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-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0176 - Precision: 0.9485 - Recall: 0.9579 - F1: 0.9532 - Accuracy: 0.9920 | 7b4cef0b89df129142971638f68c41b8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.003 | 1.0 | 1756 | 0.0180 | 0.9397 | 0.9461 | 0.9429 | 0.9908 | | 0.0013 | 2.0 |... | aea7f75b47bc927fc195196354d91d11 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-language-detection-finetuned-ner This model is a fine-tuned version of [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4196 - Precision: 0.9394 - R... | 10e92c505c36cb11bd4fc31bacc98cbb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 288 | 0.0389 | 0.9911 | 0.9900 | 0.9906 | 0.9905 | | 0.1254 | 2.0 |... | 7eab2df86de10a3203e5c3fe4f038cd8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `pyf98/librispeech_100_e_branchformer` This model was trained by Yifan Peng using librispeech_100 recipe in [espnet](https://github.com/espnet/espnet/). References: - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077) - [Branchformer: Parallel MLP... | 88510a5d690b0a8498a326fa3ad4baea |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 3c84766d951e33dd7782a9f32011c00ea2a44ea3 pip install -e . cd egs2/librispeech_100/asr1 ./run.sh --skip_data_prep false --skip_t... | 9eecb4977eedbd1b53b068ea1b890016 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Mon Dec 12 06:50:58 CST 2022` - python version: `3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.12.1` - Git hash: `26f432bc859e5e40cac1a86042d498ba7baffbb0` - Commit date: `Fri Dec 9 02:16:01 2022 +0000` | 3345b5ae53e28f4b1709b9007d52e453 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|54402|94.6|5.0|0.3|0.8|6.1|55.4| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|50948|85.3|13.3|1.4|2.1|16.7|78.9| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|52576|94.... | c256abfd57b408803e5cb4e467002716 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|288456|98.3|1.0|0.7|0.7|2.4|55.4| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|265951|93.6|4.0|2.4|2.0|8.3|78.9| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|281530|98... | ef267297be831939b54c6cb91247a2ed |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_asr_model_valid.acc.ave/dev_clean|2703|69558|92.2|4.9|2.9|0.6|8.4|55.4| |decode_asr_asr_model_valid.acc.ave/dev_other|2864|64524|81.9|12.8|5.2|2.3|20.4|78.9| |decode_asr_asr_model_valid.acc.ave/test_clean|2620|66983|92.... | 78c34fdfd557d33b9121e1bf95c88851 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_e_branchformer_size256_mlp1024_linear1024_e12_mactrue_edrop0.0_ddrop0.0.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_e_branchformer_size256_mlp1024_linear1024_e12_mactrue_e... | 2621c810a1d9e2ac8a0ba3242060d969 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-9rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4673 - Wer: 0.1766 - Mer: 0.1707 - Wil: 0.2594 - Wip: 0.7406 - Hits: 55410 - S... | 5b88ffb938cf71184613ffd3257f7136 |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.641 ... | d74c9d74d7fc89c5eed41480d6b3a5d9 |
apache-2.0 | ['generated_from_trainer', 'course', 'fine-tuned', 'bert'] | false | Intended uses & limitations Intended use here is to create a working model to teach myself and others about how to use transformers for Questions/Answering tasks. The limitation is that this approach expects input data to always have __context, question, answer__ and to use the syntax [MASK] for this use case. | 77a6fdec9254bfd54ee41ec370138e91 |
apache-2.0 | ['generated_from_trainer', 'course', 'fine-tuned', 'bert'] | false | Training procedure Setup files with the expected format from pretrained model (__context, question, answer__) `display("Context: ", raw_datasets["train"][0]["context"])` `display("Question: ", raw_datasets["train"][0]["question"])` `display("Answer: ", raw_datasets["train"][0]["answers"])` > output for code above > ... | a5659b79c9c4817e85e5a3f5e260f5ff |
openrail | ['text-to-image'] | false | El Risitas model This is the fine-tuned Stable Diffusion model trained on images from El Risitas. Use **Elrisitas** in your prompts. Update : V2 is out  | 8b6779556a69440b2177d15eb8f6e3c9 |
openrail | ['text-to-image'] | false | Sample images:   Based on StableDiffusion 1.5 model | b6fe481917ab0c67faae3e73c8bd9ae7 |
openrail | ['text-to-image'] | false | 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [... | 36b2019ff9148a57ce1cadb84cf31aa2 |
other | [] | false | Tile Grout Cleaning Lewisville TX https://carpetcleaninglewisville.com/tile-grout-cleaning.html 972-338-5376 Why look for the best grout cleaner machine for hours and then spend a few more hours doing it yourself?When all you have to do is call Carpet Cleaning Lewisville, TX, and you'll get expert, green service from a... | a0e632df0b4d36ab8aba0853131d63eb |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-16-16-5-oos This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3586 - Accuracy: 0.3689 | 51aec935b1680be3cc2df67555855b9b |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | exper3_mesum5 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 sudo-s/herbier_mesuem5 dataset. It achieves the following results on the evaluation set: - Loss: 0.6366 - Accuracy: 0.8367 | 99ef02073019348a42ff3077e206553a |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.895 | 0.23 | 100 | 3.8276 | 0.1935 | | 3.1174 | 0.47 | 200 | 3.1217 | 0.3107 | | 2.6 | 0.7 | 300 | 2.5399 | 0.... | 0841126d19bdb9cc5e952cb9670bdc75 |
mit | ['generated_from_trainer'] | false | BERiT_2000_ls This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.9311 | 82b8004e7dd0bfe59a4eb63a4a5104f8 |
mit | ['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - label_smoothing_factor: 0.2 | 127397a3664484240a877ba463b805e4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0431 | 0.19 | 500 | 6.9715 | | 6.9481 | 0.39 | 1000 | 6.9600 | | 6.9487 | 0.58 | 1500 | 6.9444 | | 6.9495 | 0.77 | 2000 | 6.9505 ... | 45632bf6b9dd143ff1cb0aebaf38f99b |
mit | ['generated_from_trainer'] | false | roberta-base_fold_1_binary_v1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4984 - F1: 0.8339 | e27e4a26e067da1f84094721815e4722 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 288 | 0.3819 | 0.8117 | | 0.4108 | 2.0 | 576 | 0.3696 | 0.8281 | | 0.4108 | 3.0 | 864 | 0.4890 | 0.8343 | |... | 8b695d6bbb999a40a54f2601d873a69b |
mit | ['indonesian-roberta-base-sentiment-classifier'] | false | Indonesian RoBERTa Base Sentiment Classifier Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) mo... | 01098efe9a3210f693e6f593f239584f |
mit | ['indonesian-roberta-base-sentiment-classifier'] | false | params | Arch. | Training/Validation data (text) | | ---------------------------------------------- | ------- | ------------ | ------------------------------- | | `indonesian-roberta-base-sentiment-classifier` | 124M | RoBERTa Base | `SmSA` | | 3f30629aded2fdd0eedd805736813d02 |
mit | ['indonesian-roberta-base-sentiment-classifier'] | false | Evaluation Results The model was trained for 5 epochs and the best model was loaded at the end. | Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | | ----- | ------------- | --------------- | -------- | -------- | --------- | -------- | | 1 | 0.342600 | 0.213551 |... | 9d2782aad44ab0180ab640bc6e6e7644 |
mit | ['indonesian-roberta-base-sentiment-classifier'] | false | As Text Classifier ```python from transformers import pipeline pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("Jangan sampai saya telpon bos saya ya!") ``` | 9beda8cb5e5478d57465be9a3034e301 |
mit | ['indonesian-roberta-base-sentiment-classifier'] | false | Author Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. | 7e402174b6ae43604bbf3b9dc0d8c9bb |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-deletion-squad-10 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0246 | 615310f305d2f8d90518739f4d48dcde |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-finetuned-bruno-mars 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: 2.4055 | 3366c2af534f1e295797dcb8677df7d7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 62 | 2.5992 | | No log | 2.0 | 124 | 2.4069 | | No log | 3.0 | 186 | 2.4055 | | e05f4b3ed6a0503b7b16f0e93f7bbe22 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab1 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: 3.1918 - Wer: 1.0 | dc1816a5b5f1756ad78fcaabf9ef25c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - 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: 1000 - num_epochs: 30 - mixed_precision_tr... | 080ee384e38b67a9cebc4f278d8c61de |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.7104 | 13.89 | 500 | 3.2161 | 1.0 | | 3.1868 | 27.78 | 1000 | 3.1918 | 1.0 | | a6716c528ef7601eb4f79d32eb12b6ae |
mit | ['exbert'] | false | Overview **Language model:** gbert-base-germandpr **Language:** German **Training data:** GermanDPR train set (~ 56MB) **Eval data:** GermanDPR test set (~ 6MB) **Infrastructure**: 4x V100 GPU **Published**: Apr 26th, 2021 | 58d4bf80596f7cd4bc7e6562a3526896 |
mit | ['exbert'] | false | Details - We trained a dense passage retrieval model with two gbert-base models as encoders of questions and passages. - The dataset is GermanDPR, a new, German language dataset, which we hand-annotated and published [online](https://deepset.ai/germanquad). - It comprises 9275 question/answer pairs in the training set... | fbf08abab31921e1e1e5b101c264345f |
mit | ['exbert'] | false | Hyperparameters ``` batch_size = 40 n_epochs = 20 num_training_steps = 4640 num_warmup_steps = 460 max_seq_len = 32 tokens for question encoder and 300 tokens for passage encoder learning_rate = 1e-6 lr_schedule = LinearWarmup embeds_dropout_prob = 0.1 num_hard_negatives = 2 ``` | ba742e3603c505b25ddeb237fe0a1fb1 |
mit | ['exbert'] | false | Performance During training, we monitored the in-batch average rank and the loss and evaluated different batch sizes, numbers of epochs, and number of hard negatives on a dev set split from the train set. The dev split contained 1030 question/answer pairs. Even without thorough hyperparameter tuning, we observed quite... | 77909260d023b8bd9ce1e2800a02ca95 |
mit | ['exbert'] | false | In haystack You can load the model in [haystack](https://github.com/deepset-ai/haystack/) as a retriever for doing QA at scale: ```python retriever = DensePassageRetriever( document_store=document_store, query_embedding_model="deepset/gbert-base-germandpr-question_encoder" passage_embedding_model="deepset/gbert-... | 46b90fb3a3c3815fe395c3a934ba45ef |
mit | ['exbert'] | false | About us  We bring NLP to the industry via open source! Our focus: Industry specific language models & large scale QA systems. Some of our work: - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert) - ... | 01d1410d771e4bbd31237f2a83edd88f |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.4626 | 5ff49c6b336bbdb35d4c8f0e3551a571 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6963 | 1.0 | 157 | 2.5091 | | 2.5737 | 2.0 | 314 | 2.4515 | | 2.5496 | 3.0 | 471 | 2.3946 | | 131a5f25868989fc9e61098275f1c15d |
apache-2.0 | ['generated_from_keras_callback'] | false | distilbert-base-uncased-finetuned-powo 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: | c29661b83cd99e46a2427f70f7f492c0 |
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... | acd15327a3dc470a698629dd17422c84 |
apache-2.0 | ['generated_from_trainer'] | false | textClass-finetuned-coba 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: 1.0553 - Accuracy: 0.7528 | 39d1434c2bef7abe14e7d70868f40bc8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.5219 | 1.0 | 2065 | 0.4944 | 0.7641 | | 0.4174 | 2.0 | 4130 | 0.5008 | 0.7726 | | 0.3071 | 3.0 | 6195 | 0.6225 ... | e152ac63fb7a01fcffd4a13a7b137047 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | 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: 0.0840 - Accuracy: 0.9850 | 49263952e06eccc2fa1a1977216f75d7 |
apache-2.0 | ['image-classification', 'vision', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.2538 | 1.0 | 130 | 0.9624 | 0.2253 | | 0.2861 | 2.0 | 260 | 0.9925 | 0.1086 | | 0.1625 | 3.0 | 390 | 0.0990 | 0.9925 ... | 573461efa6e6affb1ea99161871dcfa8 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | false | MultiBERTs Seed 4 Checkpoint 1600k (uncased) Seed 4 intermediate checkpoint 1600k 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... | d66ff27869a889d6dd0be94d186eaa78 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-4'] | 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-4-1600k') model = BertModel.from_pretrained("multiberts-seed-4-1600k") text = "Replace me by any text you'd lik... | 3fe4c25abecb08b6fc731b332b260d61 |
openrail | [] | false | This model is converted from [stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) using the [conversion script](https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py) from [🤗 Diffusers](https://github.com/huggingface/diffusers).... | b118ac94a74bd26dd9946be535bbed49 |
openrail | [] | false | Use the pipeline from this GH Gist: https://gist.github.com/carson-katri/f51532b9d5162928d5cacbaee081a799 pipe = StableDiffusionDepthPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") image = pipe( prompt="a photo of a stormtrooper from star wars", depth_image=Image.open('depth.png'), | 556ac8e4b86d2ba9014ed20f91419865 |
['apache-2.0'] | [] | false | ```python import jieba_fast from transformers import BertTokenizer from transformers import BigBirdModel class JiebaTokenizer(BertTokenizer): def __init__( self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs ): super().__init__(*args, **kwargs) self.pre_tokenizer ... | 65e892af76d4d05652a29d07b3d03853 |
mit | ['image-classification', 'created-with-modelcards'] | false | Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [Optional]:** [More Information Needed] - **Model type:** Language model - **Language(s) (NLP):** en - **License:** mit - **Related Models:** [More Information Needed] - **Parent... | 0f778ed09b06eadb401277a6ffb189cb |
apache-2.0 | ['translation'] | false | opus-mt-fi-ve * source languages: fi * target languages: ve * OPUS readme: [fi-ve](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-ve/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | a7f0d786e3fe88b6a9b225afd343db0b |
creativeml-openrail-m | ['text-to-image'] | false | sd-tzvc Dreambooth model trained by tzvc with the v1-5 base model Prompt concept name: sdtzvc Training images: * abstractswirls    mark zuckerberg portrait, abstracts... | 03de0899f9c766eb76833784f02528cc |
apache-2.0 | ['image-to-text', 'image-captioning'] | false | nlpconnect/vit-gpt2-image-captioning This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts). | a20f93a8fb322444597f4781f083cda5 |
apache-2.0 | ['image-to-text', 'image-captioning'] | false | The Illustrated Image Captioning using transformers  * https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/ | e724f294e55ac22592b3e5698a82302b |
apache-2.0 | ['image-to-text', 'image-captioning'] | false | Sample running code ```python from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer import torch from PIL import Image model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gp... | cfb6a83febcf876b32afe1bbde8bf824 |
apache-2.0 | ['image-to-text', 'image-captioning'] | false | Sample running code using transformers pipeline ```python from transformers import pipeline image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png") | 65b9aae1512416bff9569083bf149e6c |
apache-2.0 | ['translation'] | false | opus-mt-fi-toi * source languages: fi * target languages: toi * OPUS readme: [fi-toi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-toi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-24.zip](http... | 9e57d73136f9731c42317341cda7673d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the /WORKSPACE/DATA/HY/NOIZY_STUDENT_3/ - NA dataset. It achieves the following results on the evaluation set: - Loss: 0.2293 - Wer: 0.3333 - Cer: 0.0602 | f1f2fe4e87dfc5bed8c557b633ef3151 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 842 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_sc... | e8e0baa308b024154e9a4b0545f5ee80 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hy', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 3.1471 | 7.02 | 400 | 3.1599 | 1.0 | 1.0 | | 1.8691 | 14.04 | 800 | 0.7674 | 0.7361 | 0.1686 | | 1.3227 | 21.05 |... | d651024acb6803fffb497e5bc0610697 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_mrpc_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.4961 - Accuracy: 0.6912 - F1: 0.7968 - Combined Score: 0.7440... | 195f845355dd82bf8a0627349e65967f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:| | 0.6315 | 1.0 | 29 | 0.5588 | 0.6838 | 0.8122 | 0.7480 | | 0.6098 | 2.0 | 58 | 0.55... | fe88997b264247e3bda3ef7815177696 |
apache-2.0 | ['generated_from_trainer'] | false | gpt-neo-125M-wikitext2 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0325 | 644e424b0ad443456246e4a28dce19a9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 6.4308 | | 6.8563 | 2.0 | 518 | 6.0898 | | 6.8563 | 3.0 | 777 | 6.0325 | | 41eabda87207290e9b49a094a048baaa |
apache-2.0 | ['korean'] | false | KcBERT: Korean comments BERT ** Updates on 2021.04.07 ** - KcELECTRA가 릴리즈 되었습니다!🤗 - KcELECTRA는 보다 더 많은 데이터셋, 그리고 더 큰 General vocab을 통해 KcBERT 대비 **모든 태스크에서 더 높은 성능**을 보입니다. - 아래 깃헙 링크에서 직접 사용해보세요! - https://github.com/Beomi/KcELECTRA ** Updates on 2021.03.14 ** - KcBERT Paper 인용 표기를 추가하였습니다.(bibtex) - KcBERT-fine... | 6cb9c9e83f48c0d954ce20c85f49b0d0 |
apache-2.0 | ['korean'] | false | KcBERT Performance - Finetune 코드는 https://github.com/Beomi/KcBERT-finetune 에서 찾아보실 수 있습니다. | | Size<br/>(용량) | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | | :... | c13f9f5554419809cddb8a9083548e53 |
apache-2.0 | ['korean'] | false | Requirements - `pytorch <= 1.8.0` - `transformers ~= 3.0.1` - `transformers ~= 4.0.0` 도 호환됩니다. - `emoji ~= 0.6.0` - `soynlp ~= 0.0.493` ```python from transformers import AutoTokenizer, AutoModelWithLMHead | 74a4064d205fef9e1359cde34c15c49c |
apache-2.0 | ['korean'] | false | Pretrain Data - [데이터셋 다운로드(Kaggle, 단일파일, 로그인 필요)](https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments) - [데이터셋 다운로드(Github, 압축 여러파일, 로그인 불필요)](https://github.com/Beomi/KcBERT/releases/tag/TrainData_v1) | 54a39c54970f65a77ce542355d9a2bac |
apache-2.0 | ['korean'] | false | Pretrain Code Colab에서 TPU로 KcBERT Pretrain 해보기: <a href="https://colab.research.google.com/drive/1lYBYtaXqt9S733OXdXvrvC09ysKFN30W"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | a864e4097d3982aa40f58e376a749e86 |
apache-2.0 | ['korean'] | false | Finetune Samples **KcBERT-Base** NSMC Finetuning with PyTorch-Lightning (Colab) <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> **KcBERT-Large** NSMC Finetuning with PyTor... | 9e14b78b373494463ce45a26eb997793 |
apache-2.0 | ['korean'] | false | Preprocessing PLM 학습을 위해서 전처리를 진행한 과정은 다음과 같습니다. 1. 한글 및 영어, 특수문자, 그리고 이모지(🥳)까지! 정규표현식을 통해 한글, 영어, 특수문자를 포함해 Emoji까지 학습 대상에 포함했습니다. 한편, 한글 범위를 `ㄱ-ㅎ가-힣` 으로 지정해 `ㄱ-힣` 내의 한자를 제외했습니다. 2. 댓글 내 중복 문자열 축약 `ㅋㅋㅋㅋㅋ`와 같이 중복된 글자를 `ㅋㅋ`와 같은 것으로 합쳤습니다. 3. Cased Model KcBERT는 영문에 대해서는 대소문자를 유지하는 Cased model입니다. ... | e7ce340db8a4ee1b7017e9d20e0c1dc9 |
apache-2.0 | ['korean'] | false | Tokenizer Train Tokenizer는 Huggingface의 [Tokenizers](https://github.com/huggingface/tokenizers) 라이브러리를 통해 학습을 진행했습니다. 그 중 `BertWordPieceTokenizer` 를 이용해 학습을 진행했고, Vocab Size는 `30000`으로 진행했습니다. Tokenizer를 학습하는 것에는 `1/10`로 샘플링한 데이터로 학습을 진행했고, 보다 골고루 샘플링하기 위해 일자별로 stratify를 지정한 뒤 햑습을 진행했습니다. | ca296b9ff3db50f5675e733afca8f5d0 |
apache-2.0 | ['korean'] | false | BERT Model Pretrain - KcBERT Base config ```json { "max_position_embeddings": 300, "hidden_dropout_prob": 0.1, "hidden_act": "gelu", "initializer_range": 0.02, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 30000, "hidden_size": 768, "attention_probs_dropout_prob": 0... | 05dede724ed4a101f80370361aaed384 |
apache-2.0 | ['korean'] | false | HuggingFace MASK LM [HuggingFace kcbert-base 모델](https://huggingface.co/beomi/kcbert-base?text=오늘은+날씨가+[MASK]) 에서 아래와 같이 테스트 해 볼 수 있습니다.  물론 [kcbert-large 모델](https://huggingface.co/beomi/kcber... | 7fef603ef6a075f92a6b56320e6e9989 |
apache-2.0 | ['korean'] | false | NSMC Binary Classification [네이버 영화평 코퍼스](https://github.com/e9t/nsmc) 데이터셋을 대상으로 Fine Tuning을 진행해 성능을 간단히 테스트해보았습니다. Base Model을 Fine Tune하는 코드는 <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="... | 326ce88bb712e08fd98039c36f101729 |
apache-2.0 | ['korean'] | false | 실험결과 - KcBERT-Base Model 실험결과: Val acc `.8905`  - KcBERT-Large Model 실험 결과: Val acc `.9089`  - [KoBERT by SKT](https://github.com/SKTBrain/KoBERT) - [KoELECTRA by Monologg](https://github.com/monologg/KoELECTRA/) - [Transformers by Huggingface](https://github.com/huggingface/transformers) - [Tokenizers by Hugginface](https://github.com... | 4809db48a06ec49fd6d4f5618a3b0237 |
apache-2.0 | ['korean'] | false | Blogs - [Monologg님의 KoELECTRA 학습기](https://monologg.kr/categories/NLP/ELECTRA/) - [Colab에서 TPU로 BERT 처음부터 학습시키기 - Tensorflow/Google ver.](https://beomi.github.io/2020/02/26/Train-BERT-from-scratch-on-colab-TPU-Tensorflow-ver/) | 2fb3df9e7446aa9dd03c9bfca6b97e9d |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Model description **flair-uk-pos** is a Flair model that is ready to use for part-of-speech (upos) tagging. It is based on flair embeddings, that I've trained for Ukrainian language (available [here](https://huggingface.co/dchaplinsky/flair-uk-backward) and [here](https://huggingface.co/dchaplinsky/flair-uk-forward))... | 66e65f33eb50d05f83c36af9036b2d1b |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | [Usage demo](https://github.com/egorsmkv/flair-nlp-uk/blob/main/part_of_speech.py) ```python from flair.data import Sentence from flair.models import SequenceTagger from pprint import pprint tagger = SequenceTagger.load("dchaplinsky/flair-uk-pos") sentence = Sentence("Я люблю Україну. Моє імʼя Марія Шевченко, я нав... | 9f05619d53512ac945443210eab7ccd3 |
mit | ['flair', 'token-classification', 'sequence-tagger-model'] | false | Result: """ Sentence: "Я люблю Україну . Моє імʼя Марія Шевченко , я навчаюся в Київській політехніці ." → ["Я"/PRON, "люблю"/VERB, "Україну"/PROPN, "."/PUNCT, "Моє"/DET, "імʼя"/NOUN, "Марія"/PROPN, "Шевченко"/PROPN, ","/PUNCT, "я"/PRON, "навчаюся"/VERB, "в"/ADP, "Київській"/ADJ, "політехніці"/NOUN, "."/PUNCT] --- The... | 1114f488e56c3c9fd1a815a1aaef915f |
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 an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6550 - Matthews Correlation: 0.2820 | 6bdf1b015017b41a5703db8e343bf22d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 1.7255 | 1.0 | 712 | 1.6687 | 0.1995 | | 1.3584 | 2.0 | 1424 | 1.6550 | 0.2820 | | 1.0... | 52a25372c3369c64fdbaa8b45cd09f31 |
apache-2.0 | ['translation'] | false | opus-mt-yo-en * source languages: yo * target languages: en * OPUS readme: [yo-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 104d82ac54913b76551f31f482395400 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-cased-finetuned-filtered-0609 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: - Loss: 0.2410 - Accuracy: 0.9748 - Precision: 0.9751 - Recall: 0.9748 - F1: 0.9749 | 16e1c500cc9de9011869272e8fe02cb9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 0.2028 | 1.0 | 3180 | 0.2405 | 0.9535 | 0.9561 | 0.9535 | 0.9538 | | 0.1632 | 2.0 ... | 2b807982ed98e24056aa1a62c3853bf2 |
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: 0.2133 - Accuracy: 0.926 - F1: 0.9259 | 1f8b5c65f6824e980fef6f846a6a79ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8175 | 1.0 | 250 | 0.3067 | 0.906 | 0.9025 | | 0.2452 | 2.0 | 500 | 0.2133 | 0.926 | 0.9259 | | c9de02557290e5bd5d60027562800cc3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.7608 | 1.0 | 2334 | 3.6655 | | 3.6335 | 2.0 | 4668 | 3.6455 | | 3.6066 | 3.0 | 7002 | 3.6424 | | b613063bddf6e378b4b4f94a812d8863 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout 49a284e69308d81c142b89795de255b4ce290c54 pip install -e . cd egs2/talromur/tts1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/GunnarThor_talromur_h_tacotron2 ``` | e3b51a609bc0339a5614f29be876ab24 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.