license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
cc-by-sa-4.0 | ['finance'] | false | Training The models are trained with the same configuration as BERT base in the [original BERT paper](https://arxiv.org/abs/1810.04805); 512 tokens per instance, 256 instances per batch, and 1M training steps. | b87270bf943d2b00aa2a146800394876 |
mit | ['generated_from_trainer'] | false | roberta-base-rte 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: 0.7660 - Accuracy: 0.7581 | 4e82db2b37b881f94eba037ef0528fbd |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.551 | 3.21 | 500 | 0.7660 | 0.7581 | | 0.1665 | 6.41 | 1000 | 1.5218 | 0.7690 | | 0.0463 | 9.62 | 1500 | 1.6747 | 0.... | ce697ed0a4d4d55bdc68238ff4cfb987 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-complaints-wandb-product This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set: - Loss: 0.4431 - Accuracy: 0.8691 - F1: 0.8645 - Recall: 0.8691 - Pre... | 6a58d9efe7ccfdb187e08296636c6023 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_tra... | 5eaef1aced718056a2005c26ee5db1b9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:| | 0.562 | 0.51 | 2000 | 0.5107 | 0.8452 | 0.8346 | 0.8452 | 0.8252 | | 0.4548 | 1.01... | 86393e24ef29ee02ec8a4062f78e9eed |
mit | [] | false | matrix on Stable Diffusion This is the `<hatman-matrix>` 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 ... | aadef43f2b94538c07be1ffbd341c8a0 |
mit | [] | false | Troubleshooting This concept was trained using "CompVis/stable-diffusion-v1-4" which is linked to in the inference notebook for concepts and has a tensor length of [756]. The notebook to train concepts links to "stabilityai/stable-diffusion-2" which has a tensor length of [1024]. Here is the new concept you will be a... | 398a90c409ada41e2a8fde3c6bd86fdc |
apache-2.0 | ['Quality Estimation', 'monotransquest', 'DA'] | false | Using Pre-trained Models ```python import torch from transquest.algo.sentence_level.monotransquest.run_model import MonoTransQuestModel model = MonoTransQuestModel("xlmroberta", "TransQuest/monotransquest-da-ne_en-wiki", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["R... | b75d78720e13288508a3dc244169ef63 |
apache-2.0 | ['translation'] | false | opus-mt-es-to * source languages: es * target languages: to * OPUS readme: [es-to](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/es-to/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-16.zip](https://... | 4a854d03eceda7b0de1c793fa1d897ba |
mit | ['generated_from_trainer'] | false | IMDB_roBERTa_5E This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2383 - Accuracy: 0.9467 | 06c1b2e622cf2d330d9fcc8d692cab0f |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5851 | 0.06 | 50 | 0.1789 | 0.94 | | 0.2612 | 0.13 | 100 | 0.1520 | 0.9533 | | 0.2339 | 0.19 | 150 | 0.1997 | 0.... | 901fdc07108a62708ad3e1b16002455b |
mit | [] | false | crested gecko on Stable Diffusion This is the `<crested-gecko>` 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 ca... | 4762f21b5f228ffd1dec3d7dc0521e56 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-evn4-ntsema-colab This model is a fine-tuned version of [ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab](https://huggingface.co/ntsema/wav2vec2-xlsr-53-espeak-cv-ft-sah2-ntsema-colab) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 2.0... | 7acb61d2b9d3677b40f59ef0c68ae1ab |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.3115 | 6.15 | 400 | 1.6416 | 0.9867 | | 0.9147 | 12.3 | 800 | 1.6538 | 0.9867 | | 0.5301 | 18.46 | 1200 | 1.8461 | 0.98 | |... | 8e78807aab0b6686c519d086a3366b8e |
apache-2.0 | ['generated_from_trainer'] | false | text-to-sparql-t5-base-2021-10-17_23-40 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2645 - Gen Len: 19.0 - P: 0.5125 - R: 0.0382 - F1: 0.2650 - Score: 5.1404 - Bleu-precisions: [88.4926849765078... | 7719fef85bdf8b199725a09f295a5eb8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Score | Bleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:------:|:------------------... | 60ef4c5daa43d912311474cb3ad450a3 |
cc-by-4.0 | ['generated_from_trainer'] | false | CTEBMSP_ner_test2 This model is a fine-tuned version of [chizhikchi/Spanish_disease_finder](https://huggingface.co/chizhikchi/Spanish_disease_finder) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0586 - Diso Precision: 0.8836 - Diso Recall: 0.8902 - Diso F1: 0.8869 - Diso Num... | 9c4d3c73fd255825f03fdd258721ddbb |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Diso Precision | Diso Recall | Diso F1 | Diso Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:-----------------:... | f945a70552a8f95cbe35d182e0ba6caa |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2183 - Accuracy: 0.925 - F1: 0.9251 | 21e0b8607079a67a0a9a4468d37e41f1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8002 | 1.0 | 250 | 0.3094 | 0.9065 | 0.9038 | | 0.2409 | 2.0 | 500 | 0.2183 | 0.925 | 0.9251 | | 55da80bc133c4be6714edb0b1f84bc25 |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-squad This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.9492 | 585e1de6928debd37978ebdd71d274c7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.8695 | 1.0 | 8248 | 0.8813 | | 0.6333 | 2.0 | 16496 | 0.8042 | | 0.4372 | 3.0 | 24744 | 0.9492 | | b2923f8b098ff849da5587ed2fd4e3e6 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HA dataset. It achieves the following results on the evaluation set: - Loss: 0.4998 - Wer: 0.5153 | 742c312ef35cf813870ab689257abf21 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | f9bcc915ed78c4c33ff412901e50a4d8 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.0021 | 8.33 | 500 | 2.9059 | 1.0 | | 2.6604 | 16.66 | 1000 | 2.6402 | 0.9892 | | 1.2216 | 24.99 | 1500 | 0.6051 | 0.6851 | |... | b124ba0aee41e54c28e4a08f12e5ff05 |
mit | [] | false | Christo person on Stable Diffusion This is the `<christo>` 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 als... | a5198378bf3e12ff3e757327e2028af6 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the rio concept trained by marshmellow77 on the marshmellow77/pics_rio dataset. This is a Stable Diffusion model fine-tuned on the rio concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of rio cat** This model was created as part of the DreamBooth Hackathon 🔥.... | b1e833c79919fcb60bbd5ecdc6ff66c8 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | model-translate-ar-to-en-from-120k-dataset-ar-en-th230111752 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2879 - Bleu: 36.3711 | 71becdb4c632913b34d6fb921af58fca |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.3225 | 1.0 | 12500 | 1.3048 | 35.6396 | | 1.0963 | 2.0 | 25000 | 1.2906 | 36.2535 | | 1.1074 | 3.0 | 37500 | 1.2879 | 3... | 89491b669b947d0663256a218221b6c8 |
apache-2.0 | ['generated_from_trainer'] | false | german_pretrained This model is a fine-tuned version of [flozi00/wav2vec-xlsr-german](https://huggingface.co/flozi00/wav2vec-xlsr-german) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9812 - Wer: 1.0 | 1bb3fa720184b6c56d0fc921fadaef1f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 12.5229 | 5.0 | 5 | 12.9520 | 1.0 | | 4.3782 | 10.0 | 10 | 5.5689 | 1.0 | | 2.56 | 15.0 | 15 | 4.8410 | 1.0 | | 2.2895 ... | 40d42636c4c0d0159d4d226e8032f4ca |
cc-by-4.0 | [] | false | algmon-base for QA This is the base model for QA [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. | 094fda8fb854b06ae3434101dd16c59d |
cc-by-4.0 | [] | false | In Haystack Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/): ```python reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2") | 2b91f751643f10efef8d3bb91af9c40f |
cc-by-4.0 | [] | false | or reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2") ``` For a complete example of ``roberta-base-squad2`` being used for Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)... | ecba7ff72391cb42dee4a4157b5742b8 |
cc-by-4.0 | [] | false | Performance Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/). ``` "exact": 79.87029394424324, "f1": 82.91251169582613, "total": 11873, "HasAns_exact": 77.93522267206478, "HasAns_f1": 84.02838248389763, "... | 85895c841418e346263e8566e64e73de |
cc | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'sts'] | false | Sentence similarity model based on SlovakBERT This is a sentence similarity model based on [SlovakBERT](https://huggingface.co/gerulata/slovakbert). The model was fine-tuned using [STSbenchmark](ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) [Cer et al 2017] translated to Slovak using [M2M100](https://huggingface.co... | 96982a0d0e36f12e626a08db340e62f5 |
cc | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'sts'] | false | Usage Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence i... | cfeec859160e7d39d4d59978b7d4397f |
cc | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'sts'] | false | Cite ``` @article{DBLP:journals/corr/abs-2109-15254, author = {Mat{\'{u}}s Pikuliak and Stefan Grivalsky and Martin Konopka and Miroslav Blst{\'{a}}k and Martin Tamajka and Viktor Bachrat{\'{y}} and Mari{\'{a}}n Simko and ... | 0a4560c80c5c7f3b29b1ad77dc90dfa0 |
apache-2.0 | ['generated_from_keras_callback'] | false | classificationEsp1 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset. It achieves the following results on the evaluation set: | 71ab49e6a0e1257a386293c1c2b1980d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3864, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay':... | b0e082f22a8777969ddb18fb88c80b94 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_unispeech-sat_s833 Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your spee... | 5b0e1f5c0d10adf8bf6ea3f5509af1c9 |
apache-2.0 | ['generated_from_keras_callback'] | false | nandysoham/9-clustered This model is a fine-tuned version of [Rocketknight1/distilbert-base-uncased-finetuned-squad](https://huggingface.co/Rocketknight1/distilbert-base-uncased-finetuned-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6059 - Train End Logits Acc... | 9e449e518d6bd2f6ea7ce4bbea8fe345 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1004, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta... | c6722706b7738570a1b67206c948a23a |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | cb6fc3d0fa13f63aa1c57233f1524c48 |
apache-2.0 | ['translation'] | false | opus-mt-de-de * source languages: de * target languages: de * OPUS readme: [de-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/de-de/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://... | d78e24c6ec53611dfa016db256f295e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.4881 | 0.8184 | | 035528cc012ed550f76e69bac68b20a9 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-imdb This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2887 | 4b7266aa810755a68f1211ce3dcce9ea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.6449 | 1.0 | 157 | 2.3557 | | 2.4402 | 2.0 | 314 | 2.2897 | | 2.3804 | 3.0 | 471 | 2.3011 | | 058c9f89c459a57283b37491d5a08227 |
apache-2.0 | ['translation'] | false | pol-ukr * source group: Polish * target group: Ukrainian * OPUS readme: [pol-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/pol-ukr/README.md) * model: transformer-align * source language(s): pol * target language(s): ukr * model: transformer-align * pre-processing: normalization + Sen... | e070bff54da631d8635c420076e0f4c6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: pol-ukr - source_languages: pol - target_languages: ukr - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/pol-ukr/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['pl', 'uk'] - src_constituents: {'pol'} - tgt_const... | 4258db5552e37e9e63c74e7cc8275b19 |
mit | [] | false | model by osanseviero This your the Stable Diffusion model fine-tuned the Mr Potato Head concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks mr potato head** You can also train your own concepts and upload them to the library by using [this notebook](... | 0d46b8ee4fb18981d58d256d61ca15db |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Rahul Soni This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 subset test dataset. It achieves the following results on the evaluation set: - Loss: 1.0458 - Wer: 525.0 | 9fbc701d1b2526072e77c9d1035cf540 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-----:| | 0.0 | 1000.0 | 1000 | 0.9920 | 450.0 | | 0.0 | 2000.0 | 2000 | 0.9749 | 475.0 | | 0.0 | 3000.0 | 3000 | 1.0266 | 525.0 | |... | 52ee4ee54e3fcf9ec57611dc1131886c |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | DreamBooth model for Starcraft:Remastered terrain  This is a Stable Diffusion model fine-tuned on Starcraft terrain images with DreamBooth. It can be used by adding the `instance_prompt`: **isometric starcraft ... | 0a75d017ff9e25f62e0540b3ecf2378d |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Use CUDA (otherwise it will take 15 minutes) device = "cuda" tilenet = AutoencoderTile.from_pretrained( "wdcqc/starcraft-terrain-64x64", subfolder="tile_vae_{}".format(tileset) ).to(device) pipeline = WaveFunctionDiffusionPipeline.from_pretrained( "wdcqc/starcraft-terrain-64x64", tile_vae = tilenet, ... | 2be56f99c8be16195797fa0b88864b07 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | need to include the dreambooth keywords "isometric starcraft {tileset_keyword} terrain" tileset_keyword = get_tileset_keyword(tileset) pipeline_output = pipeline( "lost temple, isometric starcraft {} terrain".format(tileset_keyword), num_inference_steps = 50, guidance_scale = 3.5, wfc_guidance_start_st... | 67416dcc10b6040802229dafe23c8e68 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'landscape'] | false | Generate map file from wfd.scmap import tiles_to_scx import random, time tiles_to_scx( tile_result, "outputs/{}_{}_{:04d}.scx".format(tileset, time.strftime("%Y%m%d_%H%M%S"), random.randint(0, 1e4)), wfc_data_path = wfc_data_path ) | 46f032fa7009218c47b882c036196534 |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_data_aug_mnli This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.9046 - Accuracy: 0.6099 | 057424b7bafab42d0a12279916c6f4c4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 0.8429 | 1.0 | 62880 | 0.8755 | 0.6185 | | 0.6713 | 2.0 | 125760 | 0.9512 | 0.6039 | | 0.5387 | 3.0 | 188640 | 1.0796 ... | 8760f0979714bcdad655e5814b369982 |
apache-2.0 | ['generated_from_trainer'] | false | reviews-classification This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5442 - Accuracy: 0.875 | cb80f1a54bdadb91965e46b65af200e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 350 | 0.4666 | 0.86 | | 0.4577 | 2.0 | 700 | 0.5500 | 0.8525 | | 0.2499 | 3.0 | 1050 | 0.5442 | 0.... | 3e7fe91f31a02fa3c7859d014a7c8eda |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'food'] | false | DreamBooth model for the jairzza concept trained by jairNeto on the jairNeto/pizza dataset. This is a Stable Diffusion model fine-tuned on the jairzza concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of jairzza pizza** This model was created as part of the DreamBooth Hackathon 🔥... | e6c6784c1551b46d73daf6b6f1cdcb91 |
mit | ['generated_from_trainer', 'de'] | false | feinschwarz This model is a fine-tuned version of [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). The dataset was compiled from all texts of https://www.feinschwarz.net (as of October 2021). The homepage gathers essayistic texts on theological topics. The model will be used to explore the challenges o... | 5d3a60aedfcf958ee2c73af282d8c9c1 |
mit | ['generated_from_trainer', 'de'] | false | Using the model You can create text with the model using this code: ```python from transformers import pipeline pipe = pipeline('text-generation', model="Michael711/feinschwarz", tokenizer="Michael711/feinschwarz") text = pipe("Der Sinn des Lebens ist es", max_length=100)[0]["generated_text"] pri... | af07415404717b09ca28920327b348e6 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-xsum-finetuned-billsum This model is a fine-tuned version of [Frederick0291/t5-small-finetuned-xsum](https://huggingface.co/Frederick0291/t5-small-finetuned-xsum) on an unknown dataset. | e035c79519070c7b1f1184a85708339d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 330 | 1.8540 | 32.9258 | 14.9104 | 27.1067 | 27.208 | 18... | 4e0416782e926de1935947026c317656 |
mit | ['bart', 'pytorch'] | false | BART-IT - Il Post BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be fine-tuned on a variety of tasks. | d0b7fe11e866d2f1bb02d0f52a5ac8c6 |
mit | ['bart', 'pytorch'] | false | Fine-tuning The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets: - [FanPage](https://huggingface.co/datasets/ARTeLab/fanpage) - finetuned model [here](https://huggingface.co/morenolq/bart-it-fanpage) - **This model** [IlPost](https://huggingface.co/datasets/ARTeLab/il... | d99507b1feaa2bc478121c24b216d15d |
mit | ['bart', 'pytorch'] | false | Usage In order to use the model, you can use the following code: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it-ilpost") model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it-ilpost") input_ids = tokenizer.encode("Il mod... | 8482f796b502cf7be5527ca6dbfc5cbe |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | wav2vec 2.0 with CTC/Attention trained on DVoice Darija (No LM) This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on a [DVoice](https://zenodo.org/record/6342622) Darija dataset within SpeechBrain. For a better experience, we encourage you to ... | 1ee381585426e04ab8a71dc7da6cce06 |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Transcribing your own audio files (in Darija) ```python from speechbrain.pretrained import EncoderASR asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-dvoice-darija", savedir="pretrained_models/asr-wav2vec2-dvoice-darija") asr_model.transcribe_file('speechbrain/asr-wav2vec2-dvoice-darija/example_da... | d5b74b4eb29192d05cee7631eb9d477e |
apache-2.0 | ['CTC', 'pytorch', 'speechbrain', 'Transformer'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/DVoice/ASR... | 70694c96a80b235d4953a0c7538a854e |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large Marathi This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1975 - Wer: 13.6440 | 1e4d0dcaa4538bc480049f1d98142f06 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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 - training_steps: 400 - mixed_precision... | d2dbdd6adc211365c7d1e8cccd1316d4 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1914 | 0.81 | 400 | 0.1975 | 13.6440 | | eb0f75ed1abd2e2b8dd3b1c3ac6715b5 |
mit | ['generated_from_trainer'] | false | DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset. It achieves the following results on the evaluation set: - Loss: 0.2922 - Accuracy: 0.9161 ... | ac7ab71f9cf1f3592b64dd057af23b1d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4147 | 1.0 | 650 | 0.3910 | 0.8832 | | 0.2975 | 2.0 | 1300 | 0.2922 | 0.9161 | | 0.2575 | 3.0 | 1950 | 0.3555 | 0.... | b805f765be94dca8e6c22cb3c304f578 |
apache-2.0 | ['translation'] | false | opus-mt-lv-fi * source languages: lv * target languages: fi * OPUS readme: [lv-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lv-fi/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://... | 5355f84d025922f803b897434fb894e6 |
mit | ['generated_from_trainer'] | false | farsi_lastname_classifier_4 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2337 - Accuracy: 0.96 | cb0e10b5a9b6aaffcb081497f9d085a8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision... | d99271a098a08b3e777a3ea168fb0aab |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 12 | 0.5673 | 0.836 | | No log | 2.0 | 24 | 0.4052 | 0.868 | | No log | 3.0 | 36 | 0.2211 | 0.... | 700dfa41e0c8a66408783cc2bcc0ac96 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provi... | ca57637d317985d93ece45bedc7b1108 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-6.9B has not been fine-tuned for down... | 8e4ceb4808096aae2559b48e0da669fb |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-6.9B to produce factually accurate output. This model was trained ... | 5939b5eba73a96f84a2e7db89e40c797 |
apache-2.0 | ['pytorch', 'causal-lm', 'pythia'] | false | Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. Common... | 5e83c61fe42aabbc448b3676cca475cd |
apache-2.0 | ['pytorch', 'text-generation', 'causal-lm', 'rwkv'] | false | Model Description RWKV-3 169M is a L12-D768 causal language model trained on the Pile. See https://github.com/BlinkDL/RWKV-LM for details. At this moment you have to use my Github code (https://github.com/BlinkDL/RWKV-v2-RNN-Pile) to run it. ctx_len = 768 n_layer = 12 n_embd = 768 Final checkpoint: RWKV-3-Pile-202... | 1f9ad56d898cb782e4d31a30858face7 |
apache-2.0 | ['generated_from_trainer'] | false | distilled-mt5-small-b0.01 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: 2.8163 - Bleu: 7.5421 - Gen Len: 44.4902 | 14cd1c8b7e56489ee9794accca2d0fec |
apache-2.0 | ['generated_from_trainer'] | false | xlsr-wav2vec2-3 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: 0.4201 - Wer: 0.3998 | 84245494d6346c1d03e49d79ff5dae94 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 9d88c360e54dd48ff3fb15d4b075bb69 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.0117 | 0.68 | 400 | 3.0284 | 0.9999 | | 2.6502 | 1.35 | 800 | 1.0868 | 0.9374 | | 0.9362 | 2.03 | 1200 | 0.5216 | 0.649... | 7f231df353f7ac9a832d796e99e63d1f |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1952 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7370 | 95af8ca71b368b973dd2bd41ddeee68d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 5 | 1.8526 | 0.0 | 0.0 | 0.0 | 0.7367 | | No log | 2.0 | 10 | 1... | 53bd09d72130e374fae2ac96dbe24f13 |
apache-2.0 | ['deep-narrow'] | false | T5-Efficient-LARGE-NH8-NL32 (Deep-Narrow version) T5-Efficient-LARGE-NH8-NL32 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* che... | 4752408c521c8b5cb5885050242d00a0 |
apache-2.0 | ['deep-narrow'] | false | Details model architecture This model checkpoint - **t5-efficient-large-nh8-nl32** - is of model type **Large** with the following variations: - **nh** is **8** - **nl** is **32** It has **771.34** million parameters and thus requires *ca.* **3085.35 MB** of memory in full precision (*fp32*) or **1542.68 MB** of m... | 3a7171cf5db5a731e69e0250e428b840 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the MlsEnglishSchoolBostonGnome concept trained by gavrenkov on the gavrenkov/MLSGnome dataset. This is a Stable Diffusion model fine-tuned on the MlsEnglishSchoolBostonGnome concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of MlsEnglishSchoolBostonGnome chara... | e9bb549412ed39cc0a990c3780515ccd |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('gavrenkov/MlsEnglishSchoolBostonGnome-character') image = pipeline().images[0] image ``` | d2275fe40cd63940ad3be4ff547f5078 |
mit | [] | false | model by KnightMichael This your the Stable Diffusion model fine-tuned the Yagami Taichi from Digimon Adventure (1999) concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **an anime boy character of sks** You can also train your own concepts and upload them to the li... | 552a9b7ff0278da643691a801b0d45ce |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0636 - Precision: 0.9330 - Recall: 0.9498 - F1: 0.9414 - Accuracy: 0.9861 | d5e7b65970ca68a45fc4e1b9b95ea40e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0901 | 1.0 | 1756 | 0.0696 | 0.9166 | 0.9325 | 0.9245 | 0.9815 | | 0.0366 | 2.0 |... | a6267109a5181f72e843e21ddda85d33 |
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