license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-bert-base-max-pooling') model = AutoModel.from_pretrained('sentence-transformers/nli-bert-base-max-pooling') | 6962755c92369fab319e804b31971ed9 |
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/nli-bert-base-max-pooling) | 0ea0fa7ddbd583afeb667ae18cdbb81e |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_sst2_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.8734 - Accuracy: 0.7592 | 6b3542c0bff0f7899c1ff13f9fd80713 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0863 | 1.0 | 527 | 0.8734 | 0.7592 | | 0.5116 | 2.0 | 1054 | 1.1742 | 0.7489 | | 0.3952 | 3.0 | 1581 | 0.9197 | 0.... | 76e02c226652b76e647834a5bbf69163 |
creativeml-openrail-m | ['text-to-image'] | false | weirdcore Dreambooth model trained by abesmon with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v2-1-512 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebo... | 5cba05f49bc4820faee7fd4bcb067498 |
creativeml-openrail-m | ['text-to-image'] | false | Train pictures:      . 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/Ma... | a8167fef538d792a601f0ebc4cb82636 |
other | ['vision', 'image-segmentation'] | false | Intended uses & limitations You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other fine-tuned versions on a task that interests you. | 0fde21cbf6e824935b27d4e6e003299e |
other | ['vision', 'image-segmentation'] | false | load Mask2Former fine-tuned on COCO instance segmentation processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-coco-instance") model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-coco-instance") url = "http://images.cocodataset.org/val2017/000000039769... | f77166cd333501ae75a8777c695d70f9 |
other | ['vision', 'image-segmentation'] | false | we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs) predicted_instance_map = result["segmentation"] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). | 6d2056a62c13e5b69c50604a23f77153 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-8_belgium-2_s496 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 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When usin... | 4ae7160b2153b6ca21fe7252edd5d3f5 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-eli5 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.7275 - Rouge1: 9.944 - Rouge2: 1.908 - Rougel: 8.0145 - Rougelsum: 9.2275 - Gen Len: 18.9988 | 667ba28bd64d01104510557cfac25933 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 3.9806 | 1.0 | 17040 | 3.7726 | 9.8475 | 1.872 | 7.9462 | 9.1258 | 18.9972 ... | 339f1bc13c534fa1c16a3846ce70e1b7 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2-medium_sst2_negation0.01 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: 3.4416 | 2111f7adef4a665ad7ca2810dcabacdb |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8263 | 1.0 | 1060 | 3.3054 | | 2.5408 | 2.0 | 2120 | 3.3786 | | 2.3927 | 3.0 | 3180 | 3.4416 | | aeaf55633d9a9c58e2df1044391a4aee |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for levit_128s.fb_dist_in1k A LeViT image classification model using convolutional mode (using nn.Conv2d and nn.BatchNorm2d). Pretrained on ImageNet-1k using distillation by paper authors. | 3788d4cabf5eb91d919d4bc7b942c402 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model('levit_128s.fb_dist_in1k', pretrained=True) model = mod... | 0998394f73c5d12800ae8ec6ad1aac26 |
apache-2.0 | ['image-classification', 'timm'] | false | Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open( urlopen('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png')) model = timm.create_model( 'levit_128s.fb_dist_in1k', pretrained=True, nu... | 3b5087cd44c4135e5f7232bda71c73cd |
apache-2.0 | ['translation'] | false | opus-mt-lua-en * source languages: lua * target languages: en * OPUS readme: [lua-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lua-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | e97eee7c4cfa4a946c4d1f14822e04cb |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import MobileNetV2FeatureExtractor, MobileNetV2ForSemanticSegmentation from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor ... | b4f7f59a649d6b513be06d733fc94554 |
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: 0.1401 - F1: 0.8616 | 68a3b1896df2d180d4cd01483aeb612d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2605 | 1.0 | 525 | 0.1708 | 0.8198 | | 0.1274 | 2.0 | 1050 | 0.1415 | 0.8449 | | 0.0819 | 3.0 | 1575 | 0.1401 | 0.8616 | ... | d867c019b888f8493c842a3a14480528 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-42-QAData This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0873 - Precision: 0.4420 - Recall: 0.2887 - F1: 0.3493 - Accuracy: ... | f2c48f43ae09c3d1fdcb619420ac7e6d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 1.0 | 14f7347e944fec22c27f6e18328d7b2b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1064 | 1.0 | 3118 | 0.0873 | 0.4420 | 0.2887 | 0.3493 | 0.9755 | | d16c2619fe554ca5503fb2bc385aafb6 |
mit | ['generated_from_trainer'] | false | BACnet-Klassifizierung-Kaeltettechnik-bert-base-german-cased This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the [gart-labor](https://huggingface.co/gart-labor) "klassifizierung_kaelte_v2" dataset. It achieves the following results on the evaluation set... | dd91f1273f2adb70ab48cb721f23d06b |
mit | ['generated_from_trainer'] | false | Model description This model makes it possible to classify the refrigeration components described with the BACnet standard into different categories. The model is based on a German-language data set. | d5774d258ac0708358861a9d53bdf234 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_ep... | 0cbc7a30dedbd7f0349690e7a34f0aa9 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------:| | 0.0426 | 0.85 | 5 | 0.0439 | [0.85714286 0.98507463 1. 1. ] | | 0.... | 97a096576d4a3936ac87f6a94db28298 |
mit | ['generated_from_trainer'] | false | poetry-gpt2-large-with-hoel This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.5612 | 9088bb5fa1b6b73bab34e959341863e5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 | c2fb7ac583ef11cbcb6f8314d12b27f2 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.673 | 1.0 | 20539 | 3.6197 | | 3.299 | 2.0 | 41078 | 3.5369 | | 3.0433 | 3.0 | 61617 | 3.5612 | | 5787adced6a7ee96f05d1d9566dff095 |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | **Deprecation Notice** This model is deprecated. New Filipino Transformer models trained with a much larger corpora are available. Use [`jcblaise/roberta-tagalog-base`](https://huggingface.co/jcblaise/roberta-tagalog-base) or [`jcblaise/roberta-tagalog-large`](https://huggingface.co/jcblaise/roberta-tagalog-large)... | 135479fe632f6361cc5e73fda9d762e0 |
gpl-3.0 | ['electra', 'tagalog', 'filipino'] | false | ELECTRA Tagalog Small Uncased Discriminator Tagalog ELECTRA model pretrained with a large corpus scraped from the internet. This model is part of a larger research project. We open-source the model to allow greater usage within the Filipino NLP community. This is the discriminator model, which is the main Transformer... | d61dc366a4367dcab341043b22cd796b |
apache-2.0 | ['cross-encoder', 'sentence-similarity', 'transformers'] | false | Cross-Encoder The model can be used for Information Retrieval: given a query, encode the query will all possible passages. Then sort the passages in a decreasing order. <p align="center"> <img src="https://www.exibart.com/repository/media/2020/07/bridget-riley-cool-edge.jpg" width="400"> </br> Bridget Riley,... | 3bce475825549fb4f21bea67230fb291 |
apache-2.0 | ['cross-encoder', 'sentence-similarity', 'transformers'] | false | Usage and Performance ```python from sentence_transformers import CrossEncoder model = CrossEncoder('efederici/cross-encoder-distilbert-it') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sente... | a62172085be1d3f7e10d81402f7c6f71 |
cc-by-4.0 | ['generated_from_trainer'] | false | bert-large-uncased-whole-word-masking-squad2-with-ner-Pistherea-conll2003-with-neg-with-repeat This model is a fine-tuned version of [deepset/bert-large-uncased-whole-word-masking-squad2](https://huggingface.co/deepset/bert-large-uncased-whole-word-masking-squad2) on the squad_v2 and the conll2003 datasets. | d5dc8c223fff20e9fbdd665e65a72ffa |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | fd8a1a1e3337b89272da663bc65d74dd |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-amazon-en-es-fr-mlm This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the Yaxin/amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 1.3936 - Accuracy: 0.6951 | 824f1605a366015ba1f999f37f368a33 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 | be07c0cddde02c172757d77fa7414cc6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | Why Epic Diffusion Epîc Diffusion is a general purpose model based on Stable Diffusion 1.x intended to replace the official SD releases as your default model. It is focused on providing high quality output in a wide range of different styles, with support for NFSW content. Epîc Diffusion 1.0 is a heavily calibrate... | 71ab6548c467be8653fe162267787cb6 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. CompVis claims no rights on the output... | b50b5dcb8b2ba6bee2d4b5e64ec8c4d9 |
creativeml-openrail-m | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image'] | false | e5e7eb"> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid"> Prompt </th> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid"> Parameters </th> <th style="vertical-align:top;padding:.5714286em!important;border: 1px solid;min-width:270p... | 9c6f40a7a11a72862a51e0a181a73f01 |
apache-2.0 | [] | false | LongT5 (transient-global attention, base-sized model) LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com... | 525c84742daf379a2b3cdc94fd75da9f |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, LongT5Model tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base") model = LongT5Model.from_pretrained("google/long-t5-tglobal-base") inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden... | a7841473d8702a53f15b4e71eb7bb2ad |
mit | ['generated_from_trainer'] | false | output This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6440 | 7dc163e8bbc0e6cb5b4d149d54be5e7d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - lr_s... | beb559e113dcaff73a352101a4ed8099 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 1.1985 | 0.22 | 2500 | 1.0940 | | 1.0937 | 0.44 | 5000 | 1.0033 | | 1.0675 | 0.66 | 7500 | 0.9753 | | 1.0565 | 0.87 | 10000 | 0... | bbcd5baf217b99f3bf6a06ec82469ea5 |
apache-2.0 | [] | false | This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text without predicting their location types. The model is trained using the training splits of all events from [IDRISI-R d... | c03bcc527fd52765a2e3b6cd89490eb8 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Ko(Gyungsang dialect) - p4b This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the KR Dialect Speech - gyungsang dataset. It achieves the following results on the evaluation set: - Loss: 0.2017 - Wer: 15.9300 | cffd60a5d4d08983fba9c352aff4754e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 96 - eval_batch_size: 64 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - train... | 6c8108d6dcf1c5ce469dafaebe749479 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5909 | 0.2 | 1000 | 0.4133 | 211.6022 | | 0.3612 | 0.4 | 2000 | 0.2137 | 16.9429 | | 0.5373 | 0.6 | 3000 | 0.2063 | 15... | 20f8fdce69f86781d991f182bd038fd2 |
cc-by-4.0 | [] | false | How to use Here is how to use this model to get a summary of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("TalTechNLP/mBART-ERRnews") model = AutoModelForSeq2SeqLM.from_pretrained("TalTechNLP/mBART-ERRnews") text = "Riigik... | 21887a1eabf3be0a166b7dec62316a4a |
cc-by-4.0 | [] | false | Evaluation results This model achieves the following results: | Dataset | ROUGE-1 | ROUGE-2 | ROUGE-L | ROUGE-L-SUM | |:-------:|:-------:|:-------:|:-------:|:-----------:| | ERRnews | 19.2 | 6.7 | 16.1 | 17.4 | | 56b85847d56926538a11b346bd25a9b9 |
cc-by-4.0 | [] | false | BibTeX entry and citation info ```bibtex article{henryabstractive, title={Abstractive Summarization of Broadcast News Stories for {Estonian}}, author={Henry, H{\"a}rm and Tanel, Alum{\"a}e}, journal={Baltic J. Modern Computing}, volume={10}, number={3}, page... | 3d2f6de528648083d36005b8a53b256e |
mit | ['reward-model', 'reward_model', 'RLHF'] | false | Reward model trained from human feedback Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. RM are useful in these domain: - QA model evaluation - serves as reward score in RLHF All models are train on these dataset with a same split seed across datasets (... | 6a8a37f2d964f0f2a1c9dc04dc78f99d |
mit | ['reward-model', 'reward_model', 'RLHF'] | false | How to use ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer reward_name = "OpenAssistant/reward-model-deberta-v3-large" rank_model, tokenizer = AutoModelForSequenceClassification.from_pretrained(reward_name), AutoTokenizer.from_pretrained(reward_name) question, answer = "Explain nuclear ... | ec9ef28e3757aaf87f358cdd7600ada4 |
mit | ['reward-model', 'reward_model', 'RLHF'] | false | Performance Validation split accuracy | Model | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons) | [Summary](https://huggingface.co/datasets/openai/summarize_from_feedback) | [SytheticGPT](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | |---|---|---|---| | [electra-l... | e37ea931583e6e027a195ad44cbbbd84 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-ar This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4819 - Wer: 0.4244 | 3979ee2b8b4eb0c9af5644a1aa6b1e87 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 0a7797f5c8176a8210b02b9f25f47292 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 11.0435 | 0.67 | 400 | 4.3104 | 1.0 | | 3.4451 | 1.34 | 800 | 3.1566 | 1.0 | | 3.1399 | 2.01 | 1200 | 3.0532 | 0.999... | 86a18ff2b812c51457e60b95c9ffddc7 |
apache-2.0 | ['generated_from_trainer'] | false | segformer-b0-finetuned-pokemon This model is a fine-tuned version of [ydmeira/segformer-b0-finetuned-pokemon](https://huggingface.co/ydmeira/segformer-b0-finetuned-pokemon) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0157 - Mean Iou: 0.4970 - Mean Accuracy: 0.9940 - Overall... | 56d3e24dbefcfe0c778cfb1adaaaa69f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 | 0518aba42800f6e44cfccec21470bb74 |
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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-----------------------... | 6528c770be973f5e41808b34c0f539f7 |
mit | [] | false | bert-base-multilingual-uncased-sentiment This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5). This model is intended for di... | c1cf9551b9e4111637053fe95bd1f0f1 |
mit | [] | false | Training data Here is the number of product reviews we used for finetuning the model: | Language | Number of reviews | | -------- | ----------------- | | English | 150k | | Dutch | 80k | | German | 137k | | French | 140k | | Italian | 72k | | Spanish | 5... | 10ca0440c60bd18a7976623790bf2005 |
mit | [] | false | Accuracy The finetuned model obtained the following accuracy on 5,000 held-out product reviews in each of the languages: - Accuracy (exact) is the exact match on the number of stars. - Accuracy (off-by-1) is the percentage of reviews where the number of stars the model predicts differs by a maximum of 1 from the num... | c17845b144b719cf219689a7b51ed4af |
mit | [] | false | Contact In addition to this model, [NLP Town](https://www.nlp.town) offers custom, monolingual sentiment models for many languages and an improved multilingual model through [RapidAPI](https://rapidapi.com/nlp-town-nlp-town-default/api/multilingual-sentiment-analysis2/). Feel free to contact us for questions, feed... | 8d81274e63de134cf3bd6d898e57f504 |
creativeml-openrail-m | ['text generation', 'conversational'] | false | Model description Pymalion 2.7B is a proof-of-concept dialogue model based on EleutherAI's [gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B). **Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances. | 41d55d123037db5640776dc73a5932ce |
creativeml-openrail-m | ['text generation', 'conversational'] | false | Training procedure Model weights were initialized from the `uft-2.7b` ConvoGPT model made available in [this commit](https://huggingface.co/hakurei/convogpt/tree/07707377dee0aa7d1ee5363ef660b13eb5b73f9d/2.7b-uft). The model was then further fine-tuned on ~48.5 million tokens for ~5k steps on 4 NVIDIA A40s using Deep... | 90d32abbe7180529960a6f4720af5958 |
creativeml-openrail-m | ['text generation', 'conversational'] | false | The easy way We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found [here](https://github.com/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb). | 12f2234578f862fbbceca4f4a349b6e8 |
creativeml-openrail-m | ['text generation', 'conversational'] | false | The manual way The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format: ``` [CHARACTER]'s Persona: [A few sentences about the character you want the model to play] <START> [DIALOGUE HISTORY] You: [Your input message here] [CHARACTER]: ``` ... | aa3625abde35ce9f84dede40e6bd24fb |
apache-2.0 | ['sbb-asr', 'generated_from_trainer'] | false | Whisper Small German SBB all SNR - v2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBB Dataset 05.01.2023 dataset. It achieves the following results on the evaluation set: - Loss: 0.7183 - Wer: 1.8738 | 7e6fff4cf0c089b236373b37183dc418 |
apache-2.0 | ['sbb-asr', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precisi... | a86382aa7139b4062030088d768c553b |
apache-2.0 | ['sbb-asr', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.5636 | 0.71 | 100 | 2.7931 | 1.1541 | | 1.4736 | 1.42 | 200 | 0.8866 | 1.0444 | | 0.8446 | 2.13 | 300 | 0.9127 | 1.5136 | |... | e3d66d8a8c2630aaa7d1a055bed0b882 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 7b765ded3a30a0d30c10bd561a9dba35 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.9827 | 0.29 | 1000 | inf | 0.2937 | | 1.0203 | 0.57 | 2000 | inf | 0.2711 | | 1.0048 | 0.86 | 3000 | inf | 0.262... | 7fc6985eec8161bb7368898a23cef61e |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8` with split `test` ```bash python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_i... | 178742ff336eab6c14d5c47884e1b656 |
apache-2.0 | ['generated_from_trainer'] | false | chinese-bert-wwm-finetuned-jd This model is a fine-tuned version of [hfl/chinese-bert-wwm](https://huggingface.co/hfl/chinese-bert-wwm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9340 | 1a5795468fd95c8d21f8b10c27ace838 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1648 | 1.0 | 5 | 2.9366 | | 3.0095 | 2.0 | 10 | 2.9487 | | 3.0698 | 3.0 | 15 | 2.9177 | | c9df6c6cf73479bb39297261bfb1c080 |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-base-frquad-qg` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generat... | be8d1dc4cdf8eb77e529b573e1670c1a |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** fr - **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/l... | c736565d97eb21e8f327fbeaca2fe299 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="... | adac983a414dcdf2dda0b21fcd9b22b5 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | Score | Type | Dataset | |:------... | ee7d8c4f360514f3fff7d4e94388b9ff |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 24 - b... | 29541aa27e45ea35be8938323cefea63 |
apache-2.0 | ['translation'] | false | opus-mt-en-mt * source languages: en * target languages: mt * OPUS readme: [en-mt](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-mt/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 3afeaac47a51b1fd766d6ed3fc4fd247 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.2781 - Precision: 0.8121 - Recall: 0.8302 - F1: 0.8210 - Accuracy: 0.9204 | b2d750bda740025eb27aba75dfadeb10 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3504 | 1.0 | 1250 | 0.2922 | 0.7930 | 0.8075 | 0.8002 | 0.9115 | | 0.2353 | 2.0 |... | b7cf82d22850997a5e8f798c4c80e041 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | mpid-hassanblend-v1-5-main Dreambooth model trained by tftgregrge 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/TheLas... | f44069709a07599f64f1c5c0e640f327 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-ali-hasan-colab-EX2 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: 0.5087 - Wer: 0.4458 | c948f43c6f6335e8e711917369a29983 |
apache-2.0 | ['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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_t... | a61dfe48c98dc6376535b069d7039a1a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1956 | 13.89 | 500 | 0.5087 | 0.4458 | | 0.1946 | 27.78 | 1000 | 0.5087 | 0.4458 | | ba66b10ebf505746192fa1fd6f9f58a1 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-woody This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.4349 - Accuracy: 0.7927 | 0649ea7da6e4a8c89a685bb989ec324b |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 10fa458e3d4edcfcd9d0340c70138053 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.632 | 1.0 | 58 | 0.5883 | 0.6836 | | 0.6067 | 2.0 | 116 | 0.6017 | 0.6848 | | 0.5865 | 3.0 | 174 | 0.5695 | 0.... | 3b15fbe0b06a729822f23b7543bb2aef |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | PyTorch This is an INT8 PyTorch model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2). The calibration dataloader is the train da... | 7ada3774bc8d22bda1a9813f2963537f |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | Load with Intel® Neural Compressor: ```python from optimum.intel.neural_compressor import IncQuantizedModelForSequenceClassification model_id = "Intel/albert-base-v2-sst2-int8-static" int8_model = IncQuantizedModelForSequenceClassification.from_pretrained(model_id) ``` | cf76c0e44d96b083ba398fc56aab6dd2 |
apache-2.0 | ['text-classfication', 'int8', 'Intel® Neural Compressor', 'neural-compressor', 'PostTrainingStatic'] | false | ONNX This is an INT8 ONNX model quantized with [Intel® Neural Compressor](https://github.com/intel/neural-compressor). The original fp32 model comes from the fine-tuned model [Alireza1044/albert-base-v2-sst2](https://huggingface.co/Alireza1044/albert-base-v2-sst2). | ed098a5bb045398091c7c784a848140d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-amazon-shoe-reviews 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: 0.9532 - Accuracy: 0.5779 - F1: [0.62616119 0.46456105 0.50993865 0.55755123 0.734375 ] ... | 7f76a93556fec8a06c3f50f339dd4dab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:... | 00b8bd9c879f6bebd762adc38e999114 |
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