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', 'named-entity-recognition', 'token-classification'] | false | Bertweet-base finetuned on wnut17_ner This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the [wnut_17](https://huggingface.co/datasets/wnut_17) dataset. It achieves the following results on the evaluation set: - Loss: 0.3376 - Overall Precision: 0.6803 - Overal... | c40d4ee47d83b873fa19f0d5f1d269db |
apache-2.0 | ['generated_from_trainer', 'named-entity-recognition', 'token-classification'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 100 | 72dad37b161f41d3abd09e55924d67c3 |
apache-2.0 | ['generated_from_trainer', 'named-entity-recognition', 'token-classification'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Corporation F1 | Creative-work F1 | Group F1 | Location F1 | Person F1 | Product F1 | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:-------... | 3db2df766686df9cbe45b4a7c1b6b57a |
apache-2.0 | ['generated_from_trainer', 'named-entity-recognition', 'token-classification'] | false | Overall results | metric_type | train | validation | test | |:-------------------|-----------:|-----------:|-----------:| | loss | 0.012030 | 0.271155 | 0.273943 | | runtime | 16.292400 | 5.068800 | 8.596800 | | samples_per_second | 208.318000 | 199.060000 | 149.7... | 286dfd87e6f9370c660bb47b87cdead9 |
apache-2.0 | ['translation'] | false | opus-mt-fi-efi * source languages: fi * target languages: efi * OPUS readme: [fi-efi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-efi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | ed57582c266aaee4bd1e0409872de809 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | sentence-transformers/roberta-base-nli-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. | 8d0d85ab025d3c567be68e1c77e5eaf2 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sen... | 9ca8795c3544e1427f1883268c4e236b |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/roberta-base-nli-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/roberta-base-nli-mean-tokens') | 230f1934381a15e85db8d41763fa568c |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/roberta-base-nli-mean-tokens) | de9cde48820ba7db2a7d1f47187c2206 |
apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_m... | 30c48e8e0a02273b38f31fef89f7d6bc |
apache-2.0 | ['translation'] | false | opus-mt-ko-sv * source languages: ko * target languages: sv * OPUS readme: [ko-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ko-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 51ca8a1791d5fe43ed7e5593e6ae07c4 |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_0.001-ep_10-seq_128_bs-16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6393 - Bleu: 0.1929 - Meteor: 0.3605 | f5545545c89ec24b3c5c0e629b6a08f3 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 384588a1581b9a32c6f00dbf529f16dc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 404 | 2.1057 | 0.1016 | 0.2499 | | 2.6026 | 2.0 | 808 | 1.7919 | 0.1333 | 0.2893 | | 1.8228 | 3.0 |... | ed07a20eeffce48ecb87baa1e1f86e1c |
apache-2.0 | ['translation'] | false | opus-mt-lg-en * source languages: lg * target languages: en * OPUS readme: [lg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lg-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 25ef0502668e7630697d2575a7b7d3a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 33461ff61b34ad44d2bf6b8205a3b0f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 281 | 5.8277 | | 5.7427 | 2.0 | 562 | 5.7525 | | 5.7427 | 3.0 | 843 | 5.7016 | | 5.5614 | 4.0 | 1124 | 5.6593 ... | d331f402055283cf1697f2a736829901 |
apache-2.0 | [] | false | distilbert-base-bg-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | e705818dbb7e9abde0de856dada695bf |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-bg-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-bg-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 9bed23bbe2d15e83f39f82d204416156 |
cc-by-4.0 | ['questions and answers generation'] | false | Model Card of `lmqg/mt5-small-dequad-qag` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417... | 401926a847a61b8b2b24d76eaca13a15 |
cc-by-4.0 | ['questions and answers generation'] | false | Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** de - **Training data:** [lmqg/qag_dequad](https://huggingface.co/datasets/lmqg/qag_dequad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi4... | bba2c4d6ba996872fda407e4e38dc85a |
cc-by-4.0 | ['questions and answers generation'] | false | model prediction question_answer_pairs = model.generate_qa("das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-dequad-qag")... | 93a32ec2a0f4fb917516697031d6f7f5 |
cc-by-4.0 | ['questions and answers generation'] | false | Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_dequad.default.json) | | Score | Type | Dataset ... | 14a18a85ae21a6633278c8bc8bf9bad0 |
cc-by-4.0 | ['questions and answers generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_dequad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 256 - epoch: 3 ... | 53140b0023c219f703aae02eba2f4743 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1369 - F1: 0.8620 | 2de8516d4f151b1d33e7756c42f0cce4 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.26 | 1.0 | 525 | 0.1680 | 0.8168 | | 0.126 | 2.0 | 1050 | 0.1389 | 0.8464 | | 0.0801 | 3.0 | 1575 | 0.1369 | 0.8620 | ... | 30cb1714f4585b13996e3a4cbcafe004 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout ac3c10cfe4faf82c0bb30f8b32d9e8692363e0a9 pip install -e . cd egs2/wsj0_2mix/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model lichenda/wsj0_2mix_skim_noncausal ``` <!-- Generated by ./scripts/utils/show_enh_score.sh --> | 2bb0516db50f58084076cccc388b1f70 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Environments - date: `Wed Feb 23 16:42:06 CST 2022` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1` - Git hash: `ac3c10cfe4faf82c0bb30f8b32d9e8692363e0a9` - Commit date: `Fri Feb 11 16:22:52 2022 +0800` | 6027c14688e063eb03752ccb6848399b |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | .. config: conf/tuning/train_enh_skim_tasnet_noncausal.yaml |dataset|STOI|SAR|SDR|SIR| |---|---|---|---|---| |enhanced_cv_min_8k|0.96|19.17|18.70|29.56| |enhanced_tt_min_8k|0.97|18.96|18.45|29.31| | ee9e6d802d5c8df4f9b604b1470c5069 |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | ENH config <details><summary>expand</summary> ``` config: conf/tuning/train_enh_skim_tasnet_noncausal.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/enh_train_enh_skim_tasnet_noncausal_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method... | 263c24a8ee58680cc3cb7d01eada199e |
cc-by-4.0 | ['espnet', 'audio', 'audio-to-audio'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-En... | 4bfff501a69cb303c6fe173e99ebab59 |
apache-2.0 | ['mls', 'google/xtreme_s', 'generated_from_trainer'] | false | xtreme_s_xlsr_mls_upd This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MLS.PL dataset. It achieves the following results on the evaluation set: - Loss: 3.1489 - Wer: 1.0 - Cer: 1.0 | 82b3c01b0e496d63a207ab787027ba2b |
apache-2.0 | ['mls', 'google/xtreme_s', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:---:|:---:| | 3.4678 | 0.59 | 20 | 3.4581 | 1.0 | 1.0 | | 3.1713 | 1.18 | 40 | 3.1816 | 1.0 | 1.0 | | 3.134 | 1.76 | 60 | 3.1538 ... | f140eaa86f3ccdcbb2534809dad9f500 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-legal_data 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: 6.9101 | 5d31149da48a8267155bbc39ac9aebc1 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 | 391b09d82b86dd7aac1e87e2e3ab708a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 26 | 5.3529 | | No log | 2.0 | 52 | 5.4226 | | No log | 3.0 | 78 | 5.2550 | | No log | 4.0 | 104 | 5.1011 ... | d31c3aba2b77d10589171a73003a75e5 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Pashto - Augmented This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.6979 - Wer: 53.6244 - Cer: 22.6847 | e629ab371f3984d1822c64a48703ba7d |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 90fdcda105d9134714d7688d7baf45d7 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:| | 0.9683 | 1.19 | 100 | 0.8812 | 139.3765 | 131.6166 | | 0.6848 | 2.38 | 200 | 0.7543 | 145.9973 | 151.3369 | | 0.5548 ... | dc269197a8690b220cb259d0488b394b |
apache-2.0 | ['generated_from_keras_callback'] | false | hsattar/bert-finetuned-ner 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: 0.0243 - Validation Loss: 0.0573 - Epoch: 2 | dd1d28a12bb52bc5953bc227f3621d6b |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 0.1313 | 0.0635 | 0 | | 0.0415 | 0.0536 | 1 | | 0.0243 | 0.0573 | 2 | | 46703277b3d265c373d000508085c299 |
mit | [] | false | arcimboldo-style on Stable Diffusion This is the `<arcimboldo-style>` 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. ... | e5ae0c4caf1f9dc7318a1989a45f97de |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_accent_us-5_england-5_s924 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... | 505b716b216ac402bc9f76f4f29ec5dc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_fold_9_ternary_v1 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: 1.9406 - F1: 0.7841 | fb4beb54cd3e08f9d5637f6bc8d112ba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 292 | 0.5684 | 0.7635 | | 0.5656 | 2.0 | 584 | 0.5753 | 0.7725 | | 0.5656 | 3.0 | 876 | 0.6159 | 0.7866 | |... | 2190751cc753498241e31b228f1a15e3 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-TINY-DL8 (Deep-Narrow version) T5-Efficient-TINY-DL8 is a variation of [Google's original T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) following the [T5 model architecture](https://huggingface.co/docs/transformers/model_doc/t5). It is a *pretrained-only* checkpoint and ... | ce8b822f61fa1e7615405863e8df2f79 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-tiny-dl8** - is of model type **Tiny** with the following variations: - **dl** is **8** It has **26.09** million parameters and thus requires *ca.* **104.34 MB** of memory in full precision (*fp32*) or **52.17 MB** of memory in half precision (*fp16... | c2917b73d2ad45e4995f77c22f9a83a8 |
apache-2.0 | ['generated_from_trainer'] | false | distilr2-lr5e05-wd0.05-bs64 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2791 - Rmse: 0.5283 - Mse: 0.2791 - Mae: 0.4112 | 60beb1c0d0b1fd48ded36d10fa4af338 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 512 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 0a4144e61a80408882223de453b5628d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2775 | 1.0 | 312 | 0.2756 | 0.5250 | 0.2756 | 0.4280 | | 0.2738 | 2.0 | 624 | 0.2728 | 0.5223 | 0.2728 ... | bfbe804ef108b3b4a333b46bd3d1490f |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | UL2-small-nl16 for Finnish Pretrained T5 model on Finnish language using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was intr... | 156e69140b0f81abcf78a4815511077c |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on self-superv... | b4a85e762a5b6987be7a2963061aac40 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/ul2-small-nl16-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/ul2-small-nl16-finnish") ``` and in TensorFlo... | 9294c3403c70b8aa2a71a6acdce2bce1 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length... | b3f3d160e790cbb5fed008a14eb28536 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8243 - Matthews Correlation: 0.5215 | 460308efb91834d71e2fb45a1d77cb81 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5249 | 1.0 | 535 | 0.5275 | 0.4268 | | 0.3462 | 2.0 | 1070 | 0.4858 | 0.5032 | | 0.2... | 0a90b1cafe5585541e92ae10eed2ce6c |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-low20-1-cased-DA-20 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0643 | 23dbb515a40f6c3682f48e66ae831eeb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 - mixed_precision_training: Native AMP | 820c4c3821159ddfbd024f2535700139 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5949 | 1.0 | 1 | 2.1115 | | 2.0432 | 2.0 | 2 | 1.1308 | | 1.8673 | 3.0 | 3 | 2.9839 | | 2.148 | 4.0 | 4 | 3.1041 ... | ee14ca1c0d745e6e5b31e002d8d8bcc9 |
mit | [] | false | Zero Suit Samus on Stable Diffusion This is the `<zero-suit-samus>` 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. Yo... | 4395c6b4e3ecb657ee7850c1abb56d38 |
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 SetFit/emotion. It achieves the following results on the evaluation set: - Loss: 0.2276 - Accuracy: 0.921 - F1: 0.9209 | 04b7d0989088bd84b8cad391c56bf1a2 |
apache-2.0 | ['generated_from_trainer'] | false | Model description This model follows chapter 2 of https://github.com/nlp-with-transformers/notebooks. A few things that were changed from the original notebook: - the emotion dataset has moved to SetFit/emotion https://github.com/nlp-with-transformers/notebooks/issues/77 - the new dataset doesn't have ClassLabel feat... | 54d3a5f5c2193fe58558432c049ac0f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8732 | 1.0 | 250 | 0.3279 | 0.9055 | 0.9037 | | 0.259 | 2.0 | 500 | 0.2276 | 0.921 | 0.9209 | | 20d3eb4325d6321771c7268faa2e3782 |
apache-2.0 | ['translation'] | false | opus-mt-en-to * source languages: en * target languages: to * OPUS readme: [en-to](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-to/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 724c6144286f5b10910df8e14d81a83c |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 50 - ema_inv_ga... | dd1bf7ee3673cbbaf542ec1bb2138507 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.8_pretrainedFalse This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 5.0795 | dafe47a93cc68e6e1294918f9a280b5b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.6238 | 1.0 | 1111 | 5.3468 | | 4.2134 | 2.0 | 2222 | 5.1410 | | 3.9651 | 3.0 | 3333 | 5.0795 | | aee52dc64e8a8b923d1d9df7a66a4dd3 |
mit | ['generated_from_trainer'] | false | affectionate_lumiere This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk... | 350a7718034fc19a4021a35d89f54eda |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 2d9777ac2aacd5eea56b02273d001b22 |
apache-2.0 | ['translation'] | false | eng-nor * source language name: English * target language name: Norwegian * OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nor/README.md) * model: transformer-align * source language code: en * target language codes: nb, nn * dataset: opus with backtranslations * release date: 2021-04-20... | e45d020222f49b34aa4b9bc5d722daa8 |
apache-2.0 | ['translation'] | false | System Info: * hf_name: eng-nor * source_languages: en * target_languages: nb,nn * opus_readme_url: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-nor/opus+bt-2021-04-20.zip/README.md * original_repo: Tatoeba-Challenge * tags: ['translation'] * languages: ['en', 'nb', 'nn'] * src_constituents: ['eng'] * tgt_consti... | 02e26d5fcbd41084313a5edc6466fe8f |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-subjqa-vanilla-tripadvisor-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: tripadvisor) via [`lmqg`](https://github.com/asahi41... | 10b8d411a1eca92c8a56384454fc6c1e |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (tripadvisor) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question... | 08fec0284eacb25f520320ea9cc854a4 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-subjqa-v... | 4a8c42624bc9348cbe79f1c65c4fea62 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-subjqa-vanilla-tripadvisor-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | Score | Type | Dataset ... | 1564360d09d42cef21ddf80d6afea05c |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_subjqa - dataset_name: tripadvisor - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - bat... | b98838cca2bd0ad8344423846888d934 |
mit | [] | false | r crumb style on Stable Diffusion This is the `<rcrumb>` 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 ... | 93a10d928defaabba97d1c9f0140ca33 |
apache-2.0 | ['generated_from_trainer'] | false | beit-finetuned-pokemon This model is a fine-tuned version of [ydmeira/beit-finetuned-pokemon](https://huggingface.co/ydmeira/beit-finetuned-pokemon) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0219 - Mean Iou: 0.4955 - Mean Accuracy: 0.9910 - Overall Accuracy: 0.9910 - Per ... | b72a8f18de881b19ad8c91d2a4cd186e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-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 - num_epochs: 3 | 9a65fea519d6e11f575d72dce792fafd |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:---------------------... | 9012679bd63ce4fc629e45c135b23177 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | shoebill Dreambooth model trained by Wusul with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffus... | 83a1f49e028ae09efb8902e1c941543e |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Model Details Neural machine translation model for translating from Finnish (fi) to South Slavic languages (zls). 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. ... | 7ca457d08fb85e2be4a6adb723b99083 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>bul<< Ajattelen vain sinua.", ">>slv<< Virtahevot rakastavat vettä." ] model_name = "pytorch-models/opus-mt-tc-big-fi-zls" tokenizer = MarianTokenizer.from_pretrained(mo... | 1ff9738d53c1bb83ab116b263558f9c5 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Povodni konji obožujejo vodo. ``` 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-fi-zls") print(pipe(">>bul<< Ajattelen vain sinua.")) | 8b819b6d22f1c587330db6e37fb55af6 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Training - **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-23.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-z... | bdf8a1d55b887e45e75c9be3c8330aee |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | Evaluation * test set translations: [opusTCv20210807_transformer-big_2022-07-23.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-zls/opusTCv20210807_transformer-big_2022-07-23.test.txt) * test set scores: [opusTCv20210807_transformer-big_2022-07-23.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fi... | 1be2436ecbdc6d9cd38ac19efdab7e07 |
cc-by-4.0 | ['translation', 'opus-mt-tc'] | false | words | |----------|---------|-------|-------|-------|--------| | fin-bul | flores101-devtest | 0.54912 | 26.2 | 1012 | 24700 | | fin-hrv | flores101-devtest | 0.51468 | 21.3 | 1012 | 22423 | | fin-slv | flores101-devtest | 0.51226 | 22.3 | 1012 | 23425 | | fin-srp_Cyrl | flores101-devtest | 0.50774 | 21.8 | 1012 | 234... | 27755e381d98ff8d42137d76254d8244 |
mit | ['generated_from_trainer'] | false | serene_ardinghelli This model was trained from scratch on the tomekkorbak/pii-pile-chunk3-0-50000, the tomekkorbak/pii-pile-chunk3-50000-100000, the tomekkorbak/pii-pile-chunk3-100000-150000, the tomekkorbak/pii-pile-chunk3-150000-200000, the tomekkorbak/pii-pile-chunk3-200000-250000, the tomekkorbak/pii-pile-chunk3-... | 7e2079c5f26a2f4f4bff20cc8f6a59b3 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/pii-pile-chunk3-0-50000', 'tomekkorbak/pii-pile-chunk3-50000-100000', 'tomekkorbak/pii-pile-chunk3-100000-150000', 'tomekkorbak/pii-pile-chunk3-150000-200000', 'tom... | 0ded8d744c9528c3a334fd38a32d2527 |
cc-by-sa-4.0 | ['automatic-speech-recognition'] | false | Wav2vec2-large-xlsr-cantonese This model was based on [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), finetuned using Common Voice/zh-HK/6.1.0. The training code is similar to [user ctl](https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese), except that the number of training epo... | 0b3c29ddb03f5beb729711ee902d0e41 |
mit | ['generated_from_trainer'] | false | mBART_slang_to_standard_4 This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9058 - Bleu: 60.5005 - Gen Len: 47.7251 | 77c16d2bcd8dde7f4f8dfd935c404558 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 106 | 2.6704 | 60.4144 | 51.1659 | | No log | 2.0 | 212 | 2.0665 | 60.2528 | 47.1848 | | No log |... | 4a1ac43efad3eac3aeb18dcec431655b |
cc-by-4.0 | [] | false | This model is a RoBERTa model trained on a programming language code - WolfSSL + examples of Singletons diffused with the Linux Kernel code. The model is pre-trained to understand the concep of a singleton in the code The programming language is C/C++, but the actual inference can also use other languages. Using th... | 97b4b4350f446c78c15a5128027d5cac |
mit | ['automatic-speech-recognition', 'generated_from_trainer'] | false | Model description We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 14h of labelled Luxembourgish speech from the same domain. | 07ed884c3e6a3a614544ca25bc28d8cb |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_500v0_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v0_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2679 - Precision: 0.6663 - Recall: 0.6838 - F1: 0.6750 - Accura... | 3946a171561110297b5aed4886f3f9f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 173 | 0.2827 | 0.5972 | 0.5556 | 0.5757 | 0.9079 | | No log | 2.0 |... | 2c8f333a9373fcbc0aeba2b510da4f1d |
creativeml-openrail-m | [] | false | Usage To use this model you have to download the .ckpt file as well as drop it into the "\stable-diffusion-webui\models\Stable-diffusion" folder To use it in a prompt: ```"Lamia monstergirl"``` for highest strength or just "Lamia" To increase the strength put "Lamia monstergirl" in () brackets To decrease the streng... | af0a6633e6e47f89204271e0e38e95c7 |
creativeml-openrail-m | [] | false | Example Pictures from Lamia 8k <table> <tr> <td><img src=https://i.imgur.com/EEQCv5X.png width=150% height=150%/></td> <td><img src=https://i.imgur.com/FhsRzeI.png width=100% height=100%/></td> <td><img src=https://i.imgur.com/TkTUkwZ.png width=150% height=150%/></td> </tr> </table> | 7aabcf2b9cfcfbc5d5974b503d740111 |
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: 0.0611 - Precision: 0.9305 - Recall: 0.9505 - F1: 0.9404 - Accuracy: 0.9861 | cb3a5a3e4d4020c8e10719d0729d3be7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0869 | 1.0 | 1756 | 0.0680 | 0.9174 | 0.9342 | 0.9257 | 0.9827 | | 0.0334 | 2.0 |... | ab3f8521f265dc83c36bca7558f06621 |
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