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 | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 2.3196 | 5d4ae9500f10de4de558e0aa4b2b9653 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6586 | 1.0 | 500 | 2.4203 | | 2.4655 | 2.0 | 1000 | 2.3889 | | 2.3769 | 3.0 | 1500 | 2.3279 | | 2.3024 | 4.0 | 2000 | 2.3623 ... | 4ace42182399194933756e605733d9e1 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | mt5-small-ibn-Shaddad-v4 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: 2.9233 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 | f19715ab6c32db8fc987629bbb250234 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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: 4 | 8c1dd3a3ef0d7cf6cb825da5ef1498c8 |
apache-2.0 | ['Poet', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 5.0001 | 1.0 | 935 | 3.1102 | 0.0 | 0.0 | 0.0 | 0.0 | | 3.4066 | 2.0 | 1870 ... | f472f97109e953855165e781b7f3644b |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-woody_90epochs 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: 0.4351 - Accuracy: 0.8424 | 5ad9edddaa1111fde6558ba895c781b7 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 1107fe8f92060d7f7a920cfb65e16297 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6659 | 1.0 | 58 | 0.6216 | 0.6558 | | 0.6181 | 2.0 | 116 | 0.5616 | 0.7115 | | 0.5941 | 3.0 | 174 | 0.5464 | 0.... | 984677d23c3256cee664872c8d0a1f2d |
creativeml-openrail-m | ['text-to-image'] | false | tjmo Dreambooth model trained by duja1 with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/mai... | f4bb624cd9ee8140352a177189e1c1c2 |
apache-2.0 | ['translation'] | false | opus-mt-en-st * source languages: en * target languages: st * OPUS readme: [en-st](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-st/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 2575da6e83788618a9751bdeb59ccfcd |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_50v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one50v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5788 - Precision: 0.3560 - Recall: 0.0421 - F1: 0.0752 - Accuracy... | fe45c6a6830bb988f681ab80a8aa3fd2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 22 | 0.6655 | 0.0 | 0.0 | 0.0 | 0.7775 | | No log | 2.0 |... | 1332898cbc64ff46a9b1b048e59ded71 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-final-cola 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: 0.1466 - Accuracy: 0.9611 | e2c5972e12154eab540176fddd56f8ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 48 | 0.5105 | 0.8623 | | No log | 2.0 | 96 | 0.2715 | 0.9281 | | No log | 3.0 | 144 | 0.1998 | 0.... | e4f576d5031970dbf9fd15fc5b6dcfd6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-cased-1mjuicios This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7924 | 37092620db7c8db934add36695732262 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3025 | 1.0 | 625 | 1.9433 | | 1.9743 | 2.0 | 1250 | 1.8283 | | 1.8725 | 3.0 | 1875 | 1.7924 | | fc348c1c71637f59e55342724c3dd2c1 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 500k (uncased) Seed 1 intermediate checkpoint 500k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/goo... | 47254f862c21476ec824bf5c184cacbb |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-1-500k') model = BertModel.from_pretrained("multiberts-seed-1-500k") text = "Replace me by any text you'd like.... | 7647ff5f63bee0f89d98a0947604b7a0 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Dutch (nl) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https... | e1ecc91c5b1d6c52a8362e336c4c7c37 |
apache-2.0 | [] | false | <style>table.eli5-weights tr:hover {filter: brightness(85%);}</style><p>Explained as: feature importances</p><pre>XGBoost feature importances; values are numbers 0 <= x <= 1;all values sum to 1.</pre><table class="eli5-weights eli5-feature-importances" style="border-collapse: collapse; border: none; margin-top: 0em; ta... | 8bd44234d11297827baa46ed0dd24760 |
apache-2.0 | ['generated_from_keras_callback'] | false | opus-mt-ar-en-finetunedTanzil-v6-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6863 - Validation Loss: 0.8789 - Train Rouge1: 56.4999 - Train... | 611e10b3e34243ad56b75e5a02293ace |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 1.2958 | 1.2191 | 55.4125 | 33.4250 ... | 6ece462a3807f1928ae712c912c82532 |
apache-2.0 | salesken | false | ```python from transformers import AutoTokenizer, AutoModelWithLMHead, AutoModelForCausalLM import torch if torch.cuda.is_available(): device = torch.device("cuda") else : device = "cpu" tokenizer = AutoTokenizer.from_pretrained("salesken/grammar_correction") model = AutoModelForCausalLM.from_pretrained("... | c2cc555b3c96a8257ee333ffdc04a300 |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Scottish Gaelic 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. | 7cf33692efae77988222015fcc3fca69 |
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-gd") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-gd") ``` | 99b6043a4bd6bc3d21a6c98a87849536 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | glpn-nyu-finetuned-diode-221122-044810 This model is a fine-tuned version of [vinvino02/glpn-nyu](https://huggingface.co/vinvino02/glpn-nyu) on the diode-subset dataset. It achieves the following results on the evaluation set: - Loss: 0.3690 - Mae: 0.2909 - Rmse: 0.4208 - Abs Rel: 0.3635 - Log Mae: 0.1224 - Log Rmse:... | ab010299eac30bf9a413e3c525d9ab74 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 24 - eval_batch_size: 48 - seed: 2022 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 15 - mixed_precision_... | 11b86ee172f7363aae17c8ba7b9bf186 |
apache-2.0 | ['vision', 'depth-estimation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Abs Rel | Log Mae | Log Rmse | Delta1 | Delta2 | Delta3 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:-------:|:--------:|:------:|:------:|:------:| | 1.3864 | 1.0 | 72 | 1.2016 ... | 3ba573f9d33991d66e34cd8baea4ca51 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Odia This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 or dataset. It achieves the following results on the evaluation set: - Loss: 0.9507 - Wer: 29.1961 | e7caa535666f7472beee725504565fb4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.0009 | 99.0 | 1000 | 0.5994 | 29.3371 | | 0.0001 | 199.0 | 2000 | 0.8873 | 29.6756 | | 0.0 | 299.0 | 3000 | 0.9507 | 29.196... | 84f84a00ad1773c523b5bc48eeb94bd7 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | opus-mt-tc-big-en-bg Neural machine translation model for translating from English (en) to Bulgarian (bg). 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 world. All mod... | 9fd502d6b2316bdb77705fe3616dfdd9 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model info * Release: 2022-02-25 * source language(s): eng * target language(s): bul * model: transformer-big * data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) * tokenization: SentencePiece (spm32k,spm32k) * original model: [opusTCv20210807+bt_transformer-big_2022-02-25.zip](htt... | f3d9ca06de38afd7443e3f3df91c9935 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Usage A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ "2001 is the year when the 21st century begins.", "This is Copacabana!" ] model_name = "pytorch-models/opus-mt-tc-big-en-bg" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTMod... | fd10da7f029e883278c2927490a6bffb |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Това е Копакабана! ``` 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-en-bg") print(pipe("2001 is the year when the 21st century begins.")) | 88fffb08c7d9c35b149fdd664560e9da |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Benchmarks * test set translations: [opusTCv20210807+bt_transformer-big_2022-02-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-bul/opusTCv20210807+bt_transformer-big_2022-02-25.test.txt) * test set scores: [opusTCv20210807+bt_transformer-big_2022-02-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-... | 2bd2c5eb22bfdccc84208d1fd842fb62 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | eng-bul | tatoeba-test-v2021-08-07 | 0.68987 | 51.5 | 10000 | 69504 | | eng-bul | flores101-devtest | 0.69891 | 44.9 | 1012 | 24700 | | 6ae2b67b84dabec4475189b01187442b |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_unispeech-ml_s671 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using... | 99f21f94379fff5abbcbf3a58a0de033 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 5abf43d34f2e5c2af94f4ccb19fbc31b |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1628 - Accuracy: 0.9345 - F1: 0.9348 | 307bcbc98059fd5e71a370e31461d39c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1674 | 1.0 | 250 | 0.1718 | 0.9265 | 0.9266 | | 0.1091 | 2.0 | 500 | 0.1628 | 0.9345 | 0.9348 | | f40fafcbc56af896ad617ac7d3162641 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab60 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: 3.1975 - Wer: 1.0 | c27b2f456ad5773d4f8c61878c777d91 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 5.5799 | 7.04 | 500 | 3.2484 | 1.0 | | 3.1859 | 14.08 | 1000 | 3.1951 | 1.0 | | 3.1694 | 21.13 | 1500 | 3.1754 | 1.0 | | 3.1637 ... | dfa4d4246c7964e7eb921975ca086fb1 |
apache-2.0 | ['generated_from_trainer'] | false | filipino-wav2vec2-l-xls-r-300m-test This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.7753 - Wer: 0.4831 | f9c181c7e1a9eda67b0cab75b47f80c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sched... | e926cd507d82d7ddced0acdfc11f01e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.7314 | 2.09 | 400 | 0.7541 | 0.7262 | | 0.6065 | 4.19 | 800 | 0.6738 | 0.6314 | | 0.4063 | 6.28 | 1200 | 0.6310 | 0.5992 | |... | 87cdd416574d75d245da7b0a14c6e52c |
apache-2.0 | [] | false | [DistilBERT base cased](https://huggingface.co/distilbert-base-cased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is sensitive to capital letters — "english" is different than "English". For the case insensitive version, please use [elastic/d... | d31da33cf51d509aa921fa78949faa6d |
apache-2.0 | [] | false | Training ``` $ run_ner.py \ --model_name_or_path distilbert-base-cased \ --label_all_tokens True \ --return_entity_level_metrics True \ --dataset_name conll2003 \ --output_dir /tmp/distilbert-base-cased-finetuned-conll03-english \ --do_train \ --do_eval ``` After training, we update the labels to match... | 163a312f14011cffbaa00f8351dcc14d |
mit | ['generated_from_trainer'] | false | roberta-base-stsb This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.4221 - Pearson: 0.9116 - Spearmanr: 0.9092 - Combined Score: 0.9104 | ba9bd99d704532984796012631365146 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 1.6552 | 1.39 | 500 | 0.5265 | 0.8925 | 0.8925 | 0.8925 | | 0.3579 | 2.78 | 1000 ... | 9f914973efa057599144b2092f8cfdb8 |
apache-2.0 | ['generated_from_keras_callback'] | false | timaos/distilbert-base-uncased-finetuned-cola 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: - Train Loss: 0.1915 - Validation Loss: 0.5237 - Train Matthews Correlation: 0.5... | e190b2ac33b56e4fe1401a6e0a1141dd |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5210 | 0.4500 | 0.5041 | 0 | | 0.3169 | 0.4527 | 0.5280 | 1 | | 0.1915 | 0.5237... | 8928ee633e4b554bc9bb26b750f278fa |
apache-2.0 | ['generated_from_keras_callback'] | false | bert-fine-tuned-cola This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5013 - Validation Loss: 0.4341 - Epoch: 0 | 2c1eddcb5db7b6d5e9931809c0ce7bec |
apache-2.0 | ['generated_from_keras_callback'] | false | CharlieP/t5-small-nlpfinalproject-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.2391 - Validation Loss: 3.0511 - Train Rouge1: 21.2434 - Train Rouge2: 4.0808 - Train Rougel: 16.6836... | 1740d02a2a6d11ea8b7eea7809ee4085 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 3.8204 | 3.2757 | 18.2829 | 2.7616 ... | 3c2c80680e8ff0cb22e12bb74fe61603 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-5_female-5_s21 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 4f659d4f0a20a8f3c50e477918a56c20 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_accent_us-2_england-8_s459 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 2ac6356a3f6e19fc1d7621bffce5ce14 |
apache-2.0 | ['generated_from_keras_callback'] | false | BobBraico/rlb-cyber-finetuned-imdb 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: - Train Loss: 2.7869 - Validation Loss: 2.4354 - Epoch: 0 | 32780609e3a0432c48e93df5bffa4674 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | 5def2ef862fd080bfa7dc21ce7416b58 |
mit | ['generated_from_trainer'] | false | roberta-base_stereoset_finetuned This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the stereoset dataset. It achieves the following results on the evaluation set: - Loss: 0.8461 - Accuracy: 0.7904 | efd67c808ac227e791afb36cc024f40f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 6a05ce0850815f37de537d86995101a6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.21 | 5 | 0.6915 | 0.5149 | | No log | 0.42 | 10 | 0.6945 | 0.4914 | | No log | 0.62 | 15 | 0.6931 | 0.... | a8acab9d2841ef277a58395caacb4b92 |
mit | [] | false | Usage - `pip install -U bnlp_toolkit` - `pip install fasttext==0.9.2` - Generate Vector Using Pretrained Model ```py from bnlp.embedding.fasttext import BengaliFasttext bft = BengaliFasttext() word = "গ্রাম" model_path = "bengali_fasttext_wiki.bin" word_vector = bft.generate_word_vector(model_path, word) prin... | 7f287e8d32649c7da0ff2fb61793da8f |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | platzi-vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0336 - Accuracy: 0.9925 | b013d0872dfbeb9685fd1fe773373650 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1381 | 3.85 | 500 | 0.0336 | 0.9925 | | 9a002ccbae663f3adb8f9ffb4c6b67c4 |
mit | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos'] | false | SuPar-Kanbun Tokenizer, POS-Tagger and Dependency-Parser for Classical Chinese Texts (漢文/文言文) with [spaCy](https://spacy.io), [Transformers](https://huggingface.co/transformers/) and [SuPar](https://github.com/yzhangcs/parser). | dcd193c0af253ec849983e50ed828c32 |
mit | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos'] | false | text = 不入虎穴不得虎子 1 不 不 ADV v,副詞,否定,無界 Polarity=Neg 2 advmod _ Gloss=not|SpaceAfter=No 2 入 入 VERB v,動詞,行為,移動 _ 0 root _ Gloss=enter|SpaceAfter=No 3 虎 虎 NOUN n,名詞,主体,動物 _ 4 nmod _ Gloss=tiger|SpaceAfter=No 4 穴 穴 NOUN n,名詞,固定物,地形 Case=Loc 2 obj _ Gloss=cave|SpaceAfter=No 5 不 不 ADV v,副詞,否定,無界 Polarity=Neg 6 advmod _ Gloss=... | 5dd2d1360d3d71056bdc50f4d3b7512f |
mit | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos'] | false | Installation for Cygwin64 Make sure to get `python37-devel` `python37-pip` `python37-cython` `python37-numpy` `python37-wheel` `gcc-g++` `mingw64-x86_64-gcc-g++` `git` `curl` `make` `cmake` packages, and then: ```sh curl -L https://raw.githubusercontent.com/KoichiYasuoka/CygTorch/master/installer/supar.sh | sh pip3.7... | 858c7c3575f793b72955437b6eb20ed2 |
mit | ['classical chinese', 'literary chinese', 'ancient chinese', 'token-classification', 'pos'] | false | Installation for Jupyter Notebook (Google Colaboratory) ```py !pip install suparkanbun ``` Try [notebook](https://colab.research.google.com/github/KoichiYasuoka/SuPar-Kanbun/blob/main/suparkanbun.ipynb) for Google Colaboratory. | 43d09cbdf5892512b15350d5725b3b24 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_6_binary 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: 1.6838 - F1: 0.7881 | 3f8387d5c58064037f96a16824cf311f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 290 | 0.4181 | 0.7732 | | 0.4097 | 2.0 | 580 | 0.3967 | 0.7697 | | 0.4097 | 3.0 | 870 | 0.5811 | 0.7797 | |... | 60665af5c440a46661184936656cc76d |
apache-2.0 | ['generated_from_trainer'] | false | sentiment_analysis_model 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: 0.6018 - Accuracy: 0.8513 - F1: 0.8515 | 1e73ec0cf3641c7cf174711d5640ed8b |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Zdzislaw Beksinski Art Diffusion Model I present you fine tuned model of stable-diffusion-v1-5, which heavily based of work of great artist, Zdzislaw Beksinski. Use the tokens **_beksinski style_** in your prompts for the effect. Model was trained using the diffusers library, which based on Dreambooth implementatio... | 7320029855f66b9e015c33f31c56e782 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | !pip install diffusers transformers scipy torch from diffusers import StableDiffusionPipeline import torch model_id = "s3nh/beksinski-style-stable-diffusion" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "Bus riding to school, beksinski style" image ... | 7689bae7cca206fc0440112cff12627f |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Bus riding to school, beksinski style.    | c2ccc9375f8c93eab6b2ac4c02963134 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Car traffic, beksinski style    and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | df1d243f43cfc10ff43fd0289726eb85 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_xls-r_age_teens-5_sixties-5_s463 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | fe3c7b56af8e00ecbfe6ec57fc12e222 |
mit | ['generated_from_trainer'] | false | bart_large_summarise_v2 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset. It achieves the following results on the evaluation set: - Loss: 4.2988 - Rouge1: 39.305 - Rouge2: 13.4171 - Rougel: 20.4214 - Rougelsum: 34.971 - Gen Len:... | e86238bbacb3598d54411bc7d649c2f6 |
apache-2.0 | ['generated_from_trainer'] | false | test-electra-small-yelp This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the yelp_review_full yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 2.2601 - Accuracy: 0.5677 | 70a62ade62f6a19f43ae9f51a69374d6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola-v5 This model is a fine-tuned version of [MGanesh29/distilbert-base-uncased-finetuned-cola-v5](https://huggingface.co/MGanesh29/distilbert-base-uncased-finetuned-cola-v5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2563 - Accuracy: 0.9... | db26b963cdb5cd3efff6665b880a7368 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | No log | 6.25 | 50 | 0.2638 | 0.9310 | 0.9310 | 0.9310 | 0.9310 | | No log | 12.5 |... | 68b26b2e4986e06e7bd402d95cb85baa |
apache-2.0 | ['translation'] | false | heb-ara * source group: Hebrew * target group: Arabic * OPUS readme: [heb-ara](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ara/README.md) * model: transformer * source language(s): heb * target language(s): apc apc_Latn ara arq arz * model: transformer * pre-processing: normalization... | 3d0c0d3a32f060a5769e534ac7a18576 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: heb-ara - source_languages: heb - target_languages: ara - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/heb-ara/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['he', 'ar'] - src_constituents: {'heb'} - tgt_const... | 19919212be620d0e9df262ced0b50423 |
apache-2.0 | ['translation'] | false | opus-mt-es-it * source languages: es * target languages: it * OPUS readme: [es-it](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-it/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-29.zip](https://... | 4db33d863b2967c63912c794c38588db |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_mnli This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.7995 - Accuracy: 0.6565 | 2364c5f46bdadce9b3c445ae4fc2ba85 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9882 | 1.0 | 1534 | 0.9194 | 0.5707 | | 0.8859 | 2.0 | 3068 | 0.8623 | 0.6074 | | 0.8254 | 3.0 | 4602 | 0.8507 ... | 35a79b49b66e8e0668d023ba5e48c220 |
mit | [] | false | Swedish BERT models for sentiment analysis, Sentiment targets. [Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases a Named Entity Recognition(NER) model for entety detection in Swedish. The model is based on [KB/bert-base-swedish-cased](https://huggingface.co/KB... | 70696f9f02f9967b042663c2eb9bd377 |
mit | [] | false | model by dadosdq This your the Stable Diffusion model fine-tuned the ChairTest concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **ChA1r** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com... | 135bc0957292b91cb984abbad6c1d2cf |
apache-2.0 | ['part-of-speech', 'token-classification'] | false | XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Norwegian 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. | 017dbedfc524c6de407102b5d2de5bd1 |
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-no") model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-no") ``` | defd48f2e1fd1141e12636a59af092ef |
other | ['generated_from_trainer'] | false | dalio-all-io-1.3b-3-epoch This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the AlekseyKorshuk/dalio-all-io dataset. It achieves the following results on the evaluation set: - Loss: 2.3008 - Accuracy: 0.0584 | 741882799eda35a85510bd9e65f2bf1c |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.6543 | 0.03 | 1 | 2.6113 | 0.0513 | | 2.6077 | 0.07 | 2 | 2.6113 | 0.0513 | | 2.5964 | 0.1 | 3 | 2.5605 | 0.... | 1e5402688cee96a888bafc92b2a438cf |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-uncased_ner_conll2003 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0516 - Precision: 0.9424 - Recall: 0.9530 - F1: 0.9477 - Accuracy: 0.9898 | d248a955674916240bb7b4395a11d600 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1605 | 1.0 | 878 | 0.0533 | 0.9252 | 0.9329 | 0.9290 | 0.9864 | | 0.032 | 2.0 |... | b6c0897f01afaec6bc939484233ce41a |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for coatnet_nano_rw_224.sw_in1k A timm specific CoAtNet image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | 4ca81a749a9f545c5a98edd7b71937dd |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.1 - GMACs: 2.4 - Activations (M): 15.4 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - **Dataset:** ImageNet-1... | 7c0971a31cbdbad03ef0f4c977476201 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('coatnet_nano_rw_224.sw_in1k', pretrained=True) model =... | 8cdb18573aad7592ab1a8502399e1055 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_nano_rw_224.sw_in1k', pretrained=Tr... | 61b27582815fe8df06629b209b37125b |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'coatnet_nano_rw_224.sw_in1k', pretrained=True, ... | 3e1d5f9db152053a8ae933cb66886b3b |
apache-2.0 | ['Quality Estimation', 'siamesetransquest', 'da'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-en_zh-wiki") predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "... | 13e3f15f1d91ace30582839d656b370c |
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