license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9089 - Matthews Correlation: 0.5640 | 98b8f2abc8accc33a4465e49532c9b75 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4864 | 1.0 | 535 | 0.4689 | 0.5232 | | 0.2864 | 2.0 | 1070 | 0.5835 | 0.5296 | | 0.1... | d76763728d1bfb3cca59a5dd9a9c217d |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_vp-it_s179 Fine-tuned [facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | a8f102d7e89809d8e22a7efc1abf1f2e |
gpl | ['token-classification'] | false | Training data The underlying corpus, [NerKor+CARS-OntoNotes++](https://github.com/ppke-nlpg/NYTK-NerKor-Cars-OntoNotesPP), was derived from [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor), a Hungarian gold standard named entity annotated corpus containing about 1 million tokens. It includes a small addition of 1... | 4ba17d2b19092240c92983e37b2d2404 |
gpl | ['token-classification'] | false | Tags derived from the OntoNotes 5.0 annotation Names are annotated according to the following set of types: | | | |---|---------| |`PER` | = PERSON People, including fictional | |`FAC` | = FACILITY Buildings, airports, highways, bridges, etc. | |`ORG` | = ORGANIZATION Companies, agencies, institutions, etc. | |`GPE... | 43550187ebba871935aca59a2cee8ab2 |
gpl | ['token-classification'] | false | Additional tags (not in OntoNotes 5) Further subtypes of names of type `MISC`: | | | |-|-| |`AWARD`| Awards and prizes | | `CAR` | Cars and other motor vehicles | |`MEDIA`| Media outlets, TV channels, news portals| |`SMEDIA`| Social media platforms| |`PROJ`| Projects and initiatives | |`MISC`| Unresolved subtypes of ... | fa3862a92fc1ef842f350828d7c8698a |
gpl | ['token-classification'] | false | If you use this model, please cite: ```bibtex @inproceedings{novak-novak-2022-nerkor, title = "{N}er{K}or+{C}ars-{O}nto{N}otes++", author = "Nov{\'a}k, Attila and Nov{\'a}k, Borb{\'a}la", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, ... | f4dc123ede5598ef24e1059b0ab8007e |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-partypredictor This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6783 - Accuracy: 0.2495 | 81f8a26d64c47cf8398032f2f3304c2c |
mit | ['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: 16 | f3f610feef7823f7414d3997d4d44c2f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:-----:|:--------:|:---------------:| | 1.7766 | 0.76 | 5000 | 0.1331 | 1.8909 | | 1.7572 | 1.52 | 10000 | 0.1331 | 1.7809 | | 1.7543 | 2.28 | 15000 | 0.1031 | 1.81... | 15807eda3547e8f0280f9d5957c3a9e6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-16-5 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: 0.6537 - Accuracy: 0.6332 | 9bf8a1917819591e4323f2a5e7defb9a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6925 | 1.0 | 7 | 0.6966 | 0.2857 | | 0.6703 | 2.0 | 14 | 0.7045 | 0.2857 | | 0.6404 | 3.0 | 21 | 0.7205 | 0.... | ec49eba04a75af8131a2f3dd15a51122 |
apache-2.0 | ['generated_from_keras_callback'] | false | BERT_Tweet_Sentiment_50k_5eps 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: - Train Loss: 0.0256 - Train Accuracy: 0.9913 - Validation Loss: 0.8905 - Validation Accuracy: 0.8291 - Epoc... | d75affb5db5480a3de387a7172466718 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.3395 | 0.8513 | 0.4071 | 0.8372 | 0 | | 0.1095 | 0.9606 | 0.6561 | 0.8291 ... | 58bbd3ba07ef403671623d250a20bb5c |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_add_GLUE_Experiment_sst2 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4671 - Accuracy: 0.7970 | 70ba0ee03e3d436b35730e2e7b8ee87c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6635 | 1.0 | 527 | 0.6994 | 0.5390 | | 0.5959 | 2.0 | 1054 | 0.6921 | 0.5665 | | 0.5684 | 3.0 | 1581 | 0.7082 | 0.... | 76d6fa47817720dbd1921437ea77d23e |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-billsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0972 - Rouge1: 16.6044 - Rouge2: 12.8656 - Rougel: 15.7876 - Rougelsum: 15.9784 - Gen Len: 18.9948 | 4d5ad95ada16f48483d890ad551558a8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.3854 | 1.0 | 2369 | 2.0972 | 16.6044 | 12.8656 | 15.7876 | 15.9784 | 18... | c371752b4b906f33bee2ccd748ddae40 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_sst2_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9952 - Accuracy: 0.7580 | 35d9cf922cb20b09f1f40f0da9de9226 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.5609 | 1.0 | 264 | 1.0854 | 0.7236 | | 0.9476 | 2.0 | 528 | 0.9952 | 0.7580 | | 0.7472 | 3.0 | 792 | 1.0173 | 0.... | 325ff087e03486cf93311d24f70aa698 |
cc-by-4.0 | ['question-answering, multi-step-reasoning, multi-hop-reasoning'] | false | digit_tokenization.py from https://github.com/stonybrooknlp/teabreac model_name = "StonyBrookNLP/teabreac-t5-large-iirc-retrieved" tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) | b48aaeb6ce45449488f9a2b16c0139fd |
apache-2.0 | ['generated_from_keras_callback'] | false | distil-bert-finetuned-log-parser-1 This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: | 577e5a63bb8a7699f512e0111de8b1b1 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 33, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0... | f4f88acc92c5c182501cc69e1471f08d |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-1'] | false | MultiBERTs Seed 1 Checkpoint 800k (uncased) Seed 1 intermediate checkpoint 800k 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... | c61078e4957d463ef1fbc89a689e8c94 |
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-1-800k') model = BertModel.from_pretrained("multiberts-seed-1-800k") text = "Replace me by any text you'd like.... | da1210ec7b3c72b6012ac2329747400c |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-ko-en-finetuned-ko-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2159 - Bleu: 43.3502 - Gen Len: 3.5474 | 7e5eed869a8f3b9c177e413df5647dff |
apache-2.0 | ['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 - gradient_accumulation_steps: 128 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - nu... | 7b9f67ab1c2ac09b52d8877b1858a1f5 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 0.96 | 20 | 1.3139 | 37.8375 | 3.5612 | | No log | 1.96 | 40 | 1.2849 | 40.9049 | 3.5566 | | No log |... | 08d97af57a6fb9c3b3ebacea13c56b51 |
apache-2.0 | ['multiberts', 'multiberts-seed_17'] | false | MultiBERTs - Seed 17 MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different random seeds, which causes variatio... | 9c6683e887b6e82a8d973e8972f0461f |
apache-2.0 | ['multiberts', 'multiberts-seed_17'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_17') model = TFBertModel.from_pretrained("google/multiberts-seed_... | c530c29f8305f9fd0e87ab8709f91826 |
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: 0.2229 - Accuracy: 0.923 - F1: 0.9230 | 1fda2128181dd2a37c6df4b76ad6e123 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8655 | 1.0 | 250 | 0.3228 | 0.907 | 0.9038 | | 0.2625 | 2.0 | 500 | 0.2229 | 0.923 | 0.9230 | | e8985d0fe7d8663380066b9ca832d6d1 |
creativeml-openrail-m | [] | false | Prompt with **"rizaDB anime girl"** **Training details (as far as I remember):** - Trained with [JoePenna Dreambooth repository](https://github.com/JoePenna/Dreambooth-Stable-Diffusion) - data set: 54 concept images + a number of custom reg images - default learning rate for 4500 steps **Example generations:** ![0... | e382c043a4f6779861dd34f4204e63e1 |
creativeml-openrail-m | ['text-to-image'] | false | Padrecelino Dreambooth model trained by Joscelino with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/not... | f6fcad24b255c6e94c2a61b3c8d56806 |
mit | [] | false | Training data The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset). Most parts of the dataset have been prepended using the following text: `[Genre: <genre1>, <genre2>]` | a74c2201f5e2ef8bbbe8eaacee5a6f86 |
mit | [] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-13B-Nerys') >>> generator("Welcome Captain Janeway, I apo... | 3d3b2492c63f7ed6a5e4630b767f89b9 |
mit | ['generated_from_trainer'] | false | deep-haiku-gpt-j-6b-8bit This model is a fine-tuned version of [gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit) on the [haiku](https://huggingface.co/datasets/statworx/haiku) dataset. | dfa9c868ea1ea9bbb160d21a6d3481bf |
mit | ['generated_from_trainer'] | false | Model description The model is a fine-tuned version of GPT-J-6B-8Bit for generation of [Haikus](https://en.wikipedia.org/wiki/Haiku). The model, data and training procedure is inspired by a [blog post by Robert A. Gonsalves](https://towardsdatascience.com/deep-haiku-teaching-gpt-j-to-compose-with-syllable-patterns-52... | 1e2e2ef36d04c0613af91704aae944e9 |
mit | ['generated_from_trainer'] | false | Intended uses & limitations The model is intended to generate Haikus. To do so, it was trained using a multitask learning approach (see [Caruana 1997](http://www.cs.cornell.edu/~caruana/mlj97.pdf)) with the following four different tasks: : - topic2graphemes `(keywords = text)` - topic2phonemes `<keyw... | b463970db1ac0a5cff3eaa595295b8a2 |
mit | ['generated_from_trainer'] | false | Training and evaluation data We used a collection of existing haikus for training. Furthermore, all haikus were used in their graphemes version as well as a phonemes version. In addition, we extracted key word for all haikus using [KeyBERT](https://github.com/MaartenGr/KeyBERT) and sorted out haikus with a low text q... | 40590e1ad76c0128f90f81d740fdb0c5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 | 65a8124f37fe1b19994605d0aa2e99c2 |
apache-2.0 | ['automatic-speech-recognition', 'br', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | sammy786/wav2vec2-xlsr-breton This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - br dataset. | e9ce69498bee2e00c542e2c051488b2b |
apache-2.0 | ['automatic-speech-recognition', 'br', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000045637994662983496 - train_batch_size: 8 - eval_batch_size: 32 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_typ... | b61483881f36741251ac12e1d2f9a42e |
apache-2.0 | ['automatic-speech-recognition', 'br', 'generated_from_trainer', 'hf-asr-leaderboard', 'model_for_talk', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id sammy786/wav2vec2-xlsr-breton --dataset mozilla-foundation/common_voice_8_0 --config br --split test ``` | bd8a2aaa8b78b89b6320e60e46a8d40a |
apache-2.0 | ['chinese', 'classical chinese', 'literary chinese', 'ancient chinese', 'bert', 'pytorch'] | false | Guwen NER A Classical Chinese Named Entity Recognizer. Note: There are some problems with decoding using the default sequence classification model. Use the CRF model to achieve the best results. CRF related code please refer to [Guwen Models](https://github.com/ethan-yt/guwen-models). See also: <a href="https://g... | a9d1e5824717c4ecd198b0fe32c294f7 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'el', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | wav2vec2-large-xls-r-300m-greek 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 - EL dataset. It achieves the following results on the evaluation set: - Loss: 0.6592 - Wer: 0.4564 | 348708ddf900fe1b8f6e19741b600a52 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_7_0', 'generated_from_trainer', 'el', 'robust-speech-event', 'model_for_talk', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0928 | 4.42 | 500 | 3.0804 | 1.0073 | | 1.4505 | 8.85 | 1000 | 0.9038 | 0.7330 | | 1.2207 | 13.27 | 1500 | 0.7375 | 0.604... | 854df2213c3565c1b9d0040fa7bb182c |
apache-2.0 | ['generated_from_trainer'] | false | Article_50v1_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v1_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.7237 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7775 | 71b77ee80b16567c53cf78f1fcf1f853 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 7 | 0.9016 | 0.12 | 0.0007 | 0.0015 | 0.7772 | | No log | 2.0 |... | 7efada88721ff7c9b7c4348ad5972e8d |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_wavlm_s515 Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 1... | 7ef96364261810b74900c9d3fc2ad8f1 |
mit | [] | false | WideResNet50 model ported from [torchvision](https://pytorch.org/vision/stable/index.html) for use with [Metalhead.jl](https://github.com/FluxML/Metalhead.jl). The scripts for creating this file can be found at [this gist](https://gist.github.com/darsnack/bfb8594cf5fdc702bdacb66586f518ef). To use this model in Julia,... | 2c54e92b34d05f8137fbd0fed4dcca07 |
other | ['generated_from_trainer'] | false | amazon-reviews-input-output-6.7b-best This model is a fine-tuned version of [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b) on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set: - Loss: 2.6953 - Accuracy: 0.0403 - Samples: 100 - Perplexity: 1... | 97b7df497581ebb4bc3b634db69c5b72 |
other | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | f41683b28e22e274db771e6556e06620 |
other | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.9912 | 0.06 | 1 | 2.7441 | 0.0404 | | 2.9329 | 0.12 | 2 | 2.7441 | 0.0404 | | 2.9138 | 0.19 | 3 | 2.8262 | 0.... | a550aaae106ccac97cd8cfd633357327 |
cc-by-sa-4.0 | ['chinese', 'masked-lm', 'wikipedia'] | false | Model Description This is a RoBERTa model pre-trained on Chinese Wikipedia texts (both simplified and traditional). NVIDIA A100-SXM4-40GB took 48 hours 56 minutes for training. You can fine-tune `roberta-base-chinese` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-chines... | 40bb2a0e2d9f0f6c846be068fd71defe |
cc-by-sa-4.0 | ['chinese', 'masked-lm', 'wikipedia'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-chinese") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-chinese") ``` | 7e20a6abf7bebae8f8ffad2e5c3f730f |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit-base-beans-demo-v5 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 Cifar100 dataset. It achieves the following results on the evaluation set: - Loss: 0.4420 - Accuracy: 0.8985 | 8c1a0b454dfaf259c3276af819039300 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.08 | 1.0 | 3125 | 0.6196 | 0.8262 | | 0.3816 | 2.0 | 6250 | 0.5322 | 0.8555 | | 0.1619 | 3.0 | 9375 | 0.4817 ... | 5b1d796454064e9af46620bd285b9020 |
apache-2.0 | ['translation'] | false | opus-mt-af-en * source languages: af * target languages: en * OPUS readme: [af-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://... | 7f980ada64ffc21e52569760631f1211 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | false | MultiBERTs Seed 3 Checkpoint 1700k (uncased) Seed 3 intermediate checkpoint 1700k 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/g... | a140a75190305ecbb8f1a2f047c95cd6 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-3'] | 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-3-1700k') model = BertModel.from_pretrained("multiberts-seed-3-1700k") text = "Replace me by any text you'd lik... | b252aaf18391ac2ed71432f2ab29d51a |
bsd-3-clause | ['generated_from_trainer'] | false | ast-finetuned-audioset-10-10-0.4593-finetuning-ESC-50 This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the ESC-50 dataset. It achieves the following results on the evaluation set: - Loss: 0.3356 - Accuracy: 0.9464 | 24822214464fde32973713004dde8237 |
bsd-3-clause | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_schedu... | 1cc82fec804b9b3324ef13c5059f342d |
bsd-3-clause | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0621 | 1.0 | 28 | 0.4656 | 0.875 | | 0.0694 | 2.0 | 56 | 0.3050 | 0.9107 | | 0.0157 | 3.0 | 84 | 0.3356 | 0.... | 45bca41a18aa8572d7e394750554fa9c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 5e24ad552108a4611a4ed59aa601162e |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `research-backup/t5-large-tweetqa-qag-np` This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417... | 11784c84ea4d21c31f638485e9501028 |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-large-tweetqa-qag-np") output = pipe("Beyonc... | de42378a6b0ec62f58f42b6fd60155fd |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-large-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset ... | ceaf7ef23f9776c3e8bea0bf468943af |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: t5-large - max_length: 256 - max_length_output: 128 - epoch: 16 - bat... | 4d18c2130092482bea2be7a395ad1efc |
apache-2.0 | ['vision', 'image-classification'] | false | RegNet RegNet model trained on imagenet-1k. It was introduced in the paper [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) and first released in [this repository](https://github.com/facebookresearch/pycl). Disclaimer: The team releasing RegNet did not write a model card for this model so this mo... | f26f31e87fd355246d589d3dceb35dfc |
apache-2.0 | ['vision', 'image-classification'] | false | How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetForImageClassification >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeature... | b81ee23485505a525d9e3a702536d6aa |
apache-2.0 | ['translation'] | false | opus-mt-pag-es * source languages: pag * target languages: es * OPUS readme: [pag-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pag-es/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](http... | a6c2629a5a53d6ac1e8f682b1b936731 |
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: 0.2255 - Accuracy: 0.9185 - F1: 0.9186 | d364d1bba2c248f586423d5d30d6deb7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8256 | 1.0 | 250 | 0.3137 | 0.906 | 0.9029 | | 0.2514 | 2.0 | 500 | 0.2255 | 0.9185 | 0.9186 | | c852e86b6bc64967f1b2674364bf673e |
apache-2.0 | ['automatic-speech-recognition', 'pl'] | false | exp_w2v2t_pl_wav2vec2_s840 Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition using the train split of [Common Voice 7.0 (pl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech inpu... | e772a8559de4340085af0296801b2797 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the carrotey concept trained by jonathang on the jonathang/dreambooth-hackathon-images dataset. This is a Stable Diffusion model fine-tuned on the carrotey concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of carrotey vegetable** This model was created as part... | d9cf91bb545820da9480b5d3c5cda193 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-8 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: 3.1169 - Wer: 1.0 | ca323683c360659033616f6ff18d1ec3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0006 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | fdf3b0656e2d44dd534e7fe441abcf2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.9398 | 1.56 | 200 | 3.1250 | 1.0 | | 2.8703 | 3.12 | 400 | 3.1608 | 1.0 | | 2.8632 | 4.69 | 600 | 3.1329 | 1.0 | | 2.8638 ... | 2ba1a5f6856bffc463f9e929343d6317 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_cola_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6791 - Matthews Correlation: 0.0773 | 0eae9a7cc35d7555838c7d2832b4c8e6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.558 | 1.0 | 835 | 0.6791 | 0.0773 | | 0.4341 | 2.0 | 1670 | 0.7597 | 0.0700 | | 0.3... | afbd7fc3a3f297b8be0162e45db77fac |
apache-2.0 | ['text-classification', 'pytorch'] | false | Model description: This model was created with the purpose to detect toxic or potentially harmful comments. For this model, we finetuned a dutch RobBerta-based model called [RobBERT](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the translated [Jigsaw Toxicity dataset](https://www.kaggle.com/c/jigsaw-to... | b37aedbc8636592cb4d45ce73abb73cc |
apache-2.0 | ['text-classification', 'pytorch'] | false | Model Performance: Model evaluation was done on 1/10th of the dataset, which served as the test dataset. | Accuracy | F1 Score | Recall | Precision | | --- | --- | --- | --- | | 95.63 | 78.80 | 78.99 | 78.61 | | 276d6a00c557d820a81e85a909fad57a |
mit | [] | false | MTG card on Stable Diffusion This is the `<mtg-card>` 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... | 15e7e3e357eb4932ae939b801aefbce8 |
apache-2.0 | ['generated_from_keras_callback'] | false | jiseong/mt5-small-finetuned-news-ab 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: - Train Loss: 2.0174 - Validation Loss: 1.7411 - Epoch: 3 | 4c547d4d5f226f08e34c38b5fcfc2fe5 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 4.1124 | 2.0706 | 0 | | 2.4090 | 1.8742 | 1 | | 2.1379 | 1.7889 | 2 | | 2.0174 | 1.7411 | 3 | | 831bdc342a72d80a1fee05258b7740ea |
apache-2.0 | ['generated_from_trainer'] | false | ner-bert-german This model can be used to do [named-entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) in German. It is trained on a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the German wikiann dataset. It achieves the follo... | f7cde4df6be95fc406bddba88c3d9214 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:| | 0.252... | 62a75bbb80f0637977eaf0a4b66f895d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-torgo-8batch-30epochs-500steps This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0747 - Cer: 0.3015 | c944603a66c167b83be76bb6dadb385d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 25.2161 | 2.53 | 500 | 5.2319 | 0.9816 | | 3.649 | 5.05 | 1000 | 3.7028 | 0.9816 | | 2.8241 | 7.58 | 1500 | 2.0426 | 0.8802 | |... | 1e28b45a1517f0cc4b3d45f4ed960d36 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.0525 - Accuracy: 0.9815 | 48483199eb9913f9a12f18b3fd2bd930 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2396 | 1.0 | 190 | 0.1071 | 0.9656 | | 0.1605 | 2.0 | 380 | 0.0665 | 0.9793 | | 0.1282 | 3.0 | 570 | 0.0525 | 0.... | 0dc5aeff842bba5e927466b5ce130237 |
apache-2.0 | ['generated_from_trainer'] | false | bigbird-pegasus-large-arxiv-finetuned-roundup-280922 This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) on the None dataset. | 1a339d59d36336b84e6decda14000a91 |
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 | 101 | 5.5104 | 26.9401 | 4.5002 | 18.3317 | 20.6102 | 205... | c5a928468e1d514b6dc46f4820dc75fe |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 11d687844a544fcce6f6d0ce7a0a302e0e47d442 pip install -e . cd egs2/l3das22/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/Yen-Ju_Lu_l3das22_enh_train_enh_ineube_valid.loss.ave ``` <!-- Generated by ./scripts/utils/show_enh_scor... | 5872e6a11bc80866ccb34bd34c6a0a1e |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Environments - date: `Wed Jul 6 20:46:10 UTC 2022` - python version: `3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]` - espnet version: `espnet 202205` - pytorch version: `pytorch 1.8.1` - Git hash: `77e36afdd3f069567dd33d4b5b997a26b634772b` - Commit date: `Fri Jun 17 18:32:56 2022 -0400` | 37d15227d07b39113b901fbb77527fe7 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | enh_train_enh_ineube_raw config: conf/tuning/train_enh_ineube.yaml |dataset|STOI|SAR|SDR|SIR|SI_SNR|WER|STOI|TASK 1 METRIC| |---|---|---|---|---|---|---|---|---| |enhanced_dev_multich|95.62|15.00|15.00|0.00|13.64|5.93|0.956|0.948| |enhanced_test_multich|95.70|14.59|14.59|0.00|13.34|4.85|0.957|0.954| | 66b06389d9b0c33bbf1382d79f5e83d6 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_ineube.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_ineube_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 3 di... | e0cfdbb1464be024b54f9bd99c905916 |
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: 3.6428 | 576c98fa84dd71662f5c48044a471c98 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.