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 | xlm-roberta-base-finetune-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: 0.1405 - F1: 0.8611 | 9192ae780c3a2b7a704cc0e94e833c38 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-pointer-mtop This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mtop dataset. It achieves the following results on the evaluation set: - Loss: 0.1202 - Exact Match: 0.7445 | 9905bdcfbcfdb59ccf94174996eb4c4d |
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 - 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 - trai... | f6827ae6cf8a02ea662f0910ca1190ca |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Exact Match | |:-------------:|:-----:|:----:|:---------------:|:-----------:| | 2.1451 | 6.65 | 200 | 0.5966 | 0.0134 | | 0.4695 | 13.33 | 400 | 0.2264 | 0.2998 | | 0.2229 | 19.98 | 600 | 0.1446 ... | e07a924ef6d353a978c584ff6c5172bc |
creativeml-openrail-m | [] | false | 💥🎨 The Simpsons dreambooth model. This is a fine-tuned Stable Diffusion model based on The Simpsons. Use **asim style** in your prompts. The model has some trouble with double pupils and no pupils. Using "cross-eyed" in the negative prompt appears to help? | 2bdc119798dd3da0ac7e68c81b490e0b |
creativeml-openrail-m | [] | false | Sample images: Samples are made with [dynamic prompts](https://github.com/adieyal/sd-dynamic-prompts), Euler 80 steps @ CFG 12. Negative prompts: watermark, text, signature, cross-eyed   + [d8ahazard dreambooth extension](https://github.com/d8ahazard/sd_dreambooth_extension) + [nitrosocke guide](https://github.com/nitrosocke/dreambooth-training-guide). 100 hand-cut training images. About 70% people, 20% ... | aa6cd60ff1c0f6a6506d2d4cc121b690 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | article2KW_test1.3b_barthez-orangesum-title_finetuned_for_summerization This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2702 - Rouge1: 0.2711 - ... | d939ce8556b713510306e12c820b81bc |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.7922 | 1.0 | 1036 | 1.4273 | 0.2704 | 0.0752 | 0.2711 | 0.2721 | | 1.3346 | 2.0 | 2072 ... | 996b84185c55a21d964e39f02af61ddc |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the plod-filtered dataset. It achieves the following results on the evaluation set: - Loss: 0.0319 - Precision: 0.9890 - Recall: 0.9881 - F1: 0.9886 - Accuracy: 0.9884 | ca7dc9296503bf5e17b539ad69c95b62 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-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: 2 | f1ae575daf31cd227725d30e24cb1a6a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0649 | 1.0 | 3018 | 0.0471 | 0.9838 | 0.9814 | 0.9826 | 0.9818 | | 0.0442 | 2.0 |... | 4a58367b196a38ffecf0b20bd8b3d3d9 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 256 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsil... | 8e2e99ec6f05c2fb37c63192c2cf879b |
mit | [] | false | Rail Scene on Stable Diffusion This is the `<rail-pov>` 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 t... | 407b9df85bab754dc3bef9150210182d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 | 9c3ac0c4d7cba0f1ed746e407d9ee2de |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'ru', 'speech', 'xlsr-fine-tuning-week'] | false | Fine-tuned XLSR-53 large model for speech recognition in Russian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Russian using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyub... | 176d58e311d419dc3065d16faad2c9b1 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'ru', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows... Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-russian") audio_paths = ["/... | 29fbbae2961c1fc2817357d8dee12899 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'ru', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs =... | 763a24141e3e30620206c35931113ef0 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'ru', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation 1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-russian --dataset mozilla-foundation/common_voice_6_0 --config ru --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python... | 348ff3d7c24874e64a8a23bcd00a5592 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_6_0', 'robust-speech-event', 'ru', 'speech', 'xlsr-fine-tuning-week'] | false | Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr53-large-russian, title={Fine-tuned {XLSR}-53 large model for speech recognition in {R}ussian}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-russian}}, year={2... | 68c21adeadb0aa0ffdac59f78cfd8a6e |
apache-2.0 | ['generated_from_trainer'] | false | distilroberta-base-mrpc-glu-cristian-agudelo This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.9131 - Accuracy: 0.8211 - F1: 0.8713 | 244768c218b361db8de8dc03220e5846 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.285 | 1.09 | 500 | 0.8959 | 0.8407 | 0.8845 | | 0.2653 | 2.18 | 1000 | 0.9131 | 0.8211 | 0.8713 | | fd5c60abbda4468ae67c96785516de24 |
mit | ['Yes No question-generation'] | false | [SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/) [Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg) ```python from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config m... | f3361e5efe9b44b5a5b66407545f42e5 |
mit | ['generated_from_trainer'] | false | deberta-base-combined-squad1-aqa-newsqa-50-and-newsqa-50 This model is a fine-tuned version of [stevemobs/deberta-base-combined-squad1-aqa-newsqa-50](https://huggingface.co/stevemobs/deberta-base-combined-squad1-aqa-newsqa-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.488... | 5422626828ea7b7dabd099d546eb18c5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.6957 | 1.0 | 8681 | 0.5072 | | 0.4264 | 2.0 | 17362 | 0.4881 | | b30f385ffc22e81ed80c069fbf6dde28 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-DL2 (Deep-Narrow version) T5-Efficient-LARGE-DL2 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 an... | 8a7d9afbe9f1721d390e7e343d97bb81 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-dl2** - is of model type **Large** with the following variations: - **dl** is **2** It has **368.53** million parameters and thus requires *ca.* **1474.11 MB** of memory in full precision (*fp32*) or **737.05 MB** of memory in half precision (... | 24a2277e7bdeed3cae9b34fab8879830 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-work-8-16-5 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 | 6408a91d70c0d1038fee3325149a7bf0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 1402b650739bbb240cd2c9e60275a907 |
apache-2.0 | ['generated_from_trainer'] | false | colab-demo 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: 3.9910 - Wer: 0.9714 | fec0c0de9f1fda91e1eb97e3dd2cb89e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1212 | 2.14 | 500 | 3.6706 | 1.0757 | | 0.2303 | 4.27 | 1000 | 2.6849 | 1.0578 | | 0.3003 | 6.41 | 1500 | 3.2261 | 1.0605 | |... | 919751c3fb8f99986265d443564ca366 |
mit | ['generated_from_keras_callback'] | false | nst-sat/GlossBERT-finetunedTEST This model is a fine-tuned version of [kanishka/GlossBERT](https://huggingface.co/kanishka/GlossBERT) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.1065 - Epoch: 0 | b5a62ec290b50674400acc4f14dd7d05 |
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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps... | ed68dc0243abbf10ae858183e9e33fd8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 | a6ea01b868c7562ef2d45051dfa045e7 |
cc-by-sa-4.0 | ['scientific names', 'text generation'] | false | t5-base-sci-names Biodiversity literature is dedicated to the identification, documentation, and categorization of plants, fungi, animals, and other living organisms. Correctly extracting the name of an organism within these documents involves finding the entire scientific name–including the genus, specific epithet, a... | e1ec961d20fb685a7c03662ddd1a6e1f |
apache-2.0 | [] | false | DistilBERT optimized for Apple Neural Engine This is the [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) model, optimized for the Apple Neural Engine (ANE) as described in the article [Deploying Transformers on the Apple Neural Engine](https://... | 9182a5177c7244845f6e12a6c809d209 |
apache-2.0 | [] | false | How to use Usage example: ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model_checkpoint = "apple/ane-distilbert-base-uncased-finetuned-sst-2-english" tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) model = AutoModelForSequenceClassification.from_pretr... | 37c1c6160656a0ec77b47774cdc74193 |
apache-2.0 | [] | false | Using the model with Core ML PyTorch does not utilize the ANE, and running this version of the model with PyTorch on the CPU or GPU may actually be slower than the original. To take advantage of the hardware acceleration of the ANE, use the Core ML version of the model, **DistilBERT_fp16.mlpackage**. Core ML usage e... | bab64ebcf811e7759863c552720ee09f |
apache-2.0 | ['generated_from_trainer'] | false | jobBERTA_german_QA This model is a fine-tuned version of [Joblift/distilbert-base-german-cased-finetuned-jl](https://huggingface.co/Joblift/distilbert-base-german-cased-finetuned-jl) on the germanquad dataset. | a68cfc59de0f331086740c1c06c1efc1 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2-inpainting` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-bas... | 4a713fba85112b67ff4a05680aefe723 |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 inpainting in a simple and efficient manner. ```bash pip install diffusers transformers accelerate scipy safetensors ``` ```python from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusi... | 0ad5d4c98c45f8f4d919ee35c2b9d84d |
openrail++ | ['stable-diffusion', 'text-to-image'] | false | The mask structure is white for inpainting and black for keeping as is image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[0] image.save("./yellow_cat_on_park_bench.png") ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearc... | e551803d4899a221856ee6a2d9948560 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | Description A Japanese Question Answering model fine-tuned on [JaQuAD](https://huggingface.co/datasets/SkelterLabsInc/JaQuAD). Please refer [RoBERTa base Japanese](https://huggingface.co/rinna/japanese-roberta-base) for details about the pre-training model. The codes for the fine-tuning are available [on this notebook... | 319a45f662dd36192187d40cba7108d4 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | Usage ```python from transformers import AutoModelForQuestionAnswering, AutoTokenizer question = 'アレクサンダー・グラハム・ベルは、どこで生まれたの?' context = 'アレクサンダー・グラハム・ベルは、スコットランド生まれの科学者、発明家、工学者である。世界初の>実用的電話の発明で知られている。' model = AutoModelForQuestionAnswering.from_pretrained( 'ybelkada/japanese-roberta-question-answering') tokenizer... | 9e82ba242ff368c3849e69f44a5b80d2 |
cc-by-sa-3.0 | ['question-answering', 'extractive-qa'] | false | 1 is added to `answer_end` because the index pointed by score is inclusive. answer_end = torch.argmax(answer_end_scores) + 1 answer = tokenizer.convert_tokens_to_string( tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end])) | fe52172b58c96f6c0d27641abad1d49b |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-en 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: 0.3792 - F1: 0.6918 | aaabdc2e6cc957f802cab3e44ebfed44 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0639 | 1.0 | 74 | 0.5075 | 0.5539 | | 0.491 | 2.0 | 148 | 0.4118 | 0.6510 | | 0.355 | 3.0 | 222 | 0.3792 | 0.6918 | ... | 29c0b5f43514e77b4835891a17e6d643 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: 500 - num_epochs: 2 | dba0aa11be4576ee75fcccb8664b341c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.2928 - Rouge1: 21.4274 - Rouge2: 8.18 - Rougel: 21.3234 - Rougelsum: 21.3185 - Gen Len: 4.9993 | edbe969870f0a7e0606e41f3427f7d8a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.5264 | 1.0 | 12753 | 2.2928 | 21.4274 | 8.18 | 21.3234 | 21.3185 | 4.... | ef849ab1f7f4e5b6938ef013ca37e3c0 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | distilroberta-base-mrpc-glue-juanda-bula This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - Accuracy: 0.8333 - F1: 0.8707 | a91651ebff93f33dd702b1ef13a54bd4 |
apache-2.0 | ['text-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5239 | 1.09 | 500 | 0.6723 | 0.7990 | 0.8610 | | 0.3692 | 2.18 | 1000 | 0.5684 | 0.8333 | 0.8707 | | 2ca84d9530d1b761dbc102b410e96be9 |
cc-by-4.0 | ['generated_from_trainer'] | false | nb-bert-base-user-needs This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the [BBC i... | dd71d16fa0d1d6635e67976d206182e1 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 64 - 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: 25 - mixed_precision_tr... | c5e0b2103e64d269559a3afefc154f19 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 98 | 1.1222 | 0.6263 | 0.5185 | 0.5076 | 0.6263 | | No log | 2.0 |... | 2657eb16fe5d7c1c364e6cbcb22c2399 |
mit | ['generated_from_trainer'] | false | deberta-v3-xsmall-CoLA This model is a fine-tuned version of [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4237 - Matthews Correlation: 0.5895 | 84218d06e202496b8ab8bc9627f3e024 |
mit | ['generated_from_trainer'] | false | Model description Trying to find a decent optimum between accuracy/quality and inference speed. ```json { "epoch": 3.0, "eval_loss": 0.423, "eval_matthews_correlation": 0.589, "eval_runtime": 5.0422, "eval_samples": 1043, "eval_samples_per_second": 206.853, "eval_steps_per_second": 51.76... | 75550a3d896322a5f922ecf7259895d0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 16105 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - l... | 6e65d30cf9ca251b154a2f9380731b96 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.3945 | 1.0 | 67 | 0.4323 | 0.5778 | | 0.3214 | 2.0 | 134 | 0.4237 | 0.5895 | | 0.3... | e4f300ce132815e45d359ab3571f9f04 |
apache-2.0 | ['generated_from_trainer'] | false | mini-mlm-tweet-target-imdb This model is a fine-tuned version of [muhtasham/mini-mlm-tweet](https://huggingface.co/muhtasham/mini-mlm-tweet) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.4742 - Accuracy: 0.8324 - F1: 0.9085 | bb42e57e4f345a287fddb89c9da2e194 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4141 | 0.64 | 500 | 0.2415 | 0.9025 | 0.9487 | | 0.3008 | 1.28 | 1000 | 0.2407 | 0.9046 | 0.9499 | | 0.2573 |... | cce6fd7b9cf6ec19912ec3c827708901 |
mit | [] | false | Phan's Collage on Stable Diffusion This is the `<pcollage>` 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 al... | 3d9fb85441650dba3483b020305dbced |
mit | ['camembert', 'answer extraction'] | false | Extraction de réponse Ce modèle est _fine tuné_ à partir du modèle [camembert-base](https://huggingface.co/camembert-base) pour la tâche de classification de tokens. L'objectif est d'identifier les suites de tokens probables qui pourrait être l'objet d'une question. | 4f0b7ea798c4b3c139e8de2f5f29502d |
mit | ['camembert', 'answer extraction'] | false | Données d'apprentissage La base d'entrainement est la concatenation des bases SquadFR, [fquad](https://huggingface.co/datasets/fquad), [piaf](https://huggingface.co/datasets/piaf). Les réponses de chaque contexte ont été labelisées avec le label "ANS". Volumétrie (nombre de contexte): * train: 24 652 * test: 1 370... | 3af64bad08ac120ec20632927536851a |
mit | ['camembert', 'answer extraction'] | false | Entrainement L'apprentissage s'est effectué sur une carte Tesla K80. * Batch size: 16 * Weight decay: 0.01 * Learning rate: 2x10-5 (décroit linéairement) * Paramètres par défaut de la classe [TrainingArguments](https://huggingface.co/transformers/main_classes/trainer.html | 8d502e0533b6508c78b6701d13bc70f5 |
mit | ['camembert', 'answer extraction'] | false | Critiques Le modèle n'a pas de bonnes performances et doit être corrigé après prédiction pour être cohérent. La tâche de classification n'est pas évidente car le modèle doit identifier des groupes de token _sachant_ qu'une question peut être posée.  | 79682ae7579b3aa4c176ba70385ba940 |
mit | ['camembert', 'answer extraction'] | false | Utilisation _Le modèle est un POC, nous garantissons pas ses performances_ ```python from transformers import AutoTokenizer, AutoModelForTokenClassification import numpy as np model_name = "lincoln/camembert-squadFR-fquad-piaf-answer-extraction" loaded_tokenizer = AutoTokenizer.from_pretrained(model_path) loaded_m... | 9e8fdac74c8f6dba52999d6bac525f9f |
mit | ['generated_from_trainer'] | false | rte_roberta-base_144_v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE RTE dataset. It achieves the following results on the evaluation set: - Loss: 0.6194 - Accuracy: 0.7256 | 186a6f069cf2fd6714e30e805dae0b87 |
mit | ['generated_from_trainer'] | false | bart-large-cnn-qmsum-meeting-summarization This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.7578 - Rouge1: 37.9431 - Rouge2: 10.6366 - Rougel: 25.5782 - Rougelsum: 3... | aa9d016282dfa8bf0fe91e706afc454c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-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 - lr_scheduler_warmup_steps: 500 - num_epochs: 500 - label_smoothing_fac... | 9843f7c8278a1c09de1f768a631ea684 |
cc-by-4.0 | ['espnet', 'audio', 'text-to-speech'] | 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 d5b5ec7b2e77bd3e10707141818b7e6c57ac6b3f pip install -e . cd egs2/amadeus/tts1 ./run.sh --skip_data_prep false --skip_train tru... | 6b9676460218a74067d033c66da3e712 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2174 - Accuracy: 0.923 - F1: 0.9231 | c200cf4e1abe5aa12e3f5fdc19059a60 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8279 | 1.0 | 250 | 0.3099 | 0.9075 | 0.9048 | | 0.2464 | 2.0 | 500 | 0.2174 | 0.923 | 0.9231 | | 7598901c57eee01456a19d45675fe244 |
mit | [] | false | This is a strong pre-trained RoBERTa-Large NLI model. The training data is a combination of well-known NLI datasets: [`SNLI`](https://nlp.stanford.edu/projects/snli/), [`MNLI`](https://cims.nyu.edu/~sbowman/multinli/), [`FEVER-NLI`](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever... | 1f30038a29f987bec598cf3336a3db9c |
mit | [] | false | hg_model_hub_name = "ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli" tokenizer = AutoTokenizer.from_pretrained(hg_model_hub_name) model = AutoModelForSequenceClassification.from_pretrained(hg_model_hub_name) tokenized_input_seq_pair = tokenizer.encode_plus(premise, hypothesis, ... | 9498560114d148d26290698d5f7016d8 |
mit | [] | false | remember bart doesn't have 'token_type_ids', remove the line below if you are using bart. token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0) attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0) outputs = model(input_ids, ... | c1ce23ddfdfaec556c5de8b120410951 |
mit | [] | false | batch_size only one print("Premise:", premise) print("Hypothesis:", hypothesis) print("Entailment:", predicted_probability[0]) print("Neutral:", predicted_probability[1]) print("Contradiction:", predicted_probability[2]) ``` More in [here](https://github.com/facebookresearch/anli/blob/master/src/... | 560c1710b0e294415ae011457015d33b |
cc-by-4.0 | [] | false | GenRead (MergeDPR): FiD model trained on TQA -- This is the model checkpoint of GenRead [2], based on the T5-3B and trained on the TriviaQA [1]. -- Hyperparameters: 8 x 80GB A100 GPUs; batch size 16; AdamW; LR 5e-5; best dev at 9000 steps References: [1] TriviaQA: A Large Scale Dataset for Reading Comprehension ... | e520db981a6de05948b39ef8a869754e |
cc-by-4.0 | [] | false | Model performance We evaluate it on the TriviaQA dataset, the EM score is 74.41. <a href="https://huggingface.co/exbert/?model=bert-base-uncased"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a> --- license: cc-by-4.0 --- | 7e24fcba236a0fd44788000b6807f626 |
apache-2.0 | ['generated_from_trainer'] | false | 4-way-detection-prop-16-xlnet This model is a fine-tuned version of [ultra-coder54732/4-way-detection-prop-16-bert](https://huggingface.co/ultra-coder54732/4-way-detection-prop-16-bert) on an unknown dataset. | b80f77cbee10cfdc272deb614476e957 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.3672 | 38de595ad6a67f00a01ac3d6a27f8a54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1755 | 1.0 | 11066 | 1.1177 | | 0.9004 | 2.0 | 22132 | 1.1589 | | 0.6592 | 3.0 | 33198 | 1.2326 | | 0.4823 | 4.0 | 44264 | 1.3672 ... | d524858ab0ebd2c13ba97c3e2a72009e |
mit | ['sentiment', 'Italian'] | false | Model description This model performs sentiment analysis on Italian political twitter sentences. It was trained starting from an instance of "bert-base-italian-uncased-xxl" and fine-tuned on an Italian dataset of tweets. You can try it out at https://www.unideeplearning.com/twitter_sa/ (in italian!) | 4c6add96d4f588b35cf658231e9efa08 |
mit | ['sentiment', 'Italian'] | false | Hands-on ```python import torch from torch import nn from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("unideeplearning/polibert_sa") model = AutoModelForSequenceClassification.from_pretrained("unideeplearning/polibert_sa") text = "Giusep... | be33bc1c78eaaaa1894cbf8251de14d1 |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | How to Use ```py class UDgoeswith(object): def __init__(self,bert): from transformers import AutoTokenizer,AutoModelForTokenClassification self.tokenizer=AutoTokenizer.from_pretrained(bert) self.model=AutoModelForTokenClassification.from_pretrained(bert) def __call__(self,text): import numpy,torch... | 630da64022c7791cbec982b4e8ab64a0 |
cc-by-sa-4.0 | ['english', 'token-classification', 'pos', 'dependency-parsing'] | false | text = "+text+"\n" v=[(s,e) for s,e in w["offset_mapping"] if s<e] for i,(s,e) in enumerate(v,1): q=self.model.config.id2label[p[i,h[i]]].split("|") u+="\t".join([str(i),text[s:e],"_",q[0],"_","|".join(q[1:-1]),str(h[i]),q[-1],"_","_" if i<len(v) and e<v[i][0] else "SpaceAfter=No"])+"\n" return... | de8bf75e79f1a8f9b0b1a8f380f8643f |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-fr_s51 Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure tha... | 23e32f4ee6614f37cc1db8bd7ef5455e |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Stable Diffusion v1 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. The **Stable-Diffusion-v-1-2** checkpoint was initialized with the weights of the [Stable-Diffusion-v-1-1](https:/steps/huggingface.co/CompVis/stable-diffusion-... | 4978d60da7947d7393e26837125ff93c |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Download the weights - [sd-v1-2.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt) - [sd-v1-2-full-ema.ckpt](https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2-full-ema.ckpt) This weights are intended to be used with the original [CompVis S... | 85837145f8266224926b115de054d6a7 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of... | c69d427bd9d6e56297b820ca1992d467 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_test 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: 91.1661 - Wer: 0.5714 | 2c47ad18a959f1fbf378b8e4bd53f4a4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 1000 | cb9c99ba9751bcc8d93cd818023e5c8d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 11.9459 | 100.0 | 100 | 46.9901 | 1.0 | | 3.2175 | 200.0 | 200 | 73.0950 | 1.0 | | 1.8117 | 300.0 | 300 | 78.4884 | 0.673... | c7dbe345bbc2386ca53da27f0e0158e5 |
apache-2.0 | ['generated_from_keras_callback'] | false | cochonaki/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1905 - Validation Loss: 0.5536 - Train Matthews Correlation: ... | f78b4c66c79155c511a3689673272d26 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Matthews Correlation | Epoch | |:----------:|:---------------:|:--------------------------:|:-----:| | 0.5118 | 0.4642 | 0.4617 | 0 | | 0.3259 | 0.4709 | 0.4990 | 1 | | 0.1905 | 0.5536... | 844e50710ba8dd7ccaf7c9354164546b |
apache-2.0 | [] | false | 8209;NCC|[🤗](https://huggingface.co/north/t5_small_NCC)|[🤗](https://huggingface.co/north/t5_base_NCC)|[🤗](https://huggingface.co/north/t5_large_NCC)|✔|[🤗](https://huggingface.co/north/t5_xxl_NCC)|| |North-T5& | 0f3c75460d7030a5f753c76b09d016d9 |
apache-2.0 | [] | false | 8209;lm|[🤗](https://huggingface.co/north/t5_small_NCC_lm)|[🤗](https://huggingface.co/north/t5_base_NCC_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)|[🤗](https://huggingface.co/north/t5_xxl_NCC_lm)|| | 343b8717b0a70d1acc081e3a6ae6aa14 |
mit | ['generated_from_trainer'] | false | deberta-large-finetuned-qqp This model is a fine-tuned version of [microsoft/deberta-large](https://huggingface.co/microsoft/deberta-large) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.2635 - Accuracy: 0.8986 - F1: 0.8648 | 42b4400707998551b344d2eec226d935 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.4058 | 1.0 | 22741 | 0.3923 | 0.8496 | 0.8108 | | 0.2347 | 2.0 | 45482 | 0.2635 | 0.8986 | 0.8648 | | e0b36d4b450c53d9b04a2afdbc755017 |
mit | ['summarization', 'mbart', 'bart'] | false | Résumé automatique d'article de presses Ce modèles est basé sur le modèle [`facebook/mbart-large-50`](https://huggingface.co/facebook/mbart-large-50) et été fine-tuné en utilisant des articles de presse issus de la base de données MLSUM. L'hypothèse à été faite que les chapeaux des articles faisaient de bon résumés d... | 60b730c74358d621288ba9fcde54cf32 |
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