license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | ['generated_from_trainer'] | false | Training and evaluation data SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supporte... | 571d267ffd770292115f18ad7709d30c |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.4098 | 1.0 | 8202 | 1.3860 | | 1.1716 | 2.0 | 16404 | 1.8555 | | 1.2909 | 3.0 | 24606 | 1.8025 | | Metric | | ecb5d27ff68090a1bb8b14a5539d56f2 |
mit | ['generated_from_trainer'] | false | Pipeline ```py from transformers import pipeline qa_pipeline = pipeline( "question-answering", model="esakrissa/IndoBERT-SQuAD", tokenizer="esakrissa/IndoBERT-SQuAD" ) qa_pipeline({ 'context': """Sudah sejak tahun 1920-an, Ubud terkenal di antara wisatawan barat. Kala itu pelukis Jerman; Walter Spies... | 25a43a95faead8690809b73e01fa8a5e |
mit | ['generated_from_trainer'] | false | Reference <a id="1">[1]</a>Fajri Koto and Afshin Rahimi and Jey Han Lau and Timothy Baldwin. 2020. IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. Proceedings of the 28th COLING. <a id="2">[2]</a>rifkybujana/IndoBERT-QA | c75af0be0ffe6f99e8e3e3be93cda790 |
openrail | [] | false |  Image Mixer is a model that lets you combine the concepts, styles, and compositions from multiple images (and text prompts too) and generate new images. It was trained by [Justin Pinkney](https://www.... | 454f9493a3f2cce217b407b98b9de1f4 |
openrail | [] | false | Training details This model is a fine tuned version of [Stable Diffusion Image Variations](https://huggingface.co/lambdalabs/sd-image-variations-diffusers) it has been trained to accept multiple CLIP embedding concatenated along the sequence dimension (as opposed to 1 in the original model). During training up to 5... | fa7cdb7b48bbe9f6c46443da8fc2089b |
openrail | [] | false | Usage The model is available on [huggingface spaces](https://huggingface.co/spaces/lambdalabs/image-mixer-demo) or to run locally do the following: ```bash git clone https://github.com/justinpinkney/stable-diffusion.git cd stable-diffusion git checkout 1c8a598f312e54f614d1b9675db0e66382f7e23c python -m venv .venv --... | de92c57c766fa7612e435bc8f1e4cc2b |
creativeml-openrail-m | ['text-to-image'] | false | seraphm Dreambooth model trained by mint designer at alvdansen with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook trained on 1500 steps and 12 images. Aesthetic diverse dataset which should allow the character to be us... | f6447271e75e2540ab9a2ab5e5b6a030 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-wikiandmark 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: 0.0329 - Accuracy: 0.9962 | 86cd65dee9f373a7cb0670722bf19277 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0058 | 1.0 | 1490 | 0.0261 | 0.9954 | | 0.0058 | 2.0 | 2980 | 0.0335 | 0.9945 | | 0.0024 | 3.0 | 4470 | 0.0309 | 0.... | 49daa3071cd52f8b9bcd1d17175f0985 |
apache-2.0 | ['automatic-speech-recognition', 'ar'] | false | exp_w2v2t_ar_vp-es_s801 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ar)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 641100f77055086403f8a2cf02cc545d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_stsb_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.8747 - Pearson: 0.1794 - Spearmanr: 0.1839 - Combined Score: 0.18... | 590aef7b2ab55e4871c8472b5b058c02 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:---------:|:--------------:| | 1.2844 | 1.0 | 1259 | 2.8897 | 0.1809 | 0.1879 | 0.1844 | | 0.4862 | 2.0 | 25... | 42d9398527fa63735b3d8b3aa16aa854 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1` ♻️ Imported from https://zenodo.org/record/5845307/files/asr_conformer_ar_valid.acc.ave.zip?download=1 This model was trained by vectominist using seame/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | da4096024e63985efc3721e448835cd3 |
apache-2.0 | ['object-detection', 'computer-vision', 'vision', 'mmdet', 'sahi'] | false | Model Description [YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430) [SAHI: Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection](https://arxiv.org/abs/2202.06934) Improved anchor-free YOLO architecture for object detection task. | b433ce797cae1a5532a8c146cc667911 |
apache-2.0 | ['object-detection', 'computer-vision', 'vision', 'mmdet', 'sahi'] | false | How to use - Install [sahi](https://github.com/obss/sahi) and `mmdet`: ```bash pip install -U sahi pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html pip install mmdet==2.26.0 ``` - Load model and perform prediction: ```python from sahi import AutoDetectionModel f... | 42c8eb8df32c80d567595827883a34f8 |
apache-2.0 | ['object-detection', 'computer-vision', 'vision', 'mmdet', 'sahi'] | false | create model detection_model = AutoDetectionModel.from_pretrained( model_type='mmdet', model_path=MMDET_YOLOX_TINY_MODEL_PATH, config_path=MMDET_YOLOX_TINY_CONFIG_PATH, confidence_threshold=0.5, device="cuda:0", | 5d03a83645bcf99ea1b25986c63fe399 |
apache-2.0 | ['object-detection', 'computer-vision', 'vision', 'mmdet', 'sahi'] | false | BibTeX Entry and Citation Info ``` @article{akyon2022sahi, title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, journal={2022 IEEE International Conference on Image Processing (ICIP)}, doi={10.1109/ICIP465... | 4731a351701a2508dd777a8ae093745e |
apache-2.0 | ['automatic-speech-recognition', 'nl'] | false | exp_w2v2t_nl_vp-sv_s510 Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (nl)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that you... | 5a4a93e30ac3bcfed68d51b1384d409b |
mit | [] | false | Heather on Stable Diffusion This is the `Heather*` 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 train ... | 2a2fd98a93e7b1c487b4b4a58827f465 |
mit | [] | false | 1** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:  created by [xpero](https://civitai.com/user/xpero). | 079ccf1bc7d6cf01183a21bf72e48671 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Model Description (from CivitAI) This model is a custom blend of various models, presenting many options for generating images, including NSFW. Based on Stable Diffusion 1.5. Primarily focused on the creation of digital art characters. Can easily generate great images in different styles - characters, illustration, an... | 1e88efa281711ec60bbb1b7e827bea1e |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Example 1  ``` Positive: a woman in a white top and pink shorts is standing next to a bike with a yellow background and, by Quentin Tarantino, 1girl, bracelet, brown_hair, jewelry, letterboxed, lips, long_hair, makeup, medium_b... | d4f890cea6b339fe129565d586b7598f |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Example 2  ``` Positive: a woman with blue disheveled hair and piercings, with a dark and a black background, Charlie Bowater, stanley artgerm lau, a character portrait, sots art, sharp focus, smooth, aesthetic, extremely detai... | bec0d84856fb002887f919e820b98c81 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'diffusers'] | false | Example 3  ``` Positive: 1/2 portrait of beautiful rock girl, punk, slim body, beautiful detailed glow, highres, high detail, smooth, aesthetic, extremely detailed, octane render, detailed facial features, sharp focus, rtx, amb... | 73564d4bb808e9e09c3efc38fef6593d |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-demo-F01-2 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: 1.6361 - Wer: 0.9025 | e43cecd59fc32a3103e9b887d29df832 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 23.7408 | 0.81 | 500 | 3.3782 | 1.0 | | 3.348 | 1.62 | 1000 | 2.9501 | 1.0 | | 2.8539 | 2.44 | 1500 | 2.6975 | 1.0 ... | a62d0d190ee7074c3c2b3277747c1f3b |
mit | [] | false | alisa on Stable Diffusion This is the `<alisa-selezneva>` 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... | 798f0377c3cb218191ae68a135cbbb2d |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | ja_core_news_lg Japanese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `ja_core_news_lg` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2vec`, `morphologizer`, `parse... | 80fc722d8b659d4a95c3ef696882aa8c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.37 | | `TOKEN_P` | 97.65 | | `TOKEN_R` | 97.90 | | `TOKEN_F` | 97.77 | | `POS_ACC` | 97.50 | | `MORPH_ACC` | 0.00 | | `MORPH_MICRO_P` | 34.01 | | `MORPH_MICRO_R` | 98.04 | | `MORPH_MICRO_F` | 50.51 | | `SENTS_P` | 95.56 | | `SENTS_R` | 97.63 | | `SENTS_F` | 9... | 4c588e39ac5ad882aa00fe6d687aa2c7 |
apache-2.0 | ['generated_from_trainer'] | false | sentence-transformers-msmarco-distilbert-base-tas-b-twitter_sentiment This model is a fine-tuned version of [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) on an unknown dataset. It achieves the following results on the evaluation set: ... | cc451862858e8fa5aa4ea05f75e32aa0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - 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: 20 | 97278e9ee2ab65ec8e8c3ebda2b72eb6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.8892 | 1.0 | 1387 | 0.8472 | 0.6180 | | 0.7965 | 2.0 | 2774 | 0.7797 | 0.6609 | | 0.7459 | 3.0 | 4161 | 0.7326 ... | 13c69159501a333f94c20061b2905a1a |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 48 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | 6016a2a0f687c07e2fc8131e3dcda39a |
apache-2.0 | ['generated_from_trainer'] | false | codet5-base-buggy-code-repair This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8033 - Accuracy: 0.2516 | d40197a429b2f868d753c58c726807a2 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 | 20d7ae1129daf904853025ed39e34427 |
mit | ['generated_from_trainer'] | false | distilcamembert-base-cae This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6618 - Precision: 0.8838 - Recall: 0.8835 - F1: 0.8833 | b37fd6b8afa857e3e4ecf282272d0f4c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 | cc66cc20db2b9d78e9d870795a08fcd8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 1.4308 | 1.0 | 292 | 0.5898 | 0.8153 | 0.8286 | 0.8134 | | 0.4963 | 2.0 | 584 | 0.4794 | 0.8339 ... | 680bb10cfc7ac21605779e834dc6d940 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-8-4 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: 0.6921 - Accuracy: 0.5107 | f294b7c37ab84827a4e44361c218ee58 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7163 | 1.0 | 3 | 0.7100 | 0.25 | | 0.6785 | 2.0 | 6 | 0.7209 | 0.25 | | 0.6455 | 3.0 | 9 | 0.7321 | 0.... | 5e22a870ac22a45d8605c58d93ed9011 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-wikitext-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.9013 | 3d7eaa6d31518be40ec851de5744c9f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 7.443 | 0.4 | 500 | 7.1092 | | 6.9585 | 0.8 | 1000 | 6.9474 | | 6.8416 | 1.2 | 1500 | nan | | 6.7094 | 1.6 | 2000 | 6.6852 ... | f48792994a06c39df9f92179504cadc8 |
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. Additionally, we rescore the output transcription with a 5-gram... | 4ada598d1f7efe95272345bab751fddb |
mit | ['generated_from_trainer'] | false | roberta-base-finetuned-cola This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6074 - Matthews Correlation: 0.6221 | c020a1a88149d1595f6d2ebd9b564e64 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4536 | 1.0 | 534 | 0.4104 | 0.5738 | | 0.4876 | 2.0 | 1068 | 0.5156 | 0.5729 | | 0.1... | cd56835fba872365f2253edbe4a8f8c7 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4796 - Wer: 0.3434 | e580b76ce7cefa37cf3db6d23a73b638 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4323 | 4.0 | 500 | 1.3259 | 0.9859 | | 0.5966 | 8.0 | 1000 | 0.4682 | 0.4442 | | 0.2187 | 12.0 | 1500 | 0.4490 | 0.3875 | |... | 2a54790ebe7a0a7ca10f511510eba869 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | deployment-with-nvidia-riva) | This model transcribes speech in lowercase English alphabet including spaces and apostrophes, and is trained on several thousand hours of English speech data. It is a non-autoregressive "large" variant of Streaming Citrinet, with around 140 million parameters. See the [model architectur... | 2d0100d6789ae7504cf193f2b7ff7d59 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Usage The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've in... | 984fad5edec63e33aac03e238059b9f9 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Transcribing many audio files ```shell python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_1024_gamma_0_25" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" ``` | a78a25a457ed88b2361ffd7a325f136e |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Model Architecture Streaming Citrinet-1024 model is a non-autoregressive, streaming variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on this model here: [Citrinet Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/e... | 3bc886b68b0e9b529c69aaf64914dcbb |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Training The NeMo toolkit [3] was used for training the model for over several hundred epochs. This model was trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/ci... | 7ebd417ff31ada4b78da1a1e7375dee7 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Datasets All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech: - Librispeech 960 hours of English speech - Fisher Corpus - Switchboard-1 Dataset - WSJ-0 and WSJ-1 - National Speech Corpus (Part 1, Part 6) Note: older versions of th... | 3cf893ded2abb0ed84d63244cd3ee36e |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Performance The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. | Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 |Train Da... | 669f6dcd01477764b8351e8578693c80 |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | Limitations Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech that includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. | ee5e14f94da30e0bbab17e1fad4d1a8d |
cc-by-4.0 | ['automatic-speech-recognition', 'speech', 'audio', 'CTC', 'Citrinet', 'Transformer', 'pytorch', 'NeMo', 'hf-asr-leaderboard', 'Riva'] | false | References [1] [Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition](https://arxiv.org/abs/2104.01721) [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) | bba5370ae9f666ac63725d2f82163f5e |
apache-2.0 | ['summarization', 'translation', 'openvino'] | false | Usage example You can use this model with Transformers *pipeline*. ```python from transformers import AutoTokenizer, pipeline from optimum.intel.openvino import OVModelForSeq2SeqLM model_id = "echarlaix/t5-small-openvino" model = OVModelForSeq2SeqLM.from_pretrained(model_id, use_cache=False) tokenizer = AutoTokeniz... | bc8d423072681785bf464c58fc2ec74c |
apache-2.0 | ['summarization', 'translation', 'openvino'] | false | Create a pipeline translation_pipe = pipeline("translation_en_to_fr", model=model, tokenizer=tokenizer) text = "He never went out without a book under his arm, and he often came back with two." result = translation_pipe(text) ``` | 1acc8d09dcf063473ae4714c28a807e6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-misogyny-sexism-outdomain-trans 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: 1.8950 - Accuracy: 0.2562 - F1: 0.1909 - Precision: 0.1086 - Recall: 0.7891 - Mae: 0.... | 07195b5aae6b1a5fed588e0679d0cb43 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae | Tn | Fp | Fn | Tp | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|:----:|:----:|:---:|:---:| | 0.3783 | 1.0 | 2229 | 1.3615 ... | a14ce4ed4548432b0cff1cc61098bbae |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-finetuned-ner 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: 0.6657 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8281 | d262f86370be6f7910a4e2c3428438ae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10 | df5a5eab83d2a4de664cb14e58bdf255 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 3 | 0.7560 | 0.0 | 0.0 | 0.0 | 0.8260 | | No log | 2.0 | 6 | 0... | 2c8b35db09028f5d5dc4bee3250f6110 |
apache-2.0 | ['generated_from_trainer'] | false | Graphcore/lxmert-gqa-uncased BERT (Bidirectional Encoder Representations from Transformers) is a transformers model which is designed to pretrain bidirectional representations from unlabeled texts. It enables easy and fast fine-tuning for different downstream task such as Sequence Classification, Named Entity Recogni... | 85b461a8fed8205ac80a2376bf61f293 |
apache-2.0 | ['generated_from_trainer'] | false | Model description LXMERT is a transformer model for learning vision-and-language cross-modality representations. It has a Transformer model that has three encoders: object relationship encoder, a language encoder, and a cross-modality encoder. It is pretrained via a combination of masked language modelling, visual-la... | 29708550b11f403b2a1066e6fa551ebc |
apache-2.0 | ['generated_from_trainer'] | false | Intended uses & limitations This model is a fine-tuned version of [unc-nlp/lxmert-base-uncased](https://huggingface.co/unc-nlp/lxmert-base-uncased) on the [Graphcore/gqa-lxmert](https://huggingface.co/datasets/Graphcore/gqa-lxmert) dataset. It achieves the following results on the evaluation set: - Loss: 1.9326 - Ac... | adfb9ccc7d12b542f40b594edd1badc2 |
apache-2.0 | ['generated_from_trainer'] | false | Training procedure Trained on 16 Graphcore Mk2 IPUs using [optimum-graphcore](https://github.com/huggingface/optimum-graphcore). Command line: ``` python examples/question-answering/run_vqa.py \ --model_name_or_path unc-nlp/lxmert-base-uncased \ --ipu_config_name Graphcore/lxmert-base-ipu \ --dataset_name Gra... | 05de48ce3203e7a9747ba9caea7573f4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: IPU - total_train_batch_size: 64 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_typ... | 9816dc800bd7a3b0635c2d4fd8763a36 |
apache-2.0 | ['generated_from_trainer'] | false | Training results ``` ***** train metrics ***** "epoch": 4.0, "train_loss": 0.6123406731570221, "train_runtime": 29986.2288, "train_samples": 943000, "train_samples_per_second": 125.791, "train_steps_per_second": 1.965 ***** eval metrics ***** "eval_accuracy": 0.5933514030612245, "eval_loss": 1.9326171... | 7976295fa3d969e2c40bb79f46dc228f |
apache-2.0 | ['generated_from_trainer'] | false | destilbert_fever_nli This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5463 - F1: 0.6747 | cb9d7c6e53ecaee6efe2ef28d047b9e2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 235 | 1.2711 | 0.6671 | | No log | 2.0 | 470 | 1.8000 | 0.6538 | | 0.1341 | 3.0 | 705 | 1.6965 | 0.6770 | |... | 74fc3421966b593e02dc320bbed54e42 |
mit | ['generated_from_keras_callback'] | false | orhanxakarsu/turkish-poem-generation-1 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 7.0761 - Validation Loss: 7.0393 - Epoch: 3 | 5adf50fd87b7d1dbc07dfaa2784b93d9 |
mit | ['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': 2.380655430044305e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2.3... | 5a54ed536d1533c147bc36b567b71c76 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 7.5133 | 7.0394 | 0 | | 7.0763 | 7.0388 | 1 | | 7.0762 | 7.0389 | 2 | | 7.0761 | 7.0393 | 3 | | 3c7187b35b8b3452ff0d84639c18a8e8 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-0.4-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset. It achieves the following results on the evaluation set: - Loss: 2.8191 - Bleu: 6.7381 - Gen Len: 45.6473 | e1a4de556ade8fb7e18b5a3d1dc59240 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2480 | 6964b0e5cbdf6b6526ed050ac49b24d6 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0972 | 1.0 | 291 | 1.7066 | | 1.6391 | 2.0 | 582 | 1.4318 | | 1.4844 | 3.0 | 873 | 1.3734 | | 1.3997 | 4.0 | 1164 | 1.3806 ... | 42c4dace4cd57a6f8f2d4817af85541e |
other | ['vision', 'image-segmentation'] | false | Mask2Former Mask2Former model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch... | 0b4658d2094c516b6a3b0a2d137281f7 |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on ADE20k semantic segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-ade-semantic") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-ade-semantic") url = "http://images.cocodataset.org/val2017/0000000397... | a7a8c868373ca2ac4206c30a0f18ce06 |
mit | [] | false | diwank/dyda-deberta-pair
Deberta-based Daily Dialog style dialog-act annotations classification model. It takes two sentences as inputs (one previous and one current of a dialog). The previous sentence can be an empty string if this is the first utterance of a speaker in a dialog. Outputs one of four labels (exactl... | c454e41b651800c5582b2fa1ce66acbb |
mit | [] | false | Usage
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/dyda-deberta-pair")
convert_to_label = lambda n: ["__dummy__ (0), inform (1), question (2), directive (3), commissive (4)".split(', ')[i] for i in n]
... | 88959d7a5a81b68163ddf69bb9480491 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, ... | c80fee03214751a65ee7136029cd3290 |
apache-2.0 | ['generated_from_trainer'] | false | small_finetune_CM01 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9764 - Wer: 1.0 | 91a926455e2c3ddf78b36494d7256cf1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 20 - 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: 700 - num_epochs: 2000 - mixed_precision_... | 214cba7505713c83b61563fb6cedce18 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:---:| | 40.8946 | 100.0 | 700 | 3.6118 | 1.0 | | 3.1203 | 200.0 | 1400 | 3.4805 | 1.0 | | 2.2986 | 300.0 | 2100 | 2.6437 | 1.0 | | 1.98... | 5abef95c1bf4693650a735fae9b6391b |
apache-2.0 | ['generated_from_trainer'] | false | BartConditionalGeneration-bart-large-finetuned-insult2 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan | 6cfa113d32575091e1e69006e08c50a7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 5.5977 | 1.0 | 600 | nan | | 4.7539 | 2.0 | 1200 | nan | | 4.2158 | 3.0 | 1800 | nan | | bf6e0c7151fa4ad0dd9123e6d1dd91ee |
mit | ['generated_from_trainer'] | false | pedantic_sinoussi This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tome... | 0d113d1c5ee9535d76e85f77649945df |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 512 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_s... | eaf489e536910be1ecbabbd186e59e26 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'datasets': ['tomekkorbak/detoxify-pile-chunk3-0-50000', 'tomekkorbak/detoxify-pile-chunk3-50000-100000', 'tomekkorbak/detoxify-pile-chunk3-100000-150000', 'tomekkorbak/detoxify-pile-chunk3-150000-200000', ... | baebe40ea11e43441f946401b1f338db |
mit | ['generated_from_trainer'] | false | ecstatic_hoover This model was trained from scratch on the tomekkorbak/detoxify-pile-chunk3-0-50000, the tomekkorbak/detoxify-pile-chunk3-50000-100000, the tomekkorbak/detoxify-pile-chunk3-100000-150000, the tomekkorbak/detoxify-pile-chunk3-150000-200000, the tomekkorbak/detoxify-pile-chunk3-200000-250000, the tomekk... | 5464e4c3fa6f4006cfd3d9f9a3371495 |
mit | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.00056}, ... | ffbde397b33c5311e6431183abaab2e4 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-de-finetuned-en-to-de This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.6798 - Bleu: 26.4396 - Gen Len: 24.8156 | dd3f056b8b8af3d34d211fb06075be37 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 1 - mixed_precision_training: Native AMP | 9816c3ba85161c1596744e016b94c4ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 2.0864 | 1.0 | 568611 | 1.6798 | 26.4396 | 24.8156 | | 4a5d58e0dcd5b7208518d478629172d7 |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-en-ar-evaluated-en-to-ar-2000instancesopus-leaningRate2e-05-batchSize8-11epoch-3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset. It achieves the following results on the evaluation set: - Loss: 0.1959 - Bleu: 26.2629... | e1768a3c628456879065c1bc1312f747 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 1.0519 | 0.5 | 100 | 0.1985 | 27.3525 | 0.1815 | 11.0725 | | 0.1947 | 1.0 | 200 | 0.1902 | 26.9728 | ... | 5f09938a60e61dfa69105246077566bd |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base_toy_train_data_random_low_pass 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: 1.3227 - Wer: 0.7288 | 7ad7f0c1be67b403eead40dba19347e7 |
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