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 | ['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: 1000 - num_epochs: 4 - mixed_precision_training: Native AMP | 623a32d556dd8dfb18a6c546daff4dc8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.219 | 0.04 | 500 | 0.1976 | 0.1215 | | 0.0762 | 0.08 | 1000 | 0.2818 | 0.1324 | | 0.0824 | 0.12 | 1500 | 0.4541 | 0.1602 | | 0.0807 | 0.15 | 2000 | 0.1556 | 0.1162 | | 0.0799 | 0.19 | 2500 | 0.1618 | 0.1164 | | 0.0826 | 0.23 | 3000 | 0.3510 | 0.1379 | | 0.0809 | 0.27 | 3500 | 0.1486 | 0.1182 | | 0.0854 | 0.31 | 4000 | 0.1267 | 0.1177 | | 0.0817 | 0.35 | 4500 | 0.1581 | 0.1218 | | 0.0835 | 0.38 | 5000 | 0.1670 | 0.1251 | | 0.0841 | 0.42 | 5500 | 0.1576 | 0.1179 | | 0.0798 | 0.46 | 6000 | 0.2201 | 0.1300 | | 0.083 | 0.5 | 6500 | 0.1165 | 0.1179 | | 0.0878 | 0.54 | 7000 | 0.2640 | 0.1430 | | 0.0811 | 0.58 | 7500 | 0.1585 | 0.1288 | | 0.083 | 0.62 | 8000 | 0.3127 | 0.1370 | | 0.083 | 0.65 | 8500 | 0.4790 | 0.1449 | | 0.0775 | 0.69 | 9000 | 0.1651 | 0.1163 | | 0.0787 | 0.73 | 9500 | 1.6426 | 0.2083 | | 0.0781 | 0.77 | 10000 | 0.2307 | 0.1324 | | 0.0827 | 0.81 | 10500 | 0.1765 | 0.1318 | | 0.0816 | 0.85 | 11000 | 0.1679 | 0.1201 | | 0.0797 | 0.88 | 11500 | 0.2506 | 0.1508 | | 0.0813 | 0.92 | 12000 | 0.1893 | 0.1239 | | 0.0758 | 0.96 | 12500 | 0.1266 | 0.1147 | | 0.091 | 1.0 | 13000 | 0.1606 | 0.1180 | | 0.0677 | 1.04 | 13500 | 0.1107 | 0.1118 | | 0.0733 | 1.08 | 14000 | 0.1734 | 0.1565 | | 0.072 | 1.12 | 14500 | 0.1141 | 0.1126 | | 0.0731 | 1.15 | 15000 | 0.1125 | 0.1112 | | 0.0793 | 1.19 | 15500 | 0.1818 | 0.1146 | | 0.07 | 1.23 | 16000 | 0.2678 | 0.1265 | | 0.0658 | 1.27 | 16500 | 0.2909 | 0.1203 | | 0.0678 | 1.31 | 17000 | 0.3241 | 0.1280 | | 0.0681 | 1.35 | 17500 | 0.3243 | 0.1497 | | 0.0666 | 1.38 | 18000 | 0.2056 | 0.1150 | | 0.0667 | 1.42 | 18500 | 0.4678 | 0.1252 | | 0.0656 | 1.46 | 19000 | 0.1603 | 0.1138 | | 0.0662 | 1.5 | 19500 | 0.1554 | 0.1115 | | 0.0669 | 1.54 | 20000 | 0.1215 | 0.1101 | | 0.0681 | 1.58 | 20500 | 0.1118 | 0.1083 | | 0.0708 | 1.62 | 21000 | 0.1743 | 0.1146 | | 0.0673 | 1.65 | 21500 | 0.1509 | 0.1109 | | 0.0667 | 1.69 | 22000 | 0.3411 | 0.1495 | | 0.065 | 1.73 | 22500 | 0.1045 | 0.1067 | | 0.0644 | 1.77 | 23000 | 0.0999 | 0.1075 | | 0.0643 | 1.81 | 23500 | 0.1019 | 0.1073 | | 0.0675 | 1.85 | 24000 | 0.1196 | 0.1073 | | 0.0618 | 1.88 | 24500 | 0.1092 | 0.1086 | | 0.0626 | 1.92 | 25000 | 0.1256 | 0.1070 | | 0.0635 | 1.96 | 25500 | 0.1183 | 0.1069 | | 0.0621 | 2.0 | 26000 | 0.1180 | 0.1091 | | 0.0548 | 2.04 | 26500 | 0.1199 | 0.1048 | | 0.0548 | 2.08 | 27000 | 0.1215 | 0.1057 | | 0.0531 | 2.12 | 27500 | 0.1086 | 0.1036 | | 0.0548 | 2.15 | 28000 | 0.1103 | 0.1043 | | 0.054 | 2.19 | 28500 | 0.1078 | 0.1048 | | 0.0521 | 2.23 | 29000 | 0.1094 | 0.1039 | | 0.0534 | 2.27 | 29500 | 0.1058 | 0.1037 | | 0.0539 | 2.31 | 30000 | 0.1035 | 0.1026 | | 0.0516 | 2.35 | 30500 | 0.1009 | 0.1027 | | 0.0525 | 2.38 | 31000 | 0.1292 | 0.1056 | | 0.0501 | 2.42 | 31500 | 0.1124 | 0.1033 | | 0.052 | 2.46 | 32000 | 0.1020 | 0.1028 | | 0.0519 | 2.5 | 32500 | 0.1131 | 0.1038 | | 0.0498 | 2.54 | 33000 | 0.1036 | 0.1031 | | 0.0525 | 2.58 | 33500 | 0.0994 | 0.1005 | | 0.0506 | 2.61 | 34000 | 0.1093 | 0.1015 | | 0.0484 | 2.65 | 34500 | 0.1048 | 0.1005 | | 0.0493 | 2.69 | 35000 | 0.1192 | 0.1028 | | 0.048 | 2.73 | 35500 | 0.1208 | 0.1020 | | 0.0473 | 2.77 | 36000 | 0.1410 | 0.1042 | | 0.0472 | 2.81 | 36500 | 0.1382 | 0.1052 | | 0.0467 | 2.85 | 37000 | 0.1118 | 0.1012 | | 0.0473 | 2.88 | 37500 | 0.1032 | 0.1002 | | 0.0466 | 2.92 | 38000 | 0.1041 | 0.1004 | | 0.0455 | 2.96 | 38500 | 0.1056 | 0.1004 | | 0.0483 | 3.0 | 39000 | 0.1091 | 0.0995 | | 0.0408 | 3.04 | 39500 | 0.1170 | 0.1012 | | 0.0395 | 3.08 | 40000 | 0.1106 | 0.0995 | | 0.0407 | 3.11 | 40500 | 0.1075 | 0.0998 | | 0.0403 | 3.15 | 41000 | 0.1129 | 0.1000 | | 0.0397 | 3.19 | 41500 | 0.1062 | 0.0993 | | 0.0389 | 3.23 | 42000 | 0.1072 | 0.0990 | | 0.0385 | 3.27 | 42500 | 0.1032 | 0.0985 | | 0.0389 | 3.31 | 43000 | 0.0989 | 0.0973 | | 0.0404 | 3.35 | 43500 | 0.1031 | 0.0973 | | 0.0387 | 3.38 | 44000 | 0.0998 | 0.0974 | | 0.0391 | 3.42 | 44500 | 0.1000 | 0.0969 | | 0.0387 | 3.46 | 45000 | 0.0982 | 0.0968 | | 0.0407 | 3.5 | 45500 | 0.1057 | 0.0979 | | 0.038 | 3.54 | 46000 | 0.1026 | 0.0974 | | 0.0399 | 3.58 | 46500 | 0.1020 | 0.0970 | | 0.0387 | 3.61 | 47000 | 0.1022 | 0.0968 | | 0.0379 | 3.65 | 47500 | 0.1016 | 0.0961 | | 0.0369 | 3.69 | 48000 | 0.1012 | 0.0957 | | 0.0372 | 3.73 | 48500 | 0.0993 | 0.0956 | | 0.0361 | 3.77 | 49000 | 0.1013 | 0.0951 | | 0.0366 | 3.81 | 49500 | 0.1020 | 0.0956 | | 0.0377 | 3.85 | 50000 | 0.1014 | 0.0961 | | 0.0363 | 3.88 | 50500 | 0.1019 | 0.0962 | | 0.0368 | 3.92 | 51000 | 0.1033 | 0.0963 | | 0.0381 | 3.96 | 51500 | 0.1026 | 0.0960 | | 0.0364 | 4.0 | 52000 | 0.1024 | 0.0959 | | e035e4a0ebfc8750ea0c92c1dbfb8cd2 |
mit | ['generated_from_trainer'] | false | finetuned_gpt2_sst2_negation0.0005_pretrainedTrue This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the sst2 dataset. It achieves the following results on the evaluation set: - Loss: 3.5276 | b64e09981e57a59761bb05dc40fcd204 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1086 | 1.0 | 1059 | 3.5051 | | 2.9257 | 2.0 | 2118 | 3.5195 | | 2.833 | 3.0 | 3177 | 3.5276 | | ca063b19b697230962b1374e7f60d822 |
apache-2.0 | ['generated_from_trainer'] | false | Team-Gryffindor-DistilBERT-finetuned-ner-creditcardcontract 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: - eval_loss: 0.0231 - eval_precision: 0.7448 - eval_recall: 0.75 - eval_f1: 0.7474 - eval_accuracy: 0.9942 - eval_runtime: 61.7618 - eval_samples_per_second: 27.201 - eval_steps_per_second: 3.4 - epoch: 3.0 - step: 5670 | 611a850bd2214f3a10cab48aefd1c043 |
openrail | [] | false | <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> <img src = 'https://images.unsplash.com/photo-1592564630984-7410f94db184?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1146&q=80'> | a183466c5b4a2dca7a3e639e968f816c |
openrail | [] | false | Description DialogGPT is a variant of the GPT (Generative Pretrained Transformer) language model developed by OpenAI. It's a deep neural network-based language model that's trained on massive amounts of text data to generate human-like text. DialogGPT uses the transformer architecture, which is a type of neural network designed for processing sequential data such as language. During the training phase, the model is exposed to a large corpus of text and learns to predict the next word in a sequence given the previous words. In the context of dialog, DialogGPT is trained to predict the response in a conversation, given the context of the conversation. This context can include one or more turns of the conversation, along with any additional information such as the topic of the conversation or the speaker's personality. At inference time, the model takes the current context of the conversation as input and generates a response. The response is generated by sampling from the model's predicted distribution over the vocabulary. Overall, DialogGPT provides a flexible and powerful solution for generating human-like text in a conversational context, allowing for the creation of a wide range of applications such as chatbots, conversational agents, and virtual assistants | 01c095294331d5fed9fbac5eb9c850a1 |
openrail | [] | false | Parameters Model was trained for 40 epochs, using params as follows. ``` per_gpu_train_batch_size: int = 2 self.per_gpu_eval_batch_size: int = 2 self.gradient_accumulation_steps: int = 1 self.learning_rate: float = 5e-5 self.weight_decay: float = 0.0 self.adam_epsilon: float = 1e-8 self.max_grad_norm: int = 1.0 self.num_train_epochs: int = 40 self.max_steps: int = -1 self.warmup_steps: int = 0 self.logging_steps: int = 1000 self.save_steps: int = 3500 self.save_total_limit = None self.eval_all_checkpoints: bool = False self.no_cuda: bool = False self.overwrite_output_dir: bool = True self.overwrite_cache: bool = True self.should_continue: bool = False self.seed: int = 42 self.local_rank: int = -1 self.fp16: bool = False self.fp16_opt_level: str = 'O1' ``` | e932d047b26b27e75260354d67d07e8a |
openrail | [] | false | Usage DialoGPT large version, finetuned on Morty's sequences (Rick and Morty Cartoon character). Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-small-morty') model = AutoModelWithLMHead.from_pretrained('s3nh/DialoGPT-small-morty') for step in range(4): new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) print("MortyBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) | 8a3939237908285ab8f0671c636cdb5e |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_One_500v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_one500v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.2804 - Precision: 0.6656 - Recall: 0.6225 - F1: 0.6433 - Accuracy: 0.9187 | 70a95f6ddfb94103baccd50437b0746a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 183 | 0.2784 | 0.5897 | 0.5076 | 0.5456 | 0.9064 | | No log | 2.0 | 366 | 0.2816 | 0.6535 | 0.5787 | 0.6138 | 0.9112 | | 0.1091 | 3.0 | 549 | 0.2804 | 0.6656 | 0.6225 | 0.6433 | 0.9187 | | 339feccdb010786588e958a27171853c |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | S-PubMedBert-MedQuAD This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> | b168d87950d18388ec2e772a1b729a05 |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('TimKond/S-PubMedBert-MedQuAD') embeddings = model.encode(sentences) print(embeddings) ``` | 72be17771ce86570a90b1d71698a8993 |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) | 637d7556202f5b92aadff3a66039aa0e |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.DataLoader` of length 82590 with parameters: ``` {'batch_size': 2, 'shuffle':True} ``` **Loss**: `sentence_transformers.losses.SoftmaxLoss` with parameters: ``` {'num_labels': 2, 'sentence_embedding_dimension': '768'} ``` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 1, "evaluation_steps": 0, "evaluator": None, "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 8259, "weight_decay": 0.01 } ``` | 53753622763d68b5806b4481ad148621 |
mit | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` | 91f00884ecad92ddab12384ce1af6aca |
apache-2.0 | ['generated_from_trainer'] | false | sagemaker-distilbert-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: 0.2322 - Accuracy: 0.921 | 689e9c90497dc06f002e14c7a292b983 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9306 | 1.0 | 500 | 0.2322 | 0.921 | | 8756992d117f298eaf885f5ed7284620 |
mit | [] | false | bada club on Stable Diffusion This is the `<bada-club>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:     | be8526b7e63824d4c2463a1d84b44146 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | blue_pencil <strong>blue_pencil</strong> ใฏใๆงใ
ใชใขใใซใ้ฉๅฝใช้
ๅใงใใผใธใใใขใใซใงใใ ๆๅใชใขใใซใใใใคใๆใๆตฎใในใฆใใ ใใใ ใใชใใๆใๆตฎใในใใขใใซใฏใๆใใใใฎใขใใซใซๅซใพใใฆใใพใใ ใใฎใใผใธใขใใซใฎ็นๅพดใฏใใใใพใใใ ใใใใใชใขใใซใใใผใธใใฆใฟใใใจใ็ฎ็ใชใฎใงใ่ณชใ้ซใใใใพใใใ ๅ
จใฆใฎใขใใซใฏ [stable-diffusion-webui-model-tookit](https://github.com/arenatemp/stable-diffusion-webui-model-toolkit) ใ็จใใฆ `fp16` ใซใใฆใใพใใ --- | f7836091ae16474027541055cb426d3d |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | `blue_pencil-v1b` <small>(`@20230212`)</small> `blue_pencil-v1` ใฎ [Amalgam_Mix](https://civitai.com/models/4758/amalgammix) ใฎไปฃใใใซ [Balor-V2](https://huggingface.co/ploughB660/Balor-V2) ใ้ๅฑคใใผใธใใใขใใซใงใ v1 ใจใฏใกใใฃใจๅพๅใ้ใใพใ | 3bc700a9a3c8ce4d6285599c807c45b1 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ๅบๅไพ ``` girl, tokyo, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 205537258 Size: 768x768, Clip skip: 2 Denoising strength: 0.65, Hires upscale: 2, Hires upscaler: Latent (nearest-exact) ```  --- | 7dddd0c12a377a3c4796a856174be343 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | `blue_pencil-v1` <small>(`@20230211`)</small> ไปฅไธใฎใขใใซใๅซใพใใฆใใพใ๏ผ้ ไธๅ๏ผ <details> * [Defmix-v1.1](https://huggingface.co/Defpoint/Defmix-v1.0) * Counterfeit v1.0 * Counterfeit v2.0 * Basil Mix * Anything v4.0 * [PastelRainier](https://huggingface.co/Hemlok/RainierMix) * ACertainThing * Anything-V4.5 * Counterfeit-V2.0 * Evt_V4-preview * basil_mix * pastel-mix * [GingerMixR](https://huggingface.co/Hemlok/GingerMix) * LimeMixV2 * [Elysium_Anime_V3](https://huggingface.co/hesw23168/SD-Elysium-Model) * [SukiyakiMix-v1.0](https://huggingface.co/Vsukiyaki/SukiyakiMix-v1.0) * pastel-mix * AbyssOrangeMix2 * [HD-20](https://www.cognitionai.org/hdhowtogetstarted) * [7th_anime_v3_testA](https://huggingface.co/syaimu/7th_test) * [AniReal](https://huggingface.co/Hosioka/AniReal) * [TriPhaze_B](https://huggingface.co/Lucetepolis/TriPhaze) * ultracolor.v4 * Counterfeit-V2.5 * Treebark * [Nabylon-v1.2](https://huggingface.co/NegiInNattoMaki/Nabylon-v1.0) * AbyssOrangeMix2 * LonganMix * and more * [atwcustom_V4](https://huggingface.co/atsuwo/ATW-custom) * [Amalgam_Mix](https://civitai.com/models/4758/amalgammix) </details> | 3a87c074f84c5fea2e3ad0515167debd |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 1 ``` girl, tokyo, scenery Negative prompt: EasyNegative Steps: 30, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 2526423076 Size: 768x768, Clip skip: 2 ```  | 101f1ba4e507bc5aba7e91a2f1a2f160 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 2 ``` girl, early teen, kimono, sakura, particles Negative prompt: EasyNegative Steps: 20, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 4036639388, Size: 512x768, Clip skip: 2 ```  | f49606432c22b6e69e813229c496acda |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 3 ``` girl, early teen, t-shirt, pants, from behind, landscape, scenery, apocalyptic Negative prompt: EasyNegative Steps: 40, Sampler: DPM++ SDE Karras, CFG scale: 7.5, Seed: 748447692, Size: 768x512, Clip skip: 2 ```  | d9bffd537a5688aadae35d77a7aeb5ee |
mit | ['generated_from_trainer'] | false | xlnet-base-cased_fold_4_binary_v1 This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5724 - F1: 0.8315 | 8f0da8cf4b1b8ca8df70685c99b3c681 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 289 | 0.4043 | 0.8009 | | 0.4373 | 2.0 | 578 | 0.4093 | 0.8260 | | 0.4373 | 3.0 | 867 | 0.5084 | 0.8206 | | 0.2707 | 4.0 | 1156 | 0.5945 | 0.8087 | | 0.2707 | 5.0 | 1445 | 0.6389 | 0.8251 | | 0.1691 | 6.0 | 1734 | 0.8131 | 0.8156 | | 0.1012 | 7.0 | 2023 | 0.9865 | 0.8190 | | 0.1012 | 8.0 | 2312 | 1.1356 | 0.8342 | | 0.0506 | 9.0 | 2601 | 1.0624 | 0.8369 | | 0.0506 | 10.0 | 2890 | 1.2604 | 0.8255 | | 0.0384 | 11.0 | 3179 | 1.2648 | 0.8183 | | 0.0384 | 12.0 | 3468 | 1.3763 | 0.8158 | | 0.0318 | 13.0 | 3757 | 1.4966 | 0.8217 | | 0.0221 | 14.0 | 4046 | 1.3889 | 0.8250 | | 0.0221 | 15.0 | 4335 | 1.4014 | 0.8284 | | 0.0145 | 16.0 | 4624 | 1.5321 | 0.8289 | | 0.0145 | 17.0 | 4913 | 1.4914 | 0.8233 | | 0.0172 | 18.0 | 5202 | 1.3946 | 0.8314 | | 0.0172 | 19.0 | 5491 | 1.5032 | 0.8269 | | 0.0135 | 20.0 | 5780 | 1.5111 | 0.8328 | | 0.0087 | 21.0 | 6069 | 1.4899 | 0.8318 | | 0.0087 | 22.0 | 6358 | 1.5562 | 0.8311 | | 0.0061 | 23.0 | 6647 | 1.5384 | 0.8327 | | 0.0061 | 24.0 | 6936 | 1.5798 | 0.8304 | | 0.0052 | 25.0 | 7225 | 1.5724 | 0.8315 | | 757cd8170d3b1d9178bcc8f4ab5eb3fd |
creativeml-openrail-m | [] | false | `Broken mirror, shattered mirror, brokenM_style` this style gives a shattered mirror / reflection to prompts. License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here | 5b4660b6a9b53c0a0963bbb6641b1cc2 |
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: 0.2202 - Accuracy: 0.923 - F1: 0.9232 | b272cb1bb2105806099470372eb27228 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8244 | 1.0 | 250 | 0.3104 | 0.9025 | 0.8997 | | 0.2478 | 2.0 | 500 | 0.2202 | 0.923 | 0.9232 | | 0a9652a698b41cf14597129f7d182df9 |
mit | [] | false | Simple usage sample code ```python !pip install tokenizers==0.10.3 transformers==4.8.0 from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry") model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-gpt_neo-xl-poetry", pad_token_id=tokenizer.eos_token_id) prompt_text = "ืื ื ืืืื ืฉืืงืืื ืืขืืืืช" max_len = 512 sample_output_num = 3 seed = 1000 import numpy as np import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = 0 if torch.cuda.is_available()==False else torch.cuda.device_count() print(f"device: {device}, n_gpu: {n_gpu}") np.random.seed(seed) torch.manual_seed(seed) if n_gpu > 0: torch.cuda.manual_seed_all(seed) model.to(device) encoded_prompt = tokenizer.encode( prompt_text, add_special_tokens=False, return_tensors="pt") encoded_prompt = encoded_prompt.to(device) if encoded_prompt.size()[-1] == 0: input_ids = None else: input_ids = encoded_prompt print("input_ids = " + str(input_ids)) if input_ids != None: max_len += len(encoded_prompt[0]) if max_len > 2048: max_len = 2048 print("Updated max_len = " + str(max_len)) stop_token = "<|endoftext|>" new_lines = "\n\n\n" sample_outputs = model.generate( input_ids, do_sample=True, max_length=max_len, top_k=50, top_p=0.95, num_return_sequences=sample_output_num ) print(100 * '-' + "\n\t\tOutput\n" + 100 * '-') for i, sample_output in enumerate(sample_outputs): text = tokenizer.decode(sample_output, skip_special_tokens=True) | 996281a6c53217bdc9cd291d370a7b4b |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr0_2e-05_editorials_27_02_2022-19_38_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0914 - Accuracy: 0.9746 - F1: 0.9870 | 6a22f46cef25fba7c56390f42efaae6e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 104 | 0.0501 | 0.9828 | 0.9913 | | No log | 2.0 | 208 | 0.0435 | 0.9828 | 0.9913 | | No log | 3.0 | 312 | 0.0414 | 0.9828 | 0.9913 | | No log | 4.0 | 416 | 0.0424 | 0.9799 | 0.9898 | | 0.0547 | 5.0 | 520 | 0.0482 | 0.9828 | 0.9913 | | 1d8f6767935bac476ff77daa4f0dd5eb |
apache-2.0 | ['generated_from_trainer'] | false | wnli This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6898 - Accuracy: 0.5634 | d01f269e47b3cf3fe9f54273e96590d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | 57936ab9e82a796ee992714a365befec |
apache-2.0 | ['generated_from_trainer'] | false | test-trainer This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2394 - Accuracy: 0.9395 - F1: 0.9396 | 5bcbc1b6aa7a4d6559f6901ac80d702d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2518 | 1.0 | 2000 | 0.1971 | 0.931 | 0.9305 | | 0.1678 | 2.0 | 4000 | 0.1782 | 0.9405 | 0.9406 | | 0.1048 | 3.0 | 6000 | 0.2394 | 0.9395 | 0.9396 | | 4db33e44b4a9fe4e60092bff4c1511e0 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for maxvit_xlarge_tf_384.in21k_ft_in1k An official MaxViT image classification model. Pretrained in tensorflow on ImageNet-21k (21843 Google specific instance of ImageNet-22k) and fine-tuned on ImageNet-1k by paper authors. Ported from official Tensorflow implementation (https://github.com/google-research/maxvit) to PyTorch by Ross Wightman. | 7a1abfc9cf7f81c2519e40d65b93ac37 |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 475.3 - GMACs: 292.8 - Activations (M): 668.8 - Image size: 384 x 384 - **Papers:** - MaxViT: Multi-Axis Vision Transformer: https://arxiv.org/abs/2204.01697 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k | b55bffe3b5487ae6a8a42e086add3a11 |
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('maxvit_xlarge_tf_384.in21k_ft_in1k', pretrained=True) model = model.eval() | 1484ffa63254913172585d26ee38e6d7 |
apache-2.0 | ['image-classification', 'timm'] | false | Feature Map Extraction ```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( 'maxvit_xlarge_tf_384.in21k_ft_in1k', pretrained=True, features_only=True, ) model = model.eval() | 7ae7749fe8fb6415065d63ff0a55dc55 |
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( 'maxvit_xlarge_tf_384.in21k_ft_in1k', pretrained=True, num_classes=0, | 3be0af3f6bc732157c9e566cd5126245 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | hubert-base-superb-ks This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0848 - Accuracy: 0.9822 | d38ccb8ca30c71780b944708df8daf6a |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 0 - distributed_type: IPU - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 - training precision: Mixed Precision | bd5ba1083106ba0da081a09f2ce5aeb2 |
apache-2.0 | ['translation'] | false | opus-mt-fr-ht * source languages: fr * target languages: ht * OPUS readme: [fr-ht](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-ht/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/fr-ht/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ht/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fr-ht/opus-2020-01-09.eval.txt) | cd8ebf1cfa22c4be49c8a22e0bd94cd1 |
apache-2.0 | ['translation'] | false | opus-mt-fi-fj * source languages: fi * target languages: fj * OPUS readme: [fi-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-fj/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](https://object.pouta.csc.fi/OPUS-MT-models/fi-fj/opus-2020-01-20.zip) * test set translations: [opus-2020-01-20.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-fj/opus-2020-01-20.test.txt) * test set scores: [opus-2020-01-20.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/fi-fj/opus-2020-01-20.eval.txt) | 5bfb28abd94813ea6196449bd30f1a41 |
apache-2.0 | ['generated_from_trainer'] | false | Article_50v3_NER_Model_3Epochs_UNAUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the article50v3_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.7382 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7789 | 6e9a23c4821e1e2a1db0de4354e3e1e3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 6 | 0.9648 | 0.1172 | 0.0042 | 0.0081 | 0.7782 | | No log | 2.0 | 12 | 0.7740 | 0.0 | 0.0 | 0.0 | 0.7789 | | No log | 3.0 | 18 | 0.7382 | 0.0 | 0.0 | 0.0 | 0.7789 | | 7131a703d739427d93a30fe5d25d64f1 |
mit | ['generated_from_trainer'] | false | debert_base_fine_tuned_sent140 This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9678 - Accuracy: 0.7647 | 14640351cc2ae502db0cafa20ef8632d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 408 | 0.8139 | 0.7219 | | 0.8198 | 2.0 | 816 | 0.7742 | 0.7460 | | 0.4479 | 3.0 | 1224 | 0.9678 | 0.7647 | | b870ef2e2ba3eb4d6375f8d178f546d1 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Paint Journey V2 is [V1](https://huggingface.co/FredZhang7/paint-journey-v1) fine-tuned on 768x768 oil paintings by Midjourney V4, Open Journey V2, Disco Diffusion, and artists given permission Begin the prompt with **((oil painting))** to add the oil paint effect. For digital and other painting styles, use similar prompts as you would for Midjourney V4 (with some tweaks), Stable Diffusion v1.5 (add more styles), Open Journey V2, or Disco Diffusion. [](https://colab.research.google.com/github/AMLA-UBC/100-Exploring-the-World-of-Modern-Machine-Learning/blob/main/assets/PaintJourneyV2.ipynb) | 13f8865dc8396ca9b00c4032f769b776 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Examples *All examples were generated using Camenduru's WebUI (see the Colab file)*  *โฌ๏ธ 768x1136 portraits, generated using descriptive prompts and without face restoration, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/character_settings.txt)*  *โฌ๏ธ 1280x768 (mostly) natural landscapes, used shorter prompts, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/nature_settings.txt)*  *โฌ๏ธ 1152x768 outerspace landscapes, used descriptive prompts, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/outerspace_settings.txt)*  *โฌ๏ธ 1280x768 lamborghini, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/lamborghini_settings.txt)*  *โฌ๏ธ 960x768 Eevee, [generation parameters](https://huggingface.co/FredZhang7/paint-journey-v2/raw/main/assets/eevee_settings.txt)* | dee03dc30b6c954e98b7024492568cb7 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Comparisons Paint Journey V2's paintings are closer to human-drawn art than Open Journey V2. Compared to models like Dreamlike Diffusion 1.0, PJ V2 tends to generate 768x768 or higher resolution images with reduced noise levels. This model is also capable of generating stunning portraits at 768x1136 resolution without duplicated faces (with [Camenduru's WebUI](https://github.com/camenduru/stable-diffusion-webui)), a difficult task to models like DreamShaper 3.3. At lower resolutions, DreamShaper 3.3 tends to generate higher quality portraits than PJ V2 in terms of noise levels, given the same (short) postive and negative prompts. However, PJ V2 can craft more stunning masterpieces with more descriptive positive and negative prompts and can still generate beautiful landscapes with shorter prompts. | 55ef349034969d47bbf6c8355df5c0c5 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Training Instead of solely fine-tuning its Unet, Paint Journey V2 focuses on fine-tuning its text encoder with a diverse range of prompts. This allows for a seamless blend of the digital and oil painting styles into various other types of prompts, resulting in a more natural and dynamic output. This model was trained on a curated dataset of roughly 300 images hand-picked from Midjourney, [Prompt Hero](https://prompthero.com/), [PixaBay](https://pixabay.com/images/search/paintings/), Open Journey V2, and Reddit. Before training, I used R-ESRGAN 4x on many images to increase their resolution and reduce noise. | a6725a1781892429f17a13d7e11c4684 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Running out of prompts? Useful resources: [Lexica.art](https://lexica.art/), [Fast GPT PromptGen](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2), [Prompt Hero](https://prompthero.com/) | 653b95d062c87a56b497769b00595c3c |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Output Dimensions Portrait sizes include, but are not limited to, `512x768`, `768x768`, and `768x1136`. Landscape sizes include, but are not limited to, `768x512`, `768x768`, `1152x768`, and `1280x768`. | c6a2f9c79ae48e5ba8fdc24e91a5d7bf |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Camenduru's WebUI ``` git clone -b v1.6 https://github.com/camenduru/stable-diffusion-webui ``` <details> <summary> Click to use Automatic1111's Webui instead, but may not output images as artistic </summary> ``` git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git ``` </details> Download [checkpoint](./paint_journey_v2.ckpt) and [vae](./paint_journey_v2.vae.pt) to the `./stable-diffusion-webui/models/Stable-diffusion` folder. Run `webui-user.bat`. | 6c1079bbe483ce5a5caff1460d33be39 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | ๐งจ Diffusers *Tip: using double, tripple, or quadriple brackets around some letters WORD (e.g. "((WORD))") will put an 'emphasis' on WORD* ```bash pip install --upgrade diffusers transformers ``` ```python | dcd8656532317f02d25cd00fae5e4316 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | changing-the-scheduler from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler import torch, random, datetime pipe = StableDiffusionPipeline.from_pretrained("FredZhang7/paint-journey-v2") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") def random_seed(): return random.randint(0, 2**32 - 1) prompt = "((oil painting)), gentle waves, bright blue sky, white sails billowing, sun glistening on the surface, salty sea air, distant horizon, calm breeze, birds soaring overhead, vibrant colors, artstation digital painting, high resolution, uhd, 4 k, 8k wallpaper" | 617a9fb77f41a05396bc6f446302d7dc |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | sampling steps, 30 to 40 is usually good for Euler Ancestral generator = torch.Generator("cuda").manual_seed(seed) with torch.autocast("cuda"): image = pipe(prompt=prompt, num_inference_steps=num_inference_steps, width=width, height=height, generator=generator, guidance_scale=cfg_scale).images[0] def generate_filename(string, seed): invalid_chars = ["<", ">", ":", '"', "/", "\\", "|", "?", "*"] for char in invalid_chars: string = string.replace(char, "") return f"{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}_{seed}_{string}" image.save(f"./{generate_filename(prompt, seed)}.png") ``` | 29bf04f05f42dbe0ef8b006d5539dd70 |
creativeml-openrail-m | ['text-to-image', 'midjourney', 'stable-diffusion', 'disco-diffusion', 'art', 'arxiv:2208.12242'] | false | Safety Checker V2 The official [stable diffusion safety checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker) uses up 1.22GB VRAM. I recommend using [Google Safesearch Mini V2](https://huggingface.co/FredZhang7/google-safesearch-mini-v2) (220MB) to save 1.0GB VRAM. | f4fc71b9db5790958ce467237879fcb5 |
apache-2.0 | ['generated_from_trainer'] | false | This time use self-made dataset(cut the audio of "https://www.youtube.com/watch?v=a2ZOTD3R7JI" into slices and write the corresponding transcript, totally 4 mins), don't know why the word-error-rate keep 1. But can know that much be the problem of dataset, because last time use the same pre-trained model and standard singlish corpus fine-tune get nice result. (can find it at:RuiqianLi/wav2vec2-large-xls-r-300m-singlish-colab) It achieves the following results on the evaluation set: - Loss: 3.0927 - Wer: 1.0 | e0a5c167b19919006dd01398734c404d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP | e5ca6b0a7f47a40f94b5f11782696e66 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 3.7943 | 20.0 | 200 | 3.0597 | 1.0 | | 2.9902 | 40.0 | 400 | 3.1604 | 1.0 | | 2.9696 | 60.0 | 600 | 3.1112 | 1.0 | | 2.8885 | 80.0 | 800 | 3.0234 | 1.0 | | 2.8154 | 100.0 | 1000 | 3.0927 | 1.0 | | 3d6290353f88e16f658e0b3c10699e05 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_accent_us-5_england-5_s203 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 8e12c627b77a32ade7b11f8d3fd903bd |
apache-2.0 | ['generated_from_trainer'] | false | mrpc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE MRPC dataset. It achieves the following results on the evaluation set: - Loss: 0.5611 - Accuracy: 0.6912 - F1: 0.8158 - Combined Score: 0.7535 | 525d76f253e0b88f9c1d681d563a4cf6 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 | 0229b13918e1522ed934374b55d28c96 |
mit | ['generated_from_trainer'] | false | roberta_reman This model is a fine-tuned version of [ibm/ColD-Fusion](https://huggingface.co/ibm/ColD-Fusion) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4272 - F1: 0.7004 - Roc Auc: 0.7862 - Accuracy: 0.4330 - Recall: 0.6831 - Precision: 0.7185 | 39de9edd4cbabedbcde0945706e1a365 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Recall | Precision | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------:|:---------:| | No log | 1.0 | 113 | 0.4673 | 0.5668 | 0.6955 | 0.2990 | 0.4930 | 0.6667 | | No log | 2.0 | 226 | 0.4187 | 0.6397 | 0.7403 | 0.3918 | 0.5563 | 0.7524 | | No log | 3.0 | 339 | 0.4272 | 0.7004 | 0.7862 | 0.4330 | 0.6831 | 0.7185 | | No log | 4.0 | 452 | 0.4191 | 0.6566 | 0.7539 | 0.3918 | 0.6127 | 0.7073 | | 0.3529 | 5.0 | 565 | 0.4246 | 0.6788 | 0.7706 | 0.4124 | 0.6549 | 0.7045 | | 8b6cb1bc294d3e36603cffab6bd1daad |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | MultiBERTs Seed 2 Checkpoint 1700k (uncased) Seed 2 intermediate checkpoint 1700k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in [this repository](https://github.com/google-research/language/tree/master/language/multiberts). This is an intermediate checkpoint. The final checkpoint can be found at [multiberts-seed-2](https://hf.co/multberts-seed-2). This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing MultiBERTs did not write a model card for this model so this model card has been written by [gchhablani](https://hf.co/gchhablani). | 6de0da4078532a36eddfed8c42d7abf7 |
apache-2.0 | ['exbert', 'multiberts', 'multiberts-seed-2'] | false | How to use Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('multiberts-seed-2-1700k') model = BertModel.from_pretrained("multiberts-seed-2-1700k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | 541a892fa0777518f673fe5901ed4205 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Overview <details> <summary>Click to expand</summary> - **Model type:** Language Model - **Architecture:** RoBERTa-large - **Language:** English - **License:** Apache 2.0 - **Task:** Zero-Shot Text Classification - **Data:** Microsoft Academic Graph - **Additional Resources:** - [Paper]() <-- WiP (soon to be published in EACL 2023) - [GitHub](https://github.com/TeMU-BSC/sciroshot) </details> | 134a368decc494202e6b27a158ef93c9 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Model description SCIroShot is an entailment-based Zero-Shot Text Classification model that has been fine-tuned using a self-made dataset composed of scientific articles from [Microsoft Academic Graph](https://www.microsoft.com/en-us/research/project/microsoft-academic-graph/) (MAG). The resulting model achieves SOTA performance in the scientific domain and very competitive results in other areas. | 3d070227fa125a88a76e3f5309f948c4 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | How to use ```python from transformers import pipeline zstc = pipeline("zero-shot-classification", model="BSC-LT/sciroshot") sentence = "Leo Messi is the best player ever." candidate_labels = ["politics", "science", "sports", "environment"] template = "This example is {}" output = zstc(sentence, candidate_labels, hypothesis_template=template, multi_label=False) print(output) print(f'Predicted class: {output["labels"][0]}') ``` | 48ca5f282e7030b5a7912798e1b56a1e |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Limitations and bias No measures have been taken to estimate the bias and toxicity embedded in the model. Even though the fine-tuning data (which is of a scientific nature) may seem harmless, it is important to note that the corpus used to pre-train the vanilla model is very likely to contain a lot of unfiltered content from the internet, as stated in the [RoBERTa-large model card](https://huggingface.co/roberta-large | ec93fc905f0a45a6e953c3cd679ce4a3 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Training data Our data builds on top of scientific-domain annotated data from Microsoft Academic Graph (MAG). This database consists of a heterogeneous graph with billions of records from both scientific publications and patents, in addition to metadata information such as the authors, institutions, journals, conferences and their citation relationships. The documents are organized in a six-level hierarchical structure of scientific concepts, where the two top-most levels are manually curated in order to guarantee a high level of accuracy. To create the training corpus, a random sample of scientific articles with a publication year between 2000 and 2021 were retrieved from MAG with their respective titles and abstracts in English. This results in over 2M documents with their corresponding Field Of Study, which was obtained from the 1-level MAG taxonomy (292 possible classes, such as "Computational biology" or "Transport Engineering"). The fine-tuning dataset was constructed in a weakly supervised manner by converting text classification data to the entailment format. Using the relationship between scientific texts and their matching concepts in the 1-level MAG taxonomy we are able to generate the premise- hypothesis pairs corresponding to the entailment label. Conversely, we generate the pairs for the neutral label by removing the actual relationship between the texts and their scientific concepts and creating a virtual relationship with those to which they are not matched. | 6a316f5eac66c91a312999b98218f50e |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Training procedure The newly-created scientific dataset described in the previous section was used to fine-tune a 355M parameters RoBERTa model on the entailment task. To do so, the model has to compute the entailment score between every text that is fed to it and all candidate labels. The final prediction would be the highest-scoring class in a single-label classification setup, or the N classes above a certain threshold in a multi-label scenario. A subset of 52 labels from the training data were kept apart so that they could be used as a development set of fully-unseen classes. As a novelty, the validation was not performed on the entailment task (which is used a proxy) but directly on the target text classification task. This allows us to stop training at the right time via early stopping, which prevents the model from "overfitting" to the training task. This method was our way to counteract an effect that was empirically discovered during the experimentation period, where it was observed that after a certain point the model can start to worsen in the target task (ZSTC) despite still continuing to improve in the training task (RTE). The simple act of shortening the training time led to a boost in performance. Read the paper for more details on the methodology and the analysis of RTE/ZSTC correlation. | b95ad17b52b80d6a3d411b1a39e9a408 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Evaluation data The model's performance was evaluated on a collection of disciplinary-labeled textual datasets, both from the scientific domain (closer to training data) and the general domain (to assess generalizability). The following table provides an overview of the number of examples and labels for each dataset: | Dataset | Labels | Size | |------------------|--------|--------| | arXiv | 11 | 3,838 | | SciDocs-MeSH | 11 | 16,433 | | SciDocs-MAG | 19 | 17,501 | | Konstanz | 24 | 10,000 | | Elsevier | 26 | 14,738 | | PubMed | 109 | 5,000 | | Topic Categorization (Yahoo! Answers) | 10 | 60,000 | | Emotion Detection (UnifyEmotion) | 10 | 15,689 | | Situation Frame Detection (Situation Typing) | 12 | 3,311 | Please refer to the paper for further details on each particular dataset. | 4b7a164db819e2276b76698497e0966c |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Scientific domain benchmark | Model | arXiv | SciDocs-MesH | SciDocs-MAG | Konstanz | Elsevier | PubMed | |-------|-------|--------------|-------------|----------|----------|--------| | [fb/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) | 33.28 | **66.18**๐ฅ | 51.77 | 54.62 | 28.41 | **31.59**๐ฅ | | SCIroShot | **42.22**๐ฅ | 59.34 | **69.86**๐ฅ | **66.07**๐ฅ | **54.42**๐ฅ | 27.93 | | c55bf777c71e25e9c9dcfb4d78ca2a03 |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | General domain benchmark | Model | Topic | Emotion | Situation | |-------|-------|---------|-----------| | RTE [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 43.8 | 12.6 | **37.2**๐ฅ | | FEVER [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 40.1 | 24.7 | 21.0 | | MNLI [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf) | 37.9 | 22.3 | 15.4 | | NSP [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 50.6 | 16.5 | 25.8 | | NSP-Reverse [(Ma et al., 2021)](https://aclanthology.org/2021.acl-short.99.pdf) | 53.1 | 16.1 | 19.9 | | SCIroShot | **59.08**๐ฅ | **24.94**๐ฅ | 27.42 All the numbers reported above represent **label-wise weighted F1** except for the Topic classification dataset, which is evaluated in terms of **accuracy** following the notation from [(Yin et al., 2019)](https://arxiv.org/pdf/1909.00161.pdf). | adfaf46e4879c97904e2b318fcf8e3cf |
apache-2.0 | ['zero-shot', 'text-classification', 'science', 'mag'] | false | Disclaimer <details> <summary>Click to expand</summary> The model published in this repository is intended for a generalist purpose and is made available to third parties under a Apache v2.0 License. Please keep in mind that the model may have bias and/or any other undesirable distortions. When third parties deploy or provide systems and/or services to other parties using this model (or a system based on it) or become users of the model itself, they should note that it is under their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owners and creators of the model be liable for any results arising from the use made by third parties. </details> | 1cf9101de3a62ca8b7b188ef89f1cdda |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | Info This Wav2Vec2 model was first pretrained on 500 hours Kalmyk TV recordings and 1000 hours Mongolian speech recognition dataset. After that, the model was finetuned on a 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp | 9966ad1002f2dd027f02ec44b3a184b7 |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | datasets) created by a voice conversion model. * 50% WER on a private test set created from Kalmyk TV recordnings * on clean voice recordings, the model should have much lower WER * voice conversion info * 300 hours [Kalmyk synthetic STT dataset](https://github.com/tugstugi/mongolian-nlp | 9a82b63c4a5af1e1ddcb3b2245a3890a |
apache-2.0 | ['speech', 'audio', 'automatic-speech-recognition'] | false | datasets) * The source voice is a Kalmyk female voice TTS * Target voices are from the VCTK dataset * example data: https://twitter.com/tugstugi/status/1409111296897912835 * each WAV has a different text created from Kalmyk books | ca5d9770e8393a0c9324f87ff88e59d2 |
apache-2.0 | ['generated_from_trainer'] | false | english-filipino-wav2vec2-l-xls-r-test-07 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the filipino_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6768 - Wer: 0.3755 | f729d84a7cf370972dc9d93fbc143bfb |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - 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_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP | 494f78b2cdbc3e96df26ce17ac130563 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9255 | 2.09 | 400 | 0.7742 | 0.7694 | | 0.5792 | 4.19 | 800 | 0.5368 | 0.5250 | | 0.3611 | 6.28 | 1200 | 0.4796 | 0.4718 | | 0.2742 | 8.38 | 1600 | 0.5308 | 0.4764 | | 0.201 | 10.47 | 2000 | 0.5885 | 0.4723 | | 0.164 | 12.57 | 2400 | 0.5595 | 0.4750 | | 0.1374 | 14.66 | 2800 | 0.5836 | 0.4366 | | 0.1138 | 16.75 | 3200 | 0.6110 | 0.4628 | | 0.0991 | 18.85 | 3600 | 0.6179 | 0.4174 | | 0.0837 | 20.94 | 4000 | 0.6681 | 0.4170 | | 0.0722 | 23.04 | 4400 | 0.6665 | 0.4103 | | 0.0576 | 25.13 | 4800 | 0.7538 | 0.4068 | | 0.052 | 27.23 | 5200 | 0.6808 | 0.3844 | | 0.0449 | 29.32 | 5600 | 0.6768 | 0.3755 | | e7ad6a7689222d5cd08966448c6b1174 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | vit_receipts_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cord, rvl-cdip, visual-genome and an external receipt dataset to carry out Binary Classification (`ticket` vs `no_ticket`). Ticket here is used as a synonym to "receipt". It achieves the following results on the evaluation set, which contain pictures from the above datasets in scanned, photography or mobile picture formats (color and grayscale): - Loss: 0.0116 - F1: 0.9991 | 178f6b1fb7f9eb5817b70dbb2cfad7d4 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Intended uses & limitations Use this model to classify your images into tickets or not tickers. WIth the tickets group, you can use Multimodal Information Extraction, as Visual Named Entity Recognition, to extract the ticket items, amounts, total, etc. Check the Cord dataset for more information. | 53d26941e34e04205da931edafe2a584 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training and evaluation data This model used 2 datasets as positive class (`ticket`): - `cord` - `https://expressexpense.com/blog/free-receipt-images-ocr-machine-learning-dataset/` For the negative class (`no_ticket`), the following datasets were used: - A subset of `RVL-CDIP` - A subset of `visual-genome` | 7e7f5abfa12b9c98f7344ddb2fb69739 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training procedure Datasets were loaded with different distributions of data for positive and negative classes. Then, normalization and resizing is carried out to adapt it to ViT expected input. Different runs were carried out changing the data distribution and the hyperparameters to maximize F1. | 227d3c926bf59d476e9865dad90290d4 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 - num_epochs: 1 - mixed_precision_training: Native AMP | 668deb64684b9b9c34fe78decac78430 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0026 | 0.28 | 500 | 0.0187 | 0.9982 | | 0.0186 | 0.56 | 1000 | 0.0116 | 0.9991 | | 0.0006 | 0.84 | 1500 | 0.0044 | 0.9997 | | 4129f78bc586e1bd74cb809397eba117 |
mit | [] | false | Cosmoose-SD on Stable Diffusion via Dreambooth trained on the [fast-DreamBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook | a29ea3cf51728c1e113734f223414ca8 |
mit | [] | false | Model by woolion This your the Stable Diffusion model fine-tuned the Cosmoose-SD concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt(s)` with `csmoos_style`. The DreamBooth step was trained on Cosmoose(.org) images, all drawn by Woolion. You can also train your own concepts and upload them to the library by using [the fast-DremaBooth.ipynb by TheLastBen](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb). You can run your new concept via A1111 Colab :[Fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Sample pictures of this concept:     | 58053af6138533efe66d6e0ba05d4f3f |
cc-by-4.0 | ['generated_from_trainer'] | false | thai-squad This model is a fine-tuned version of [deepset/xlm-roberta-base-squad2](https://huggingface.co/deepset/xlm-roberta-base-squad2) on Thai dataset from [iApp Technology Co., Ltd.](https://github.com/iapp-technology/iapp-wiki-qa-dataset). | e42da5dd8795fd97aedc11036b944fe3 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 | b695ad7dabfa455f7087fd35f0ce9b57 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_vp-es_s35 Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (ru)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 770facc59d85ab7f1c9732193b43b887 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab4 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: 0.9149 - Wer: 0.5907 | 6cc9516be5bbbeb743ecb60d53057305 |
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