
apache-2.0 | ['generated_from_trainer'] | false | my_first_model This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6853 - Accuracy: 0.6 | f1622795e30ecc6a83f20ddce31c0ab5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6918 | 1.0 | 23 | 0.6895 | 0.8 | | 0.7019 | 2.0 | 46 | 0.6859 | 0.6 | | 0.69 | 3.0 | 69 | 0.6853 | 0.... | 4a15d31c6fb355ad6204210726998cb7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | LH_miki_v1 Dreambooth model trained by asukii 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-dif... | 31490920ae23793fda991d9a6c69a65a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-TIMIT-IPA This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3130 - Per: 0.0550 | f341633e061acf551d7c4f3d58c3de31 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Per | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.3003 | 6.85 | 500 | 3.8093 | 0.9424 | | 1.7151 | 13.7 | 1000 | 0.2929 | 0.0708 | | 0.2212 | 20.55 | 1500 | 0.2259 | 0.0575 | |... | be5adf28a2a7b390bf1d91754ba10bb5 |
apache-2.0 | ['translation'] | false | opus-mt-de-gaa * source languages: de * target languages: gaa * OPUS readme: [de-gaa](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-gaa/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 7b566bea5ec1eb1a02ff60c0560baea5 |
mit | [] | false | Xuna on Stable Diffusion This is the `<Xuna>` 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 ... | 7268045394db7d0d58fa85f0896cb40d |
mit | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Utilizing These weights are intended to be used with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/CompVis/stabilityai/sd-vae-ft-ema). | 211f7842ce61ca8e4464102b408202f6 |
mit | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/laion-aesthetics/) and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enri... | 7b22a9426f2d83eaf032e1776508ca23 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.4-2 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 3.6725 - Bleu: 1.5399 - Gen Len: 88.995 | 22d45538b8f9f806a736e5d18e7b15d4 |
apache-2.0 | [] | false | Model Card for mT5-base-HunSum-1 The mT5-base-HunSum-1 is a Hungarian abstractive summarization model, which was trained on the [SZTAKI-HLT/HunSum-1 dataset](https://huggingface.co/datasets/SZTAKI-HLT/HunSum-1). The model is based on [google/mt5-base](https://huggingface.co/google/mt5-base). | 6d8b16af8e230785b38d00286bcd2d14 |
apache-2.0 | [] | false | Intended uses & limitations - **Model type:** Text Summarization - **Language(s) (NLP):** Hungarian - **Resource(s) for more information:** - [GitHub Repo](https://github.com/dorinapetra/summarization) | e7e3b511bb812f1bee6c070685f27bbd |
apache-2.0 | [] | false | Parameters - **Batch Size:** 12 - **Learning Rate:** 5e-5 - **Weight Decay:** 0.01 - **Warmup Steps:** 3000 - **Epochs:** 10 - **no_repeat_ngram_size:** 3 - **num_beams:** 5 - **early_stopping:** False - **encoder_no_repeat_ngram_size:** 4 | 31564c1e56864396a8d034559fff2728 |
apache-2.0 | [] | false | Results | Metric | Value | | :------------ | :------------------------------------------ | | ROUGE-1 | 37.70 | | ROUGE-2 | 11.22 | | ROUGE-L | 24.37 ... | dc1e3a4dfdcce156b4bb93140169a0da |
apache-2.0 | [] | false | Citation If you use our model, please cite the following paper: ``` @inproceedings {HunSum-1, title = {{HunSum-1: an Abstractive Summarization Dataset for Hungarian}}, booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)}, year = {2023}, publisher = {Szegedi Tudományegyetem, Informatikai ... | 05a9039f3cdda7e1b79d7ecbcb97f13a |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2186 - Accuracy: 0.924 - F1: 0.9241 | 725f01cde0a90fdd31ebc612188846c2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8218 | 1.0 | 250 | 0.3165 | 0.9025 | 0.9001 | | 0.2494 | 2.0 | 500 | 0.2186 | 0.924 | 0.9241 | | aea5011e13b6dbf5911079688e201769 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'kmr', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | wav2vec2-large-xls-r-300m-kurdish 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 - KMR dataset. It achieves the following results on the evaluation set: - Loss: 0.2548 - Wer: 0.2688 | 90bf2489a87498a0a9aded4eee936f43 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'kmr', 'model_for_talk', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.3161 | 12.27 | 2000 | 0.4199 | 0.4797 | | 1.0643 | 24.54 | 4000 | 0.2982 | 0.3721 | | 0.9718 | 36.81 | 6000 | 0.2762 | 0.333... | ff5167e357d1c7238b5585297eecaf42 |
apache-2.0 | ['generated_from_trainer'] | false | test_trainer This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 1.0183 - Accuracy: 0.586 | 56579eba0bb6e0c5985941efac9ceba1 |
apache-2.0 | ['translation'] | false | opus-mt-ar-en * source languages: ar * target languages: en * OPUS readme: [ar-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ar-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 00ca7dca2b10202c9a8701f4554c0f6a |
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: 3.6423 | ef3de32a47600d65ec5abdff2ec9fa87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7602 | 1.0 | 2334 | 3.6669 | | 3.633 | 2.0 | 4668 | 3.6455 | | 3.6078 | 3.0 | 7002 | 3.6423 | | 4960f6d8d8bbdc02dcac8d06d2b81021 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | | Release | Test WER | GPUs | |:-------------:|:--------------:| :--------:| | 22-05-11 | - | 1xK80 24GB | after 9 epochs training - valid %WER: 4.09e+02 after 12 epochs training - valid %WER: 2.07e+02, test WER: 1.78e+02 | 634182528686cba745c67e0172524f6c |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Pipeline description (by SpeechBrain text) This ASR system is composed with 3 different but linked blocks: - Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. - Neural language model (RNNLM) trained on the full (380K) words dataset. - Acoustic model... | de707b69d7d3e0ac71c7540a03031ab9 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Install SpeechBrain First of all, please install SpeechBrain with the following command: ``` pip install speechbrain ``` Please notice that SpeechBrain encourage you to read tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | 084207ea1dc3a75ccc610292747584fa |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in Russian) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="AndyGo/speechbrain-asr-crdnn-rnnlm-buriy-audiobooks-2-val", savedir="pretrained_models/speech-brain-asr-crdnn-rnnlm-buriy-audiobooks-2-val") asr_model.transc... | ea14c8c64a17e7476408da9045b3e0c8 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Russian Speech Datasets Russian Speech Datasets are provided by Microsoft Corporation with CC BY-NC license. Instructions by downloading - https://github.com/snakers4/open_stt The CC BY-NC license requires that the original copyright owner be listed as the author and the work be used only for non-commercial purposes W... | e0c4ea4e86252bb0d95a3c9ed1f27338 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'pytorch', 'speechbrain'] | false | Citing SpeechBrain Please, cite SpeechBrain if you use it for your research or business. @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawa... | 5edfe945d7dbd49f6b3091f6d7e802f3 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/paraphrase-albert-base-v2 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. | c67ce925377af850fa99348ebe095066 |
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 example sen... | e71de8606d608ba296a7d4993095244d |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-albert-base-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-albert-base-v2') | 8d364edc0c2ba67a515aee96ffc55c38 |
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/paraphrase-albert-base-v2) | b97bfcfae9f266c4a9aa440b3b38435c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: AlbertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_m... | 4dd433453715824ebe2521425758ba3e |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | the same as the other one execpt with built-in support for genshin impact characters <center><img src="https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters/resolve/main/img/5.jpg" width="512" height="512"/></center> ![visitors](https://visitor-badge.glitch.me/badge?page_id... | 38c8b8fd71143beac1dd8d54e6d5ba79 |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a beautif... | ca8f32daf0ddd51be6eb0d6ea95dc57a |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | Online Demo You can try the Online Web UI demo build with [Gradio](https://github.com/gradio-app/gradio), or use Colab Notebook at here: *My Online Space Demo* [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c6473... | af8d3750970a61087b35bd9ee4c6de96 |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | **👇Model👇** AI Model Weights available at huggingface: https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters <center><img src="https://huggingface.co/AdamOswald1/Cyberpunk-Anime-Diffusion_with_support_for_Gen-Imp_characters/resolve/main/img/2.jpg" width="512" height="512"... | d747fa5422fececbf25b304e3240719c |
creativeml-openrail-m | ['cyberpunk', 'anime', 'stable-diffusion', 'aiart', 'text-to-image', 'TPU'] | false | Usage After model loaded, use keyword **dgs** in your prompt, with **illustration style** to get even better results. For sampler, use **Euler A** for the best result (**DDIM** kinda works too), CFG Scale 7, steps 20 should be fine **Example 1:** ``` portrait of a girl in dgs illustration style, Anime girl, female... | c2d6ca52fac2783525c89e32ec0a9e93 |
mit | ['generated_from_keras_callback'] | false | sachinsahu/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: 0.5775 - Train End Logits Accuracy: 0.8368 - Train Start Logit... | 7847e75cc1eff3bdd6a3dbbc496c57e5 |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 8de4a21eef1f11ad099866308bd65627 |
apache-2.0 | ['generated_from_trainer'] | false | fine-tuned-ai-ss-hs-01 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - AUC: 0.88609 - Precision: 0.8514 - Accuracy: 0.8101 - F1: 0.7875 - Recall: 0.7326 | 7046d6dcfc89ffaf34a48ca93a03a374 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.1207606211860595e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 7bb33ff17720375b82712e04c61bfa81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 357 | 1.0285 | 0.6955 | 0.5657 | 0.8987 | 0.4128 | | 0.5857 | 2.0 |... | 7e629ef8384caf60b1a1a648eb2c7d40 |
apache-2.0 | ['generated_from_trainer'] | false | pneumonia1 This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1400 - Accuracy: 0.9519 | 6e1ddf3081edd36bda2c3fabf2ac6d96 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 163 | 0.3493 | 0.8429 | | No log | 2.0 | 326 | 0.1928 | 0.9423 | | No log | 3.0 | 489 | 0.1373 | 0.... | ca87ef4e20530805ce185869cd91b8f3 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Longformer Encoder-Decoder (LED) for Narrative-Esque Long Text Summarization <a href="https://colab.research.google.com/gist/pszemraj/36950064ca76161d9d258e5cdbfa6833/led-base-demo-token-batching.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> - **What:** Thi... | 52bac38334dcaa190cd35b152c435118 |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | About - Trained on the BookSum dataset released by SalesForce (this is what adds the `bsd-3-clause` license) - Trained for 16 epochs vs. [`pszemraj/led-base-16384-finetuned-booksum`](https://huggingface.co/pszemraj/led-base-16384-finetuned-booksum), - parameters adjusted for _very_ fine-tuning type training (supe... | 405fcd2eee1e7bf233658a8d045db60e |
['apache-2.0', 'bsd-3-clause'] | ['summarization', 'led', 'summary', 'longformer', 'booksum', 'long-document', 'long-form'] | false | Usage - Basics - it is recommended to use `encoder_no_repeat_ngram_size=3` when calling the pipeline object to improve summary quality. - this param forces the model to use new vocabulary and create an abstractive summary otherwise it may l compile the best _extractive_ summary from the input provided. - create the... | 4c779b8c03bd0624572838f71babf35f |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-vi-infovqa 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: 3.5470 | e7f06f9827013297ee5b4fbe9732b079 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.21 | 100 | 4.2058 | | No log | 0.43 | 200 | 4.0210 | | No log | 0.64 | 300 | 4.0454 | | No log | 0.85 | 400 | 3.7557 ... | 98240c309ffccccaa29b8b9a4564cda4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `siddhana/slurp_entity_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best` ♻️ Imported from https://zenodo.org/record/5590204 This model was trained by siddhana using fsc/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | 2e0c5ba9d5eb74966ecc501e1e62a11e |
apache-2.0 | ['generated_from_trainer'] | false | switch-base-8-finetuned-samsum This model is a fine-tuned version of [google/switch-base-8](https://huggingface.co/google/switch-base-8) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4606 - Rouge1: 46.5651 - Rouge2: 23.2378 - Rougel: 39.4484 - Rougelsum: 43.1011 - Gen Len: ... | 9cd34b3eef25f6ab81504d2e7641075c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.8829 | 1.0 | 3683 | 1.5154 | 46.3805 | 23.0982 | 39.0612 | 43.0142 |... | cc0f7e83f78bc4e2c510cd71272d91a0 |
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: 0.2848 - F1: 0.8299 | dcd4d5ff7263784c9cb248b853938a42 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5989 | 1.0 | 191 | 0.3383 | 0.7928 | | 0.2617 | 2.0 | 382 | 0.2966 | 0.8318 | | 0.1672 | 3.0 | 573 | 0.2848 | 0.8299 | ... | a75c02c06082c4804b7dd79c3d2169e8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_2e-05_all_01_03_2022-05_32_03 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: 0.4208 - Accuracy:... | 97fcfdbb42a6bd772775714746b58a25 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.4443 | 0.7768 | 0.8589 | 0.8072 | 0.9176 | | 0.4532 | 2.0 |... | f2c48d21dab2926c13b0cdaea8643e75 |
apache-2.0 | ['generated_from_trainer'] | false | albert-large-v2_cls_CR This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6549 - Accuracy: 0.6383 | e519b3e8ebefb8ba6be7eda661127926 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 213 | 0.3524 | 0.8803 | | No log | 2.0 | 426 | 0.6839 | 0.6383 | | 0.5671 | 3.0 | 639 | 0.6622 | 0.... | 4a03c7830035279f3a49fc0aec154c7c |
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: 1.2108 | 600a7ca045e6def55a6458aa85d8da79 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.4952 | 1.0 | 5533 | 1.3895 | | 1.3024 | 2.0 | 11066 | 1.2490 | | 1.2087 | 3.0 | 16599 | 1.2108 | | 77422a9358a77a55c754529eac450480 |
apache-2.0 | [] | false | This repository is created with the aim to provide better models for NLI in persian, with the transparent codes for training I hope you guys find it inspiring and build better model in the future. for more details about the task and methods used for training check the [medium post](https://haddadhesam.medium.com/) and ... | 90410ef075f81b25981ca911befb427d |
apache-2.0 | [] | false | Model The proposed model is published at HuggingFace Hub with the name of ``demoversion/bert-fa-base-uncased-haddad-wikinli``. You can download and use the model from [HuggingFace Website](https://huggingface.co/demoversion/bert-fa-base-uncased-haddad-wikinli) or directly in transformers library like this: from ... | 8867947e0876917110ea6db69b4ca277 |
apache-2.0 | [] | false | Results The result comparing to the original model published for this dataset is available in the table bellow. |Model|dev_accuracy| dev_f1|test_accuracy|test_f1| |--|--|--|--|--| |[m3hrdadfi/bert-fa-base-uncased-wikinli](https://huggingface.co/m3hrdadfi/bert-fa-base-uncased-wikinli)|77.88|77.57|76.64|75.99| |[demo... | 68b72c8bc1a614f42889ae9120e8b270 |
apache-2.0 | [] | false | Notebooks Notebooks used for training and evaluation are available below. [Training ](https://colab.research.google.com/github/DemoVersion/persian-nli-trainer/blob/main/notebooks/training.ipynb) [Evaluation  on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5902 - Validation Loss: 1.3655 - Epoch: 9 | 93a902ef339678b1adfc8da966a930db |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.8689 | 2.0671 | 0 | | 2.2250 | 1.8456 | 1 | | 2.0132 | 1.7104 | 2 | | 1.9079 | 1.6828 | 3 | | 1.8237 | 1.5935 | 4 | | 1.7651 |... | 42d7b920ef0d09859b8e547317fadc6c |
apache-2.0 | ['bert'] | false | German Hotel Review Sentiment Classification A model trained on English Hotel Reviews from Switzerland. The base model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for ... | d2f93db58ef3b52528a1945a674026aa |
apache-2.0 | ['bert'] | false | Model Performance | Classes | Precision | Recall | F1 Score | | :--- | :---: | :---: |:---: | | Room | 77.78% | 77.78% | 77.78% | | Location | 95.45% | 95.45% | 95.45% | | Staff | 75.00% | 93.75% | 83.33% | | Unknown | 71.43% | 50.00% | 58.82% | | HotelOrganisation | 27.27% | 30.00% | 28.57% | | Food | 87.50% | 87.50... | 0c27ffdcb4748458849816dca6c3aed7 |
apache-2.0 | ['generated_from_keras_callback'] | false | albert-humor-classification This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: | 20c18b43e199859fd12132ff14e3412e |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-emotion-model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2238 - Accuracy: 0.9205 - F1: 0.9204 | f6d8610b95b8a15f7131a9ec1665aa25 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3235 | 0.9035 | 0.9003 | | 0.5384 | 2.0 | 500 | 0.2238 | 0.9205 | 0.9204 | | eb25619e7e67e8b0de217cf8de583cbc |
apache-2.0 | ['vision', 'image-segmentation'] | false | CLIPSeg model CLIPSeg model with reduce dimension 16. It was introduced in the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Lüddecke et al. and first released in [this repository](https://github.com/timojl/clipseg). | 0469fb80a7866f958826253ca7c8816b |
apache-2.0 | ['generated_from_trainer'] | false | mbert-targin-final This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9847 - Accuracy: 0.7025 - Precision: 0.6490 - Recall: 0.6487 - F1: 0.6489 | c0da3687ecdc62f2b14b0c6ec0c80cd7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 1.0 | 296 | 0.5774 | 0.7091 | 0.6506 | 0.6378 | 0.6426 | | 0.5912 | 2.0 |... | 326786c9c862c5aa3618a57fbff71ff8 |
apache-2.0 | ['tapas'] | false | TAPAS medium model fine-tuned on Sequential Question Answering (SQA) This model has 2 versions which can be used. The default version corresponds to the `tapas_sqa_inter_masklm_medium_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas). This model was pre-trained on MLM and... | 0ce7cb08136edc5f99fd7d2025ebd537 |
apache-2.0 | ['tapas'] | false | Results on SQA - Dev Accuracy Size | Reset | Dev Accuracy | Link -------- | --------| -------- | ---- LARGE | noreset | 0.7223 | [tapas-large-finetuned-sqa (absolute pos embeddings)](https://huggingface.co/google/tapas-large-finetuned-sqa/tree/no_reset) LARGE | reset | 0.7289 | [tapas-large-finetuned-sqa](https... | c2b72f3959a1eabb56b8c6f62e559093 |
apache-2.0 | ['tapas'] | false | BibTeX entry and citation info ```bibtex @misc{herzig2020tapas, title={TAPAS: Weakly Supervised Table Parsing via Pre-training}, author={Jonathan Herzig and Paweł Krzysztof Nowak and Thomas Müller and Francesco Piccinno and Julian Martin Eisenschlos}, year={2020}, eprint={2004.02349}, ... | 4fd126b1a2f22e0f8c2fc17028821cbe |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | mT5 Base for News Headline Style Transfer (Il Giornale to Repubblica) 🗞️➡️🗞️ 🇮🇹 This repository contains the checkpoint for the [mT5 Base](https://huggingface.co/google/mt5-base) model fine-tuned on news headline style transfer in the Il Giornale to Repubblica direction on the Italian CHANGE-IT dataset as part of... | 08baddfd3ab714fd37540b5afb477f89 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'newspaper', 'ilgiornale', 'repubblica', 'style-transfer'] | false | Using the model The model is trained to generate an headline in the style of Repubblica from the full body of an article written in the style of Il Giornale. Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipel... | 1121398613d88302e380f362cbf435b5 |
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: 0.7778
- Accuracy: 0.9171
| d7c44b119423bcdec35525fa5430d873 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
| eed06c4adad3c0a8a30277e85c48bd5d |
apache-2.0 | ['generated_from_trainer'] | false | Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2882 | 1.0 | 318 | 3.2777 | 0.7390 |
| 2.6228 | 2.0 | 636 | 1.8739 | 0.8287 |
| 1.5439 | 3.0 | 954 | 1.1619 ... | e2e59c47577682fb5ca1bf826469c153 |
mit | [] | false | char-con on Stable Diffusion This is the `<char-con>` 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 tra... | 2e229a195acd4d88535222ec5318ed8a |
creativeml-openrail-m | ['text-to-image'] | false | pills1testmodel Dreambooth model fine-tuned v2-1-512 base model Sample pictures of: pills (use that on your prompt) ![pills 0](https://huggingface.co/thegovind/pills1testmodel/resolve/main/concept_images/pills_%281%2... | 242c2ed6f9998edd9897239d9c9d2236 |
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