license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | d5c7290f5e06c1962de63742d57288de |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 3.7357 | 1.0 | 13655 | 3.6781 | | 3.5721 | 2.0 | 27310 | 3.5302 | | 3.4961 | 3.0 | 40965 | 3.4658 | | 3.4406 | 4.0 | 54620... | a971b43d890807cefc5c740f044ccc22 |
agpl-3.0 | [] | false | Model is developed in support of the University of Belgrade doctoral dissertation "Composite pseudogrammars based on parallel language models of Serbian" by Mihailo Škorić. It generates syntactly masked sentences for Serbian. This small gpt-2 model was fine-tuned on several corpora for Serbian, augmented using [Serb... | 302cc8eeedbeda15e96ffeae9c909591 |
apache-2.0 | ['generated_from_trainer'] | false | swin-base-finetuned-snacks This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the snacks dataset. It achieves the following results on the evaluation set: - Loss: 0.2404 - Accuracy: 0.9455 | 40e1e09d276ef11a62754a57e026c18b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0044 | 1.0 | 38 | 0.2981 | 0.9309 | | 0.0023 | 2.0 | 76 | 0.2287 | 0.9445 | | 0.0012 | 3.0 | 114 | 0.2404 | 0.... | a884ea51035d17e77e63fc57e26451c3 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_model 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: 0.2362 - Accuracy: 0.9308 | 920c41824063183e19e9766b15e16fbb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2356 | 1.0 | 1563 | 0.1914 | 0.9268 | | 0.1511 | 2.0 | 3126 | 0.2362 | 0.9308 | | 092f4661156989974ad4f0f4fd63b1f8 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_distilgpt2_sst2_negation0.0_pretrainedTrue_epochs0 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - eval_loss: 4.6217 - eval_runtime: 0.9515 - eval_samples_per_second: 193.385 - eval_steps_pe... | b824b0b79ed79772fb1b22ef41c903e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 0 | 87181c4c17304518f3855014fddbf49f |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-009901 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: 3.4697 - Bleu: 1.7899 - Gen Len: 46.5638 | d6d7e384343807645739602ab72679bd |
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: 0.8133 - Matthews Correlation: 0.5478 | 3e13a13c051fbcff312c11c42eaf168e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5259 | 1.0 | 535 | 0.5401 | 0.4009 | | 0.3513 | 2.0 | 1070 | 0.5403 | 0.4876 | | 0.2... | abf413bea3c3c3fa01eab1fbe0db988e |
apache-2.0 | ['generated_from_trainer'] | false | F_Roberta_classifier2 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: 0.1317 - Accuracy: 0.9751 - F1: 0.9751 - Precision: 0.9751 - Recall: 0.9751 - C Report: precis... | cff36483be07d9da9f7b2a081107b829 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | C Report ... | 4d6cee4250e8b3d2f97f6ac940456ba6 |
apache-2.0 | [] | false | BART (large-sized model) BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://gith... | 63ea1b8f2c9cac26f77fff7877d1c873 |
apache-2.0 | [] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') model = BartModel.from_pretrained('facebook/bart-large') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model... | 5b71fb14ad5ff061352a16a330d42f00 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_pretrain_mnli This model is a fine-tuned version of [gokuls/mobilebert_sa_pre-training-complete](https://huggingface.co/gokuls/mobilebert_sa_pre-training-complete) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3782 - Accuracy: 0.839... | 54f100cb390a714a86c786f3e364076e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6657 | 1.0 | 3068 | 0.4271 | 0.8153 | | 0.4271 | 2.0 | 6136 | 0.4219 | 0.8248 | | 0.3376 | 3.0 | 9204 | 0.3896 ... | 65dfceccbdcefdd83287394cccd299d1 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers', 'safetensors'] | false | Trained Dreambooth model using all my own art currently uploaded to my Tumblr (https://nadanainone.tumblr.com/, link only for reference, imagine plugging your own work for clout l m a o) and drawn over the course of around 10 years. The results are an inconsistent mess of half animu, half cartoony and a complete artist... | ee5af1f31727c4f837e49c59ea2ecba3 |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_xlsr-53_s972 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | 6bafb37e7ccd08d74e94a04d82555f64 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | `kan-bayashi/jsut_tts_train_vits_raw_phn_jaconv_pyopenjtalk_accent_with_pause_train.total_count.ave` ♻️ Imported from https://zenodo.org/record/5414980/ This model was trained by kan-bayashi using jsut/tts1 recipe in [espnet](https://github.com/espnet/espnet/). | 44864efa38320c4253da2304f33dba71 |
mit | ['generated_from_trainer'] | false | gpt2-finetuned-nft-shakes-seuss-2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9547 | 39d1c7c9fc6b5891821b8ed99712d755 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.3454 | 1.0 | 1490 | 4.1027 | | 4.0534 | 2.0 | 2980 | 3.9857 | | 3.9384 | 3.0 | 4470 | 3.9547 | | 5186e93426a67eab1be9fea92bd53499 |
mit | ['generated_from_trainer'] | false | xlm-roberta-large-finetuned-squad-v2 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4627 | 3da92d40e8f6150fb59864c48aff7cb7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | c7d36d9f675c47b935b5054404fde5f7 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.029 | 1.0 | 950 | 0.9281 | | 0.9774 | 2.0 | 1900 | 0.6130 | | 0.6781 | 3.0 | 2850 | 0.4627 | | bdf5704311e7b86f8ad2402c85235fda |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2238 | 1.0 | 5533 | 1.1549 | | 0.9633 | 2.0 | 11066 | 1.1189 | | 0.7524 | 3.0 | 16599 | 1.1586 | | c129c13e6ce7081572d41f10c85dcc6a |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | platzi-distilroberta-base-mrpc-glue This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets. It achieves the following results on the evaluation set: - Loss: 0.5320 - Accuracy: 0.7843 - F1: 0.8288 | b9c4aee4c1f93c04800d867babd0c6ef |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5381 | 1.09 | 500 | 0.5320 | 0.7843 | 0.8288 | | 0.3849 | 2.18 | 1000 | 0.5543 | 0.8431 | 0.8869 | | 672c565df1122d32d52daaf3e7c6206f |
apache-2.0 | ['generated_from_trainer'] | false | ru_t5model_for_legalsimplification This model is a fine-tuned version of [IlyaGusev/rut5_base_sum_gazeta](https://huggingface.co/IlyaGusev/rut5_base_sum_gazeta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Rouge1: 0.5364 - Rouge2: 0.1481 - Rougel: 0.506 - Rougelsum: 0.... | bde4fcb2f416e93ca9952e9feac2c601 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - 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: 10 - mixed_precision_training: Native AMP | 0f8f25578a25ec6ce02516ff5b3427f0 |
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 | 157 | nan | 0.5364 | 0.1481 | 0.506 | 0.4917 | 163.03 | |... | 4074f0e6ea3a255c360d859e7c564142 |
apache-2.0 | ['generated_from_keras_callback'] | false | ChatGPT Prompt Generator This model is a fine-tuned version of [BART-large](https://huggingface.co/facebook/bart-large) on a ChatGPT prompts dataset. It achieves the following results on the evaluation set: - Train Loss: 2.8329 - Validation Loss: 2.5015 - Epoch: 4 | fbca86ea0a89bbc8f585d89d373955f4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Intended uses & limitations You can use this to generate ChatGPT personas. Simply input a persona like below: ``` from transformers import BartForConditionalGeneration, BartTokenizer example_english_phrase = "photographer" batch = tokenizer(example_english_phrase, return_tensors="pt") generated_ids = model.generate... | 3e9d22bd6d7576a4c81ad18c0c801f93 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 8.4973 | 6.3592 | 0 | | 5.3145 | 3.2640 | 1 | | 3.5899 | 2.8350 | 2 | | 3.1044 | 2.6154 | 3 | | 2.8329 | 2.5015 | 4 | | f3bdb3a8d638de167c893c1a1e1af858 |
['cc0-1.0'] | ['autoencoder', 'time series', 'anomaly detection'] | false | Keras Implementation of industrial time series anomaly detection using an Autoencoder ⌛ This repo contains the model and the notebook [for this time series anomaly detection implementation of Keras](https://keras.io/examples/timeseries/timeseries_anomaly_detection/). | fdfe56f0131b7302d238dd618c08c2cc |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v7_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v7_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.6772 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7783 ... | d6c8d1aacb6e69e4a9b407416d281377 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 12 | 0.7850 | 0.0 | 0.0 | 0.0 | 0.7783 | | No log | 2.0 | 24 | 0... | a2a7c9db33567d5827d60a6073cf0e45 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotinons-jinesh This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2175 - Accuracy: 0.9275 - F1: 0.9274 | b03bf91f0b2848b229c4f347ac1c0b15 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8177 | 1.0 | 250 | 0.3146 | 0.904 | 0.9009 | | 0.246 | 2.0 | 500 | 0.2175 | 0.9275 | 0.9274 | | b1a1662904092466d0229359179bb1a1 |
creativeml-openrail-m | [] | false | Waifu-Diffusion-v1-3 based StableDiffusion model with Dreambooth training based on the game Hades from Supergiant Games including on 3 characters (Zagreus, Megaera, and Hades) as well as the general artstyle of the game. Trained for 12,000 steps using 96 total training images. | 1a50cba67ce6756b8a8e6d3ec1f8852f |
creativeml-openrail-m | [] | false | Usage Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use the following tokens in your prompt to a... | c0ab936e33b5fb26a6f3c78bce4b4b99 |
creativeml-openrail-m | [] | false | Example images using ```"s_hdsg artstyle"``` <table> <tr> <td><img src=https://i.imgur.com/Q3LFplo.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/2yKXToS.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/nzahHUH.png width=100% height=100%/></td> <td><img src=https... | bbb9bb13459b55095ccba4a1a98ee8c5 |
creativeml-openrail-m | [] | false | Example images from ```"c_zgr man"``` <table> <tr> <td><img src=https://i.imgur.com/POuJMvO.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/jcD0uOv.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/thacmQA.png width=100% height=100%/></td> <td><img src=https://i.im... | 3d0eb917e024df889520f60ee0867ec6 |
creativeml-openrail-m | [] | false | Example images from ```"c_mgr woman"``` <table> <tr> <td><img src=https://i.imgur.com/AasPZ8u.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/TPI4gQM.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/Et8sMJR.png width=100% height=100%/></td> <td><img src=https://i.... | 71c93caadaa2831bf64da8d5b5ef3c0b |
creativeml-openrail-m | [] | false | Example images from ```"c_hds man"``` <table> <tr> <td><img src=https://i.imgur.com/pemcAcb.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/8SvMT1b.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/8eR9HO0.png width=100% height=100%/></td> <td><img src=https://i.im... | 2508e2ac2b14473038975554c43af7c3 |
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: 0.5035 - Wer: 0.3346 | 13c30942830620b1013c28219ea7b8e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.1411 | 1.0 | 500 | 0.6675 | 0.6001 | | 0.5668 | 2.01 | 1000 | 0.4699 | 0.4973 | | 0.3773 | 3.01 | 1500 | 0.4475 | 0.440... | 85cf9c92bc442f81a1394ff1a0deb16b |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | LSM-v1.0 Dreambooth model trained by 9LSMai 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-diffu... | e4ecf8616db623307ba37614977e8a85 |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | Model Details **Model Description:** This model is a [DistilBERT](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) fine-tuned on SST-2 dynamically quantized with [optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel... | e882a13446087a70c8e353d005b82fdd |
apache-2.0 | ['text-classification', 'neural-compressor', 'int8'] | false | How to Get Started With the Model To load the quantized model and run inference using the Transformers [pipelines](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines), you can do as follows: ```python from transformers import AutoTokenizer, pipeline from optimum.intel.neural_compressor import In... | 743b63e379b292577b099ddd10c1a9a1 |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | roberta-base + DAPT + Task Transfer for Domain-Specific QA Objective: This is Roberta Base with Domain Adaptive Pretraining on Movie Corpora --> Then trained for the NER task using MIT Movie Dataset --> Then a changed head to do the SQuAD Task. This makes a QA model capable of answering questions in the movie domai... | 75be655078629c3989bb65d7b486a1dc |
cc-by-4.0 | ['roberta', 'roberta-base', 'question-answering', 'qa', 'movies'] | false | Overview **Language model:** roberta-base **Language:** English **Downstream-task:** NER --> QA **Training data:** imdb, polarity movie data, cornell_movie_dialogue, 25mlens movie names, MIT Movie, SQuADv1 **Eval data:** MoviesQA (From https://github.com/ibm-aur-nlp/domain-specific-QA) **Infrastructure**: 4x... | 09db1beccc9f15faa6b3d43d9ab38d0b |
apache-2.0 | ['translation'] | false | en-ja * source group: English * target group: Japanese * OPUS readme: [eng-jpn](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-jpn/README.md) * model: transformer-align * source language(s): eng * target language(s): jpn * model: transformer-align * pre-processing: normalization + Sente... | 544ec45151603304e6385d6696132156 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: en-ja - source_languages: eng - target_languages: jpn - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-jpn/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'ja'] - src_constituents: ('English', {'eng'}) - tgt_co... | bf3ad961833b94322261e27e0a945e67 |
apache-2.0 | [] | false | RAG This is the RAG-Token Model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al. The model is a *uncased* model, which means that capital letters are simply converted to lower-case lette... | 1e781f85edf723b0b43d13518fe50626 |
apache-2.0 | [] | false | Usage: **Note**: In the usage example below only the *dummy* retriever of *wiki_dpr* is used because the complete *lecagy* index requires over 75 GB of RAM. The model can generate answers to any factoid question as follows: ```python from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration tokeniz... | 39c527708a8a43944f8f41b6cd414b96 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Czech This model is part of our paper called: - Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details. | 0bb053bda037b016ae0f3601c5e2e9bd |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cs") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-cs") ``` | 6fe4f3b435d692f0a11d1c7cf860d4f8 |
mit | ['generated_from_trainer'] | false | bart-large-mnli-aitools This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1230 - Accuracy: 0.9722 | 0a346d591e4e8e4a46719307cf70de95 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.31 | 50 | 0.5212 | 0.8611 | | No log | 0.61 | 100 | 0.5397 | 0.8333 | | No log | 0.92 | 150 | 0.0322 | 0.... | 6f7867534b5c332120b4a42b3ab4b753 |
apache-2.0 | ['translation'] | false | opus-mt-ber-en * source languages: ber * target languages: en * OPUS readme: [ber-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ber-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](http... | ac485043a85d1c597b96db907a1ed1f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5062 | 0.8061 | | 7a00e83e42cef118be4e67cb1ba48962 |
apache-2.0 | ['generated_from_trainer'] | false | sumups-batch3-model 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: 1.2370 - Precision: 0.0282 - Recall: 0.1030 - F1: 0.0442 - Accuracy: 0.5356 | a7e6f6f901bc2bf3c7e97474f30e8b8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 20 | 1.3966 | 0.0062 | 0.0257 | 0.0100 | 0.4634 | | No log | 2.0 |... | e6890cafaf890aac43ae588afd5115bf |
apache-2.0 | ['summarization', 'translation'] | false | Model Card for T5 11B - fp16  | e21f30075969d99167fb33fd87ef6b92 |
apache-2.0 | ['summarization', 'translation'] | false | Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT... | 970386914d1b552016662fe762554dca |
apache-2.0 | ['summarization', 'translation'] | false | Disclaimer **Before `transformers` v3.5.0**, due do its immense size, `t5-11b` required some special treatment. If you're using transformers `<= v3.4.0`, `t5-11b` should be loaded with flag `use_cdn` set to `False` as follows: ```python t5 = transformers.T5ForConditionalGeneration.from_pretrained('t5-11b', use_cdn ... | f54405c772d2323ef46406f2129e472a |
apache-2.0 | ['summarization', 'translation'] | false | transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more context. | f21a8818f8784ad280b4aff7ff0d0b78 |
apache-2.0 | ['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: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 | 47dcdee3d63c18bfbf18cf44f02c1b4c |
mit | ['generated_from_trainer'] | false | bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3-arxiv3o3 This model is a fine-tuned version of [theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3](https://huggingface.co/theojolliffe/bart-large-cnn-pubmed1o3-pubmed2o3-pubmed3o3-arxiv1o3-arxiv2o3) on the scientific_papers datas... | d06444ef4029db69f693680e9b1a2b02 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 2.0865 | 1.0 | 33840 | 2.0646 | 42.5835 | 16.1887 | 24.7972 | 38.1846 ... | 5a5c69062ac8b8baf82455d30ed6b936 |
creativeml-openrail-m | [] | false | This model is a work in progress. It isn't perfect, but it can generate some nice images. I have trained Xaela (dark horns and scales) and Raen (light horns and scales) as separate concepts, which will allow you to specify which you would like in your image by using the tokens specified below. I hope you enjoy it as... | abbadc680fd98719844323b3300bea1d |
creativeml-openrail-m | [] | false | Usage Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions. Use ```"m_arxla"``` for Xaela clan Au Ra or ... | b3fcb59d51f22f19a7bc581a6f59ada9 |
creativeml-openrail-m | [] | false | Recommended negative prompt ```"poorly drawn, bad quality, colored skin"``` You can also add the following to your negative prompt in order to steer your output towards the WD1.3 default caucasian skintone: ```"blue skin, purple skin"``` If you are generating Raen Au Ra I highly recommend also adding ```"black scal... | 8ec9fa88a676c78132ed3f7906b5c47a |
creativeml-openrail-m | [] | false | Example prompt ```"m_arrn, 1girl, light smile, detailed eyes, extremely detailed face, sidelocks, black hair, grey eyes, long hair, hair clip, collarbone, tank top, shorts, looking to the side, highly detailed face, extremely detailed, intricate, best quality, ultra realistic, cowboy shot, holding shopping bags at the... | f65c2448f3aa7fbc656a8ecba1403c46 |
creativeml-openrail-m | [] | false | Xaela example images using ```"m_arxla"``` <table> <tr> <td><img src=https://i.imgur.com/gvgZeT1.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/wWFDxCS.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/yzWhulJ.png width=100% height=100%/></td> <td><img src=https:/... | 8bb95a26a2cbede5d88a8c539dc3968a |
creativeml-openrail-m | [] | false | Raen example images using ```"m_arrn"``` <table> <tr> <td><img src=https://i.imgur.com/jwoWZWE.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/k1XPAZI.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/MvcSlAd.png width=100% height=100%/></td> <td><img src=https://i... | 1c0e5fc8117f767213624d2b48391393 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-mlm-paraphrasing This model is a fine-tuned version of [gayanin/t5-small-mlm-pubmed](https://huggingface.co/gayanin/t5-small-mlm-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4243 - Rouge2 Precision: 0.8281 - Rouge2 Recall: 0.6508 - Rouge2 Fmeasure: 0.7125 ... | 50ebe1d76fbee2ba9bfe8b6148f7e6a4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 0.6445 | 0.75 | 500 | 0.5049 | 0.821 | 0.6477 | 0.7078 ... | 9ad7070a8369a372a8b7a939ea86ebec |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | deployment-with-nvidia-riva) | This model utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the Aishell-2 Mandard Corpus. It is a non-autoregressive "large" variant of Citrinet, with around 140 million parameters. See the [model architecture]( | b012c854889dcafee3acca5939c1a62d |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_zh_citrinet_1024_gamma_0_25" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | 5626fb5966c18f58cf08302c1ce32832 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Model Architecture Citrinet model is a non-autoregressive model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html | 6e0ce9335a527c50a8975a9cb5492978 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/citr... | 5bc81d13720225d48673a6af297e05e6 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Datasets All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech: - AIShell 2 Note: older versions of the model may have trained on smaller set of datasets. | d4b6cb72454f5b2a5406fecd2886469a |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | Dev iOS | Test iOS | Dev Android | Test Android | Dev Mic | Test Mic | Train ... | fb78220252e5daf4a0fb92d6703ff5fa |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | References - [1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) - [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) - [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) | 758c53bc6da24debb529ce8f6dc47085 |
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: 0.2263 - Accuracy: 0.9285 - F1: 0.9284 | 5d7b0c546764ea8286b9e7dfd1ce9fc2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.865 | 1.0 | 250 | 0.3356 | 0.897 | 0.8926 | | 0.2641 | 2.0 | 500 | 0.2263 | 0.9285 | 0.9284 | | 9b99f50d05b99fdadace91fec382ac3c |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-3 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4921 - Rouge1: 0.0598 - Rouge2: 0.0 - Rougel: 0.0591 - Rougelsum: 0.0588 | 0d0558d915f00838ed2358990ea2e106 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 918dc514f185396e6a903bd50a7e93ab |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 6.841 | 1.0 | 60 | 5.4156 | 0.0 | 0.0 | 0.0 | 0.0 | | 4.5073 | 2.0 | 120 ... | b7a18cafd41a5c3803e1fa8c9401f5e3 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.1020 - Accuracy: 0.9815 | 4a3412d7767070d69461cf5a691b8a58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7887 | 1.0 | 399 | 0.7190 | 0.7682 | | 0.3784 | 2.0 | 798 | 0.2387 | 0.9737 | | 0.2159 | 3.0 | 1197 | 0.1335 | 0.... | 9ef13e1bfe28f035a8c0f319626492b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.9013 | 1.0 | 833 | 3.7301 | | 3.7057 | 2.0 | 1666 | 3.7014 | | 3.6827 | 3.0 | 2499 | 3.6948 | | 0b73017d811cb09ed1c981a7226d7f25 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-zle-de Neural machine translation model for translating from East Slavic languages (zle) to German (de). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the w... | e2376711e287a7299872b54fbdfa3171 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-03-19 * source language(s): bel rus ukr * target language(s): deu * model: transformer-big * data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807_transformer-big_2022-03-19.zip](h... | ebd26deb2e109a02c8261776d63daded |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "Это был по-настоящему прекрасный день.", "Дождь кончился?" ] model_name = "pytorch-models/opus-mt-tc-big-zle-de" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pre... | 1c4346990104275d57074aeb96f68cac |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Ist der Regen vorbei? ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-zle-de") print(pipe("Это был по-настоящему прекрасный день.")) | 4af898d2a3cd065d270a6f7f636c3528 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-deu/opusTCv20210807_transformer-big_2022-03-19.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zl... | f278a149fc782990c2ad0db37ca830d8 |
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