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 results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6868 | 1.0 | 1053 | 0.7027 | 0.5092 | | 0.6868 | 2.0 | 2106 | 0.7027 | 0.5092 | | 0.6867 | 3.0 | 3159 | 0.6970 | 0.5092 | | 0.687 | 4.0 | 4212 | 0.6992 | 0.5092 | | 0.6866 | 5.0 | 5265 | 0.6983 | 0.5092 | | 59a877611255358e171cce2e06991c67 |
cc | ['text generation'] | false | How to use ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("nandinib1999/quote-generator") model = AutoModelWithLMHead.from_pretrained("nandinib1999/quote-generator") ``` | 6a3098c876a2f3b5f153448d89ccb649 |
cc | ['text generation'] | false | Training data This is the distribution of the total dataset into training, validation and test dataset for the fine-tuning task. <table style="width:30%"> <tr> <th>train</th> <td>349796</td> </tr> <tr> <th>validation</th> <td>99942</td> </tr> <tr> <th>test</th> <td>49971</td> </tr> </table> | 63a34a1b69600bf9c30ccb754c5f9bc3 |
mit | [] | false | Contextualized Commonsense Inference in Dialogues v2 The pretrained checkpoint for the paper [Multiview Contextual Commonsense Inference: A New Dataset and Task](https://arxiv.org/abs/2210.02890). The model is trained based on the [T5-large](https://huggingface.co/t5-large) checkpoint.  | a8e1b73153c3b02557963088199e4dd5 |
mit | [] | false | Datasets The dataset used to pretrain the model can be obtained from the [CICERO repo](https://github.com/declare-lab/CICERO) following instructions. The CICEROv2 consists of annotated commonsense inferences including cause and emotional reaction, etc. The dialogues are from multiple datasets. | Dataset | | 9a4a20b79605fdebee57d7ad90fdc4ef |
mit | [] | false | Examples Some examples of generated results from the pretrained model (the zero-shot setting). **Subsequent Event** ``` What is or could be the subsequent event of the target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it ``` Predicted subsequent event: ``` David's girlfriend apologized to david for her mistake. ``` **Cause** ``` What is or could be the cause of the target? <sep> target: But she did and made me disappointed . <sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it ``` Predicted cause: ``` David's girlfriend was not nice to him. ``` **Emotional Reaction** ``` What is the possible emotional reaction of the listener in response to target? <sep> target: Oh . I just can't forget it .<sep> context: A: David , why didn't you clean the room ?, <utt> B: I'm not in the mood ., <utt> A: Why are you feeling depressed ?, <utt> B: I was told my girlfriend was speaking ill of me. That \u2019 s a real let-down ., <utt> A: I don t think she will do such a thing ., <utt> B: But she did and made me disappointed ., <utt> A: Oh , cheer up . A girlfriend is not everything ., <utt> B: But she means a lot to me ., <utt> A: Then forgive her mistake ., <utt> B: Oh . I just can't forget it ``` Predicted emotional reaction: ``` The listener is hopeful that david will forgive his girlfriend for her mistake. ``` | 7ec14fd4d5db2207dff8ba73a9cca515 |
apache-2.0 | ['generated_from_keras_callback'] | false | bearbearchu/mt5-small-finetuned-wikipedia-summarization-jp This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2757 - Validation Loss: 0.2210 - Epoch: 7 | 6f7d611dfa70ebfd8ad32c8315530d80 |
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': 5.6e-05, 'decay_steps': 7656, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 | 91f31f7fd880e216ce38954627fc98fc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.1713 | 0.3484 | 0 | | 0.6239 | 0.3156 | 1 | | 0.4820 | 0.2693 | 2 | | 0.3973 | 0.2595 | 3 | | 0.3377 | 0.2480 | 4 | | 0.3093 | 0.2321 | 5 | | 0.2843 | 0.2236 | 6 | | 0.2757 | 0.2210 | 7 | | fe88918f8db51aa6efad511d9ce30126 |
apache-2.0 | ['sentiment analysis', 'classification', 'arabic dialect', 'tunisian dialect'] | false | This is a fineTued Bert model on Tunisian dialect text (Used dataset: AhmedBou/Tunisian-Dialect-Corpus), ready for sentiment analysis and classification tasks. LABEL_1: Positive LABEL_2: Negative LABEL_0: Neutral This work is an integral component of my Master's degree thesis and represents the culmination of extensive research and labor. If you wish to utilize the Tunisian-Dialect-Corpus or the TuniBert model, kindly refer to the directory provided. [huggingface.co/AhmedBou][github.com/BoulahiaAhmed] | 8d4b673f0a3e715b666a442336b1e7ca |
mit | [] | false | model by deref This your the Stable Diffusion model fine-tuned the Arthur Leywin concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks guy** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:      | 304e00d24938c937240662e68c197474 |
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-et_en-wiki", num_labels=1, use_cuda=torch.cuda.is_available()) predictions, raw_outputs = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions) ``` | 1cf0ab43935f73fcd324c3ea94b3629d |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3651 - Accuracy: 0.9151 | 637997524733a64fe1bdee9f96f138e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1902 | 1.0 | 4210 | 0.3102 | 0.9117 | | 0.1293 | 2.0 | 8420 | 0.3672 | 0.9048 | | 0.084 | 3.0 | 12630 | 0.3651 | 0.9151 | | 0.0682 | 4.0 | 16840 | 0.3971 | 0.9037 | | 0.0438 | 5.0 | 21050 | 0.4720 | 0.9117 | | 654ec49526eb72b643294263429dc26e |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | mt-sq-sv-finetuned This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sq-sv](https://huggingface.co/Helsinki-NLP/opus-mt-sq-sv) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2250 - Bleu: 47.0111 | 7b757792ac7e70b943fa6574528854ad |
apache-2.0 | ['generated_from_trainer', 'translation'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 1.7042 | 1.0 | 4219 | 1.4806 | 41.9650 | | 1.5537 | 2.0 | 8438 | 1.3955 | 43.1524 | | 1.4352 | 3.0 | 12657 | 1.3142 | 44.4373 | | 1.3346 | 4.0 | 16876 | 1.2793 | 45.2265 | | 1.2847 | 5.0 | 21095 | 1.2597 | 45.8071 | | 1.2821 | 6.0 | 25314 | 1.2454 | 46.3737 | | 1.2342 | 7.0 | 29533 | 1.2363 | 46.6308 | | 1.2092 | 8.0 | 33752 | 1.2301 | 46.8227 | | 1.1766 | 9.0 | 37971 | 1.2260 | 46.9719 | | 1.1836 | 10.0 | 42190 | 1.2250 | 47.0111 | | 562935187cbfe4436be9f217a4ec45a3 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_accent_france-2_belgium-8_s709 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When 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. | 27c0261f5e423c06d353b3391b7ecc52 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Tiny ml - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1286 - Wer: 106.9296 | 07bf862e0f730f468cdb115e71abc5c3 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5755 | 4.02 | 500 | 0.4241 | 81.2652 | | 0.4182 | 9.01 | 1000 | 0.3245 | 72.7494 | | 0.3387 | 14.01 | 1500 | 0.2914 | 67.2749 | | 0.2923 | 19.0 | 2000 | 0.2745 | 60.3406 | | 0.2596 | 24.0 | 2500 | 0.2645 | 58.2725 | | 0.2356 | 28.02 | 3000 | 0.2629 | 60.3406 | | 0.2167 | 33.01 | 3500 | 0.2647 | 59.9757 | | 0.2039 | 4.02 | 4000 | 0.2617 | 58.2725 | | 0.1938 | 9.01 | 4500 | 0.2644 | 58.2725 | | 0.1858 | 14.01 | 5000 | 0.2636 | 58.7591 | | 9f7284df344b8149444823f3215d41ae |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Afrikaans (af) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza). This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo Last updated 2022-10-12 02:47:23.696 | b8fe21b45f2d9de00c32d155a599466f |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | distilgpt2-tiny-conversational This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a parsed version of Wizard of Wikipedia. Persona alpha/beta framework designed for use with [ai-msgbot](https://github.com/pszemraj/ai-msgbot). It achieves the following results on the evaluation set: - Loss: 2.2461 | a953a3a200197ac15a01d4157fd0774d |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Intended uses & limitations - usage is designed for integrating with this repo: [ai-msgbot](https://github.com/pszemraj/ai-msgbot) - the main specific information to know is that the model generates whole conversations between two entities, `person alpha` and `person beta`. These entity names are used functionally as custom `<bos>` tokens to extract when one response ends and another begins. | 9822eef1ab2e90b14eb7db0075544f2d |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 30 | 3c37c88a38102a84bbcf69745de635fe |
apache-2.0 | ['text-generation', 'chatbot', 'dialogue', 'distilgpt2', 'gpt2', 'ai-msgbot'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 418 | 2.7793 | | 2.9952 | 2.0 | 836 | 2.6914 | | 2.7684 | 3.0 | 1254 | 2.6348 | | 2.685 | 4.0 | 1672 | 2.5938 | | 2.6243 | 5.0 | 2090 | 2.5625 | | 2.5816 | 6.0 | 2508 | 2.5332 | | 2.5816 | 7.0 | 2926 | 2.5098 | | 2.545 | 8.0 | 3344 | 2.4902 | | 2.5083 | 9.0 | 3762 | 2.4707 | | 2.4793 | 10.0 | 4180 | 2.4551 | | 2.4531 | 11.0 | 4598 | 2.4395 | | 2.4269 | 12.0 | 5016 | 2.4238 | | 2.4269 | 13.0 | 5434 | 2.4102 | | 2.4051 | 14.0 | 5852 | 2.3945 | | 2.3777 | 15.0 | 6270 | 2.3848 | | 2.3603 | 16.0 | 6688 | 2.3711 | | 2.3394 | 17.0 | 7106 | 2.3613 | | 2.3206 | 18.0 | 7524 | 2.3516 | | 2.3206 | 19.0 | 7942 | 2.3398 | | 2.3026 | 20.0 | 8360 | 2.3301 | | 2.2823 | 21.0 | 8778 | 2.3203 | | 2.2669 | 22.0 | 9196 | 2.3105 | | 2.2493 | 23.0 | 9614 | 2.3027 | | 2.2334 | 24.0 | 10032 | 2.2930 | | 2.2334 | 25.0 | 10450 | 2.2852 | | 2.2194 | 26.0 | 10868 | 2.2754 | | 2.2014 | 27.0 | 11286 | 2.2695 | | 2.1868 | 28.0 | 11704 | 2.2598 | | 2.171 | 29.0 | 12122 | 2.2539 | | 2.1597 | 30.0 | 12540 | 2.2461 | | 9473bd87e1c4375b06551700d8dee25a |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-korean-convsen2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0094 - Cer: 0.0012 | 03b9e141328bca2770cf483873638726 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - 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: 3 - mixed_precision_training: Native AMP | 9f730f35aaa753705614a3659c787a12 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.8421 | 1.0 | 1762 | 0.2383 | 0.0591 | | 0.1721 | 2.0 | 3524 | 0.0309 | 0.0060 | | 0.065 | 3.0 | 5286 | 0.0094 | 0.0012 | | 9f1b1c5ed326474ea31fe843a0d67fa0 |
apache-2.0 | ['generated_from_trainer'] | false | TSE_BERT_5E 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: 0.3664 - Accuracy: 0.9267 | 27621251009f18d113c752ed2a8bb684 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6836 | 0.06 | 50 | 0.5614 | 0.8267 | | 0.4679 | 0.12 | 100 | 0.3521 | 0.9 | | 0.3325 | 0.17 | 150 | 0.2747 | 0.8933 | | 0.2493 | 0.23 | 200 | 0.2712 | 0.9067 | | 0.273 | 0.29 | 250 | 0.2304 | 0.9333 | | 0.2888 | 0.35 | 300 | 0.2253 | 0.92 | | 0.2558 | 0.4 | 350 | 0.2110 | 0.9267 | | 0.1997 | 0.46 | 400 | 0.2206 | 0.9267 | | 0.2748 | 0.52 | 450 | 0.2358 | 0.9267 | | 0.2448 | 0.58 | 500 | 0.2942 | 0.8933 | | 0.2247 | 0.63 | 550 | 0.2410 | 0.9067 | | 0.2002 | 0.69 | 600 | 0.2222 | 0.9133 | | 0.2668 | 0.75 | 650 | 0.2372 | 0.9133 | | 0.2701 | 0.81 | 700 | 0.2288 | 0.9333 | | 0.2034 | 0.87 | 750 | 0.2415 | 0.9267 | | 0.2374 | 0.92 | 800 | 0.2278 | 0.92 | | 0.2305 | 0.98 | 850 | 0.2270 | 0.92 | | 0.1704 | 1.04 | 900 | 0.2591 | 0.9333 | | 0.1826 | 1.1 | 950 | 0.2481 | 0.9267 | | 0.1116 | 1.15 | 1000 | 0.2906 | 0.9133 | | 0.1527 | 1.21 | 1050 | 0.2902 | 0.92 | | 0.1692 | 1.27 | 1100 | 0.2489 | 0.9333 | | 0.158 | 1.33 | 1150 | 0.2576 | 0.9333 | | 0.1608 | 1.38 | 1200 | 0.3344 | 0.9267 | | 0.1194 | 1.44 | 1250 | 0.3615 | 0.9267 | | 0.201 | 1.5 | 1300 | 0.3374 | 0.92 | | 0.1938 | 1.56 | 1350 | 0.2847 | 0.92 | | 0.1479 | 1.61 | 1400 | 0.3044 | 0.9267 | | 0.1628 | 1.67 | 1450 | 0.2980 | 0.9267 | | 0.1783 | 1.73 | 1500 | 0.3132 | 0.9267 | | 0.1885 | 1.79 | 1550 | 0.2676 | 0.9333 | | 0.1651 | 1.85 | 1600 | 0.2709 | 0.9333 | | 0.1376 | 1.9 | 1650 | 0.2777 | 0.94 | | 0.1571 | 1.96 | 1700 | 0.2761 | 0.9333 | | 0.1561 | 2.02 | 1750 | 0.2912 | 0.94 | | 0.1187 | 2.08 | 1800 | 0.2893 | 0.9467 | | 0.1205 | 2.13 | 1850 | 0.2882 | 0.9467 | | 0.0751 | 2.19 | 1900 | 0.3032 | 0.9467 | | 0.1412 | 2.25 | 1950 | 0.2926 | 0.9467 | | 0.0783 | 2.31 | 2000 | 0.2962 | 0.9467 | | 0.1094 | 2.36 | 2050 | 0.2909 | 0.9333 | | 0.1158 | 2.42 | 2100 | 0.3087 | 0.9333 | | 0.0606 | 2.48 | 2150 | 0.3102 | 0.9467 | | 0.1164 | 2.54 | 2200 | 0.2812 | 0.94 | | 0.1311 | 2.6 | 2250 | 0.3736 | 0.9267 | | 0.1087 | 2.65 | 2300 | 0.3069 | 0.94 | | 0.109 | 2.71 | 2350 | 0.3176 | 0.94 | | 0.0789 | 2.77 | 2400 | 0.3130 | 0.94 | | 0.0784 | 2.83 | 2450 | 0.3338 | 0.94 | | 0.1388 | 2.88 | 2500 | 0.3440 | 0.9333 | | 0.1062 | 2.94 | 2550 | 0.2883 | 0.94 | | 0.1016 | 3.0 | 2600 | 0.2776 | 0.94 | | 0.0642 | 3.06 | 2650 | 0.3302 | 0.9333 | | 0.052 | 3.11 | 2700 | 0.3217 | 0.94 | | 0.0539 | 3.17 | 2750 | 0.3899 | 0.9267 | | 0.0593 | 3.23 | 2800 | 0.3283 | 0.9467 | | 0.0468 | 3.29 | 2850 | 0.3382 | 0.9467 | | 0.0546 | 3.34 | 2900 | 0.3133 | 0.9467 | | 0.107 | 3.4 | 2950 | 0.3550 | 0.94 | | 0.1079 | 3.46 | 3000 | 0.3484 | 0.94 | | 0.0782 | 3.52 | 3050 | 0.3313 | 0.94 | | 0.0635 | 3.58 | 3100 | 0.3418 | 0.94 | | 0.0771 | 3.63 | 3150 | 0.3685 | 0.9333 | | 0.0629 | 3.69 | 3200 | 0.3467 | 0.9333 | | 0.0552 | 3.75 | 3250 | 0.3677 | 0.94 | | 0.0531 | 3.81 | 3300 | 0.3436 | 0.9333 | | 0.0819 | 3.86 | 3350 | 0.3802 | 0.9333 | | 0.0583 | 3.92 | 3400 | 0.3441 | 0.9333 | | 0.0434 | 3.98 | 3450 | 0.3666 | 0.9333 | | 0.0747 | 4.04 | 3500 | 0.3554 | 0.9333 | | 0.0309 | 4.09 | 3550 | 0.3582 | 0.9333 | | 0.1057 | 4.15 | 3600 | 0.3615 | 0.9267 | | 0.0391 | 4.21 | 3650 | 0.3583 | 0.9267 | | 0.0433 | 4.27 | 3700 | 0.3514 | 0.9333 | | 0.0597 | 4.33 | 3750 | 0.3580 | 0.9333 | | 0.0663 | 4.38 | 3800 | 0.3390 | 0.94 | | 0.0563 | 4.44 | 3850 | 0.3518 | 0.9267 | | 0.0702 | 4.5 | 3900 | 0.3542 | 0.9267 | | 0.0383 | 4.56 | 3950 | 0.3528 | 0.9267 | | 0.0474 | 4.61 | 4000 | 0.3485 | 0.9333 | | 0.0265 | 4.67 | 4050 | 0.3489 | 0.94 | | 0.0165 | 4.73 | 4100 | 0.3616 | 0.9333 | | 0.0489 | 4.79 | 4150 | 0.3579 | 0.9333 | | 0.0478 | 4.84 | 4200 | 0.3603 | 0.9333 | | 0.0536 | 4.9 | 4250 | 0.3666 | 0.9267 | | 0.0551 | 4.96 | 4300 | 0.3664 | 0.9267 | | a4be8aed77c50168ce9c7dcb67d19a6d |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Model Card for DeBERTa-v3-base-tasksource-nli DeBERTa-v3-base fine-tuned with multi-task learning on 444 tasks of the [tasksource collection](https://github.com/sileod/tasksource/) You can further fine-tune this model to use it for any classification or multiple-choice task. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI). The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training. This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder. Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched. The number of examples per task was capped to 64k. The model was trained for 20k steps with a batch size of 384, and a peak learning rate of 2e-5. The list of tasks is available in tasks.md tasksource training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing | 0f9cb87b7034ecde924df0b93e82f458 |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Model Recycling An earlier (weaker) version model is ranked 1st among all models with the microsoft/deberta-v3-base architecture as of 10/01/2023 Results: [Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=1.41&mnli_lp=nan&20_newsgroup=0.63&ag_news=0.46&amazon_reviews_multi=-0.40&anli=0.94&boolq=2.55&cb=10.71&cola=0.49&copa=10.60&dbpedia=0.10&esnli=-0.25&financial_phrasebank=1.31&imdb=-0.17&isear=0.63&mnli=0.42&mrpc=-0.23&multirc=1.73&poem_sentiment=0.77&qnli=0.12&qqp=-0.05&rotten_tomatoes=0.67&rte=2.13&sst2=0.01&sst_5bins=-0.02&stsb=1.39&trec_coarse=0.24&trec_fine=0.18&tweet_ev_emoji=0.62&tweet_ev_emotion=0.43&tweet_ev_hate=1.84&tweet_ev_irony=1.43&tweet_ev_offensive=0.17&tweet_ev_sentiment=0.08&wic=-1.78&wnli=3.03&wsc=9.95&yahoo_answers=0.17&model_name=sileod%2Fdeberta-v3-base_tasksource-420&base_name=microsoft%2Fdeberta-v3-base) using sileod/deberta-v3-base_tasksource-420 as a base model yields average score of 80.45 in comparison to 79.04 by microsoft/deberta-v3-base. | 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|--------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| | 87.042 | 90.9 | 66.46 | 59.7188 | 85.5352 | 85.7143 | 87.0566 | 69 | 79.5333 | 91.6735 | 85.8 | 94.324 | 72.4902 | 90.2055 | 88.9706 | 63.9851 | 87.5 | 93.6299 | 91.7363 | 91.0882 | 84.4765 | 95.0688 | 56.9683 | 91.6654 | 98 | 91.2 | 46.814 | 84.3772 | 58.0471 | 81.25 | 85.2326 | 71.8821 | 69.4357 | 73.2394 | 74.0385 | 72.2 | For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/) | 68656aac3e59570de4d2cd0bef3d41f1 |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Citation More details on this [article:](https://arxiv.org/abs/2301.05948) ```bib @article{sileo2023tasksource, title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation}, author={Sileo, Damien}, url= {https://arxiv.org/abs/2301.05948}, journal={arXiv preprint arXiv:2301.05948}, year={2023} } ``` | ea924a304fe8d610f0f73057e820b38e |
apache-2.0 | ['deberta-v3-base', 'text-classification', 'nli', 'natural-language-inference', 'multitask', 'multi-task', 'extreme-multi-task', 'extreme-mtl', 'deberta-v3-base', 'tasksource'] | false | Loading a specific classifier Classifiers for all tasks available. ```python from torch import nn TASK_NAME = "hh-rlhf" class MultiTask(transformers.DebertaV2ForMultipleChoice): def __init__(self, *args, **kwargs): super().__init__(*args) n=len(self.config.tasks) cs=self.config.classifiers_size self.Z = nn.Embedding(n,768) self.classifiers = nn.ModuleList([torch.nn.Linear(*size) for size in cs]) model = MultiTask.from_pretrained("sileod/deberta-v3-base-tasksource-nli",ignore_mismatched_sizes=True) task_index = {k:v for v,k in dict(enumerate(model.config.tasks)).items()}[TASK_NAME] model.classifier = model.classifiers[task_index] | 7c8d99f262988d23c69347b066aee933 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mnli-target-glue-mnli This model is a fine-tuned version of [muhtasham/small-mlm-glue-mnli](https://huggingface.co/muhtasham/small-mlm-glue-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6497 - Accuracy: 0.7259 | 7aed6d6b2398c84046fb019825a1b0aa |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9145 | 0.04 | 500 | 0.8234 | 0.6373 | | 0.8123 | 0.08 | 1000 | 0.7786 | 0.6628 | | 0.7745 | 0.12 | 1500 | 0.7489 | 0.6756 | | 0.7496 | 0.16 | 2000 | 0.7311 | 0.6878 | | 0.7424 | 0.2 | 2500 | 0.7205 | 0.6921 | | 0.7325 | 0.24 | 3000 | 0.7007 | 0.7007 | | 0.7126 | 0.29 | 3500 | 0.6780 | 0.7131 | | 0.7007 | 0.33 | 4000 | 0.6652 | 0.7189 | | 0.6755 | 0.37 | 4500 | 0.6737 | 0.7249 | | 0.6803 | 0.41 | 5000 | 0.6497 | 0.7259 | | af1a9098a950239416d2c91afdac147f |
apache-2.0 | ['generated_from_trainer'] | false | aesthetic_attribute_classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [PCCD dataset](https://github.com/ivclab/DeepPhotoCritic-ICCV17). It achieves the following results on the evaluation set: - Loss: 0.3976 - Precision: {'precision': 0.877129341279301} - Recall: {'recall': 0.8751381215469614} - F1: {'f1': 0.875529982855803} - Accuracy: {'accuracy': 0.8751381215469614} | 8124c15bfbc78be794be6007d7df3df3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:------------------------------:|:--------------------------:|:--------------------------------:| | 0.452 | 1.0 | 1528 | 0.4109 | {'precision': 0.8632779077963935} | {'recall': 0.8615101289134438} | {'f1': 0.8618616182904953} | {'accuracy': 0.8615101289134438} | | 0.3099 | 2.0 | 3056 | 0.3976 | {'precision': 0.877129341279301} | {'recall': 0.8751381215469614} | {'f1': 0.875529982855803} | {'accuracy': 0.8751381215469614} | | 0.227 | 3.0 | 4584 | 0.4320 | {'precision': 0.876211408446225} | {'recall': 0.874401473296501} | {'f1': 0.8747427955387239} | {'accuracy': 0.874401473296501} | | 0.1645 | 4.0 | 6112 | 0.4840 | {'precision': 0.8724641667216837} | {'recall': 0.8714548802946593} | {'f1': 0.8714577820909117} | {'accuracy': 0.8714548802946593} | | 0.1141 | 5.0 | 7640 | 0.5083 | {'precision': 0.8755445355051571} | {'recall': 0.8747697974217311} | {'f1': 0.8748766125899489} | {'accuracy': 0.8747697974217311} | | 05ce3d67b9dadebb87d2533ca78c781e |
apache-2.0 | ['generated_from_trainer'] | false | pos_test_model_1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1521 - Accuracy: 0.9530 - F1: 0.9523 - Precision: 0.9576 - Recall: 0.9530 | deaf9173718026216158fab6c2e7dd95 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.1882 | 1.0 | 1744 | 0.1521 | 0.9530 | 0.9523 | 0.9576 | 0.9530 | | eb9b39c008000f5c6b811201a2d714d2 |
apache-2.0 | ['translation'] | false | jpn-msa * source group: Japanese * target group: Malay (macrolanguage) * OPUS readme: [jpn-msa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-msa/README.md) * model: transformer-align * source language(s): jpn jpn_Hani jpn_Hira jpn_Kana * target language(s): ind zlm_Latn zsm_Latn * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-msa/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-msa/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-msa/opus-2020-06-17.eval.txt) | 7497c3168b62f12e49c64a9ba64d390b |
apache-2.0 | ['translation'] | false | System Info: - hf_name: jpn-msa - source_languages: jpn - target_languages: msa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/jpn-msa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ja', 'ms'] - src_constituents: {'jpn_Hang', 'jpn', 'jpn_Yiii', 'jpn_Kana', 'jpn_Hani', 'jpn_Bopo', 'jpn_Latn', 'jpn_Hira'} - tgt_constituents: {'zsm_Latn', 'ind', 'max_Latn', 'zlm_Latn', 'min'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-msa/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/jpn-msa/opus-2020-06-17.test.txt - src_alpha3: jpn - tgt_alpha3: msa - short_pair: ja-ms - chrF2_score: 0.469 - bleu: 21.5 - brevity_penalty: 0.9259999999999999 - ref_len: 17028.0 - src_name: Japanese - tgt_name: Malay (macrolanguage) - train_date: 2020-06-17 - src_alpha2: ja - tgt_alpha2: ms - prefer_old: False - long_pair: jpn-msa - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | a02acf3bc98716dc90540c34e579351d |
mit | [] | false | inuyama-muneto-style on Stable Diffusion Artist: <https://twitter.com/inuyamamuneto/status/1223899994832302081> This is the `<inuyama-muneto-style>` 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`:     | a92f4c1d416c4e108717fced4ed01083 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_logit_kd_qqp This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset. It achieves the following results on the evaluation set: - Loss: 0.6623 - Accuracy: 0.6425 - F1: 0.0601 - Combined Score: 0.3513 | 918cdc12fa1ae6a93dbe527e551126ce |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:| | 0.7968 | 1.0 | 1422 | 0.7159 | 0.6323 | 0.0030 | 0.3176 | | 0.6542 | 2.0 | 2844 | 0.6925 | 0.6338 | 0.0115 | 0.3226 | | 0.5893 | 3.0 | 4266 | 0.6695 | 0.6348 | 0.0172 | 0.3260 | | 0.5538 | 4.0 | 5688 | 0.7068 | 0.6386 | 0.0393 | 0.3390 | | 0.5323 | 5.0 | 7110 | 0.6670 | 0.6500 | 0.1014 | 0.3757 | | 0.5181 | 6.0 | 8532 | 0.6738 | 0.6420 | 0.0573 | 0.3497 | | 0.5082 | 7.0 | 9954 | 0.6623 | 0.6425 | 0.0601 | 0.3513 | | 0.5012 | 8.0 | 11376 | 0.6995 | 0.6412 | 0.0536 | 0.3474 | | 0.4957 | 9.0 | 12798 | 0.6836 | 0.6472 | 0.0858 | 0.3665 | | 0.4911 | 10.0 | 14220 | 0.6778 | 0.6484 | 0.0922 | 0.3703 | | 0.4874 | 11.0 | 15642 | 0.7183 | 0.6415 | 0.0550 | 0.3483 | | 0.484 | 12.0 | 17064 | 0.6730 | 0.6451 | 0.0744 | 0.3598 | | a849ea6e7a7b733029e2cf9492d8a528 |
other | ['vision', 'image-segmentation'] | false | SegFormer (b5-sized) model fine-tuned on ADE20k SegFormer model fine-tuned on ADE20k at resolution 640x640. It was introduced in the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Xie et al. and first released in [this repository](https://github.com/NVlabs/SegFormer). Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team. | 21c6ac2426764509b55bb6a5e61c7a90 |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation from PIL import Image import requests feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-512-512") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | 39aa2198ff5b87cd34130bbbb8e0a94d |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 2 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 24 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 | 54b7994c15fc2b961cd3f5ad784a41ce |
mit | ['generated_from_trainer'] | false | roberta-base-ner This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0814 - eval_precision: 0.9101 - eval_recall: 0.9336 - eval_f1: 0.9217 - eval_accuracy: 0.9799 - eval_runtime: 10.2964 - eval_samples_per_second: 315.646 - eval_steps_per_second: 39.529 - epoch: 1.14 - step: 500 | 8b27a3afb9dee05b024c77810253bab8 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - 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 | 353d6ce6ea504f563c42a996cff9e4e7 |
mit | ['summarization', 'generated_from_trainer'] | false | mbart-large-50-finetuned-amazon-pr-test This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.9825 - Rouge1: 0.1522 - Rouge2: 0.0535 - Rougel: 0.1400 - Rougelsum: 0.1407 | cd446a512760862b699aade953a0f2d9 |
mit | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 2.909 | 1.0 | 838 | 2.8106 | 0.1264 | 0.0576 | 0.1237 | 0.1245 | | 1.8102 | 2.0 | 1676 | 2.8872 | 0.1392 | 0.0683 | 0.1341 | 0.1353 | | 1.0773 | 3.0 | 2514 | 3.3501 | 0.1548 | 0.0660 | 0.1481 | 0.1496 | | 0.5431 | 4.0 | 3352 | 3.9495 | 0.1190 | 0.0566 | 0.1137 | 0.1152 | | 0.2371 | 5.0 | 4190 | 4.5519 | 0.1562 | 0.0707 | 0.1462 | 0.1470 | | 0.0934 | 6.0 | 5028 | 4.7016 | 0.1524 | 0.0636 | 0.1451 | 0.1462 | | 0.0375 | 7.0 | 5866 | 4.9661 | 0.1531 | 0.0564 | 0.1422 | 0.1435 | | 0.0155 | 8.0 | 6704 | 4.9825 | 0.1522 | 0.0535 | 0.1400 | 0.1407 | | 3542fb82f54734fe6b6bc28c5f263faf |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-expression_epoch5 This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5897 - Precision: 0.5835 - Recall: 0.5688 - F1: 0.5760 - Accuracy: 0.8344 | 90c152179ce83809238f1ea6c7a4a9e8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 218 | 0.5185 | 0.5076 | 0.5034 | 0.5055 | 0.8207 | | No log | 2.0 | 436 | 0.4972 | 0.4948 | 0.5638 | 0.5271 | 0.8177 | | 0.5193 | 3.0 | 654 | 0.5128 | 0.5838 | 0.5554 | 0.5692 | 0.8390 | | 0.5193 | 4.0 | 872 | 0.5665 | 0.5612 | 0.6074 | 0.5834 | 0.8224 | | 0.2063 | 5.0 | 1090 | 0.5897 | 0.5835 | 0.5688 | 0.5760 | 0.8344 | | 8618b4b48029b726de40a06cc354e6dd |
cc-by-4.0 | ['spanish', 'roberta'] | false | This is a **RoBERTa-base** model trained from scratch in Spanish. The training dataset is [mc4](https://huggingface.co/datasets/bertin-project/mc4-es-sampled ) subsampling documents to a total of about 50 million examples. Sampling is random. This model has been trained for 230.000 steps (early stopped before the 250k intended steps). Please see our main [card](https://huggingface.co/bertin-project/bertin-roberta-base-spanish) for more information. This is part of the [Flax/Jax Community Week](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organised by [HuggingFace](https://huggingface.co/) and TPU usage sponsored by Google. | 2065d8a15ae8a9c6377c25265d1e31e7 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1383 - F1: 0.8589 | 068544fb43434caac93cb796f0beaa04 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2631 | 1.0 | 525 | 0.1596 | 0.8218 | | 0.1296 | 2.0 | 1050 | 0.1353 | 0.8479 | | 0.0821 | 3.0 | 1575 | 0.1383 | 0.8589 | | 9de78702c900444ff37a5eaefa7c1926 |
apache-2.0 | ['image-classification', 'timm'] | false | Model card for coatnet_rmlp_nano_rw_224.sw_in1k A timm specific CoAtNet (w/ a MLP Log-CPB (continuous log-coordinate relative position bias motivated by Swin-V2) image classification model. Trained in `timm` on ImageNet-1k by Ross Wightman. ImageNet-1k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program. | b30d8beb72925ee45e985a055dacc4cc |
apache-2.0 | ['image-classification', 'timm'] | false | Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 15.1 - GMACs: 2.6 - Activations (M): 20.3 - Image size: 224 x 224 - **Papers:** - CoAtNet: Marrying Convolution and Attention for All Data Sizes: https://arxiv.org/abs/2201.03545 - Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883 - **Dataset:** ImageNet-1k | c624c1234e9bbf62a01432a6fd264dcd |
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('coatnet_rmlp_nano_rw_224.sw_in1k', pretrained=True) model = model.eval() | e441561d7d6abf470b000b8753f8da78 |
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( 'coatnet_rmlp_nano_rw_224.sw_in1k', pretrained=True, features_only=True, ) model = model.eval() | 8dda246f42dd2ee9057cf586538a4548 |
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( 'coatnet_rmlp_nano_rw_224.sw_in1k', pretrained=True, num_classes=0, | a408fd18d24065140c213692fb856f27 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 2524 - mixed_precision_training: Native AMP | c326bc665898f5be7a8f53b3f7f1b3a7 |
apache-2.0 | ['generated_from_trainer'] | false | Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True, 'skip_tokens': 2969174016}, 'generation': {'batch_size': 128, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'bad_words_ids': [[32769]], 'do_sample': True, 'eos_token_id': 0, 'max_length': 640, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_hits_threshold': 0, 'num_samples': 4096, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>', 'should_insert_prefix': True}, 'model': {'from_scratch': False, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'model_kwargs': {'revision': '9cdfa11a07b00726ddfdabb554de05b29d777db3'}, 'num_additional_tokens': 2, 'path_or_name': 'kejian/grainy-pep8'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 128, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/nearest-pep8', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0001, 'logging_first_step': True, 'logging_steps': 10, 'num_tokens': 3300000000.0, 'output_dir': 'training_output_2', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5034, 'save_strategy': 'steps', 'seed': 42, 'tokens_already_seen': 2969174016, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} | 5beed08e451d5c47ec13a880a2d014c0 |
apache-2.0 | ['refugiados'] | false | Model Description <!-- Provide a longer summary of what this model is/does. --> Model for Saturdays.IA - **Developed by:** More information needed - **Shared by [Optional]:** More information needed - **Model type:** Language model - **Language(s) (NLP):** es - **License:** apache-2.0 - **Parent Model:** More information needed - **Resources for more information:** More information needed | 1b1de35c8a47b88501e3b41a67306e36 |
apache-2.0 | ['refugiados'] | false | Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> | 93ec7f5483df0131ccd4d40d10ff017f |
apache-2.0 | ['refugiados'] | false | Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> | 0a976b0bfaa5072cd02c7ac5ceaec3ce |
apache-2.0 | ['refugiados'] | false | Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> More information on training data needed | 652427f2aad4f440dc4acf8975ab4a46 |
apache-2.0 | ['refugiados'] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed | 55e9068433c2ac7d3e0ccde0caa986b7 |
apache-2.0 | ['refugiados'] | false | Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** More information needed **APA:** More information needed | 9ccf1b1a4098414dd2be21005cadb40e |
apache-2.0 | ['refugiados'] | false | Model Card Authors [optional] <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> More information needed | c1249cd4af623a9664d91c9fb73d7b82 |
apache-2.0 | ['automatic-speech-recognition', 'zh-CN'] | false | exp_w2v2t_zh-cn_vp-es_s869 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 (zh-CN)](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. | 513370e49bf154dbd2b5be54648202e6 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-hi-mr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1942 - F1: 0.8710 | 6bfd9078c26ead0a9476054e5ca76993 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4628 | 1.0 | 417 | 0.2603 | 0.8062 | | 0.2064 | 2.0 | 834 | 0.1951 | 0.8492 | | 0.1289 | 3.0 | 1251 | 0.1942 | 0.8710 | | b8d230166c45d6a0f5622f42b897ec4b |
mit | ['roberta-base', 'roberta-base-epoch_75'] | false | RoBERTa, Intermediate Checkpoint - Epoch 75 This model is part of our reimplementation of the [RoBERTa model](https://arxiv.org/abs/1907.11692), trained on Wikipedia and the Book Corpus only. We train this model for almost 100K steps, corresponding to 83 epochs. We provide the 84 checkpoints (including the randomly initialized weights before the training) to provide the ability to study the training dynamics of such models, and other possible use-cases. These models were trained in part of a work that studies how simple statistics from data, such as co-occurrences affects model predictions, which are described in the paper [Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions](https://arxiv.org/abs/2207.14251). This is RoBERTa-base epoch_75. | 7593eaca72acb01d09bbb21c47fc8467 |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | Model Description This is a RoBERTa model pre-trained on 青空文庫 texts with [Japanese-LUW-Tokenizer](https://github.com/KoichiYasuoka/Japanese-LUW-Tokenizer). You can fine-tune `roberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora-ud-goeswith), and so on. | a225a438dad7f9c72de380aacbcbc7ac |
cc-by-sa-4.0 | ['japanese', 'masked-lm'] | false | How to Use ```py from transformers import AutoTokenizer,AutoModelForMaskedLM tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora") model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-large-japanese-aozora") ``` | ea2fe7396e8f193d0a61d2a2a170761c |
apache-2.0 | ['Summarization', 'generated_from_trainer'] | false | t5-finetuned-amazon-english This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 3.1713 - Rouge1: 19.1814 - Rouge2: 9.8673 - Rougel: 18.1982 - Rougelsum: 18.2963 | a311a553c9929ead41fd3834bd4f8d56 |
apache-2.0 | ['Summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.3583 | 1.0 | 771 | 3.2513 | 16.6865 | 9.0598 | 15.8299 | 15.8472 | | 3.1022 | 2.0 | 1542 | 3.2147 | 16.8499 | 9.4849 | 16.1568 | 16.2437 | | 3.0067 | 3.0 | 2313 | 3.1718 | 16.9516 | 8.762 | 16.104 | 16.2186 | | 2.9482 | 4.0 | 3084 | 3.1854 | 18.9582 | 9.5416 | 18.0846 | 18.2938 | | 2.8934 | 5.0 | 3855 | 3.1669 | 18.857 | 9.934 | 17.9027 | 18.0272 | | 2.8389 | 6.0 | 4626 | 3.1782 | 18.6736 | 9.326 | 17.6943 | 17.8852 | | 2.8174 | 7.0 | 5397 | 3.1709 | 18.4342 | 9.6936 | 17.5714 | 17.6516 | | 2.8 | 8.0 | 6168 | 3.1713 | 19.1814 | 9.8673 | 18.1982 | 18.2963 | | 6827b7d05531fc64132ae452b219c6c3 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_text_conc_overfit This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.0273 - Validation Loss: 2.0426 - Epoch: 19 | e8f31e8ada89bbc1145024aedbe21fb9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1269 | 2.0771 | 0 | | 2.1136 | 2.0757 | 1 | | 2.1167 | 2.0427 | 2 | | 2.1109 | 2.0339 | 3 | | 2.0844 | 1.9720 | 4 | | 2.0713 | 2.0379 | 5 | | 2.0546 | 1.9741 | 6 | | 2.0215 | 2.0126 | 7 | | 2.0196 | 2.0414 | 8 | | 2.0196 | 2.0455 | 9 | | 2.0374 | 2.0087 | 10 | | 2.0238 | 1.9891 | 11 | | 2.0186 | 2.0296 | 12 | | 2.0117 | 2.0892 | 13 | | 2.0129 | 1.9999 | 14 | | 2.0377 | 1.9766 | 15 | | 2.0220 | 1.9925 | 16 | | 2.0296 | 2.0060 | 17 | | 2.0365 | 2.0009 | 18 | | 2.0273 | 2.0426 | 19 | | 41826a561e76265326de21ea23f1a1be |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-11 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0827 - Wer: 1.0 | 4e1b1448d09193418695fbdc4da6ca9e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 24 - eval_batch_size: 6 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 30 | a1c4cffa815eb63ab8c587bfd28ccbec |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 4.2589 | 1.18 | 200 | 3.1595 | 1.0 | | 2.8683 | 2.35 | 400 | 3.1270 | 1.0 | | 2.8692 | 3.53 | 600 | 3.1041 | 1.0 | | 2.8577 | 4.71 | 800 | 3.0804 | 1.0 | | 2.8587 | 5.88 | 1000 | 3.0556 | 1.0 | | 2.8615 | 7.06 | 1200 | 3.1084 | 1.0 | | 2.8598 | 8.24 | 1400 | 3.0608 | 1.0 | | 2.8571 | 9.41 | 1600 | 3.0997 | 1.0 | | 2.8595 | 10.59 | 1800 | 3.1533 | 1.0 | | 2.8568 | 11.76 | 2000 | 3.0621 | 1.0 | | 2.8563 | 12.94 | 2200 | 3.1072 | 1.0 | | 2.8556 | 14.12 | 2400 | 3.1299 | 1.0 | | 2.8581 | 15.29 | 2600 | 3.0565 | 1.0 | | 2.8534 | 16.47 | 2800 | 3.0821 | 1.0 | | 2.857 | 17.65 | 3000 | 3.0734 | 1.0 | | 2.8545 | 18.82 | 3200 | 3.1392 | 1.0 | | 2.8568 | 20.0 | 3400 | 3.0541 | 1.0 | | 2.8519 | 21.18 | 3600 | 3.0856 | 1.0 | | 2.8542 | 22.35 | 3800 | 3.1477 | 1.0 | | 2.8565 | 23.53 | 4000 | 3.0433 | 1.0 | | 2.8525 | 24.71 | 4200 | 3.0826 | 1.0 | | 2.8538 | 25.88 | 4400 | 3.0972 | 1.0 | | 2.857 | 27.06 | 4600 | 3.0762 | 1.0 | | 2.8523 | 28.24 | 4800 | 3.0828 | 1.0 | | 2.8526 | 29.41 | 5000 | 3.0827 | 1.0 | | 48437f724ac65b95582f77035a5d179a |
mit | ['RoBERTa'] | false | 模型分类 Model Taxonomy | 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra | | :----: | :----: | :----: | :----: | :----: | :----: | | 通用 General | 自然语言理解 NLU | RoBERTa | RoBERTa | 390M | 中文 Chinese | | 846ea5f000eb59d17ed582ad5a69f8c5 |
mit | ['RoBERTa'] | false | 模型信息 Model Information 参考论文：[RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 为了得到一个中文版的autohome-roberta-large（390M），我们用autohome口碑板块语料库(1.2G)进行二次预训练。模型初始化参数采用hfl/chinese-bert-wwm-ext-large的参数进行初始化，我们在MLM中使用了全词掩码(wwm)的方式。具体地，我们在二次预训练阶段中使用了[transformers框架](https://github.com/huggingface/transformers)大概花费了4张A100约11小时。 | 84595405b4e7605efccfd5a7a5691d1b |
mit | ['RoBERTa'] | false | 使用 Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('ChaosW/autohome-roberta-large') model=AutoModelForMaskedLM.from_pretrained('ChaosW/autohome-roberta-large') text = '生活的真谛是[MASK]。' fillmask_pipe = FillMaskPipeline(model, tokenizer, device=0) print(fillmask_pipe(text, top_k=10)) ``` | f515cdf87fcea5e92521e65fbb3ba631 |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Web_browser-clustered This model is a fine-tuned version of [nandysoham16/20-clustered_aug](https://huggingface.co/nandysoham16/20-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1934 - Train End Logits Accuracy: 0.9861 - Train Start Logits Accuracy: 0.9167 - Validation Loss: 0.2436 - Validation End Logits Accuracy: 0.6667 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | df8d58cb736950887b886ec9bc45dcd6 |
mit | ['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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.1934 | 0.9861 | 0.9167 | 0.2436 | 0.6667 | 1.0 | 0 | | 93c2e7c49dc07221d82fa1b9fe29fd0a |
other | ['vision', 'image-classification'] | false | MobileViT (extra small-sized model) MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari, and first released in [this repository](https://github.com/apple/ml-cvnets). The license used is [Apple sample code license](https://github.com/apple/ml-cvnets/blob/main/LICENSE). Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team. | e13b29edb0001c1f0726611bd361d5cf |
other | ['vision', 'image-classification'] | false | Model description MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches before it is processed by the transformer layers. Afterwards, the patches are "unflattened" back into feature maps. This allows the MobileViT-block to be placed anywhere inside a CNN. MobileViT does not require any positional embeddings. | 6fd2cd38a47b638e8b17452049f766cf |
other | ['vision', 'image-classification'] | false | How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import MobileViTFeatureExtractor, MobileViTForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/mobilevit-x-small") model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-x-small") inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits | cc0e570293f9e610e5ef9ecd2fdf8b5d |
other | ['vision', 'image-classification'] | false | params | URL | |------------------|-------------------------|-------------------------|-----------|-------------------------------------------------| | MobileViT-XXS | 69.0 | 88.9 | 1.3 M | https://huggingface.co/apple/mobilevit-xx-small | | **MobileViT-XS** | **74.8** | **92.3** | **2.3 M** | https://huggingface.co/apple/mobilevit-x-small | | MobileViT-S | 78.4 | 94.1 | 5.6 M | https://huggingface.co/apple/mobilevit-small | | 9729fafa8b27b10c870e71958df096d9 |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  | 5dd04033886062526d0adebe5cd3bb7f |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | whisper-medium-mn-10 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2103 - Wer: 21.2585 - Cer: 6.8756 | 511dc6cd658889f4f27df4ea220574fd |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | 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: 500 - training_steps: 40000 - mixed_precision_training: Native AMP | b454c1c8f47eea7571282a83fc92296b |
apache-2.0 | ['whisper-event', 'hf-asr-leaderboard', 'generated_from_multiple_datasets'] | false | Training results | Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:-------:|:---------------:|:-------:| | 0.4197 | 0.09 | 1000 | 19.0947 | 0.4462 | 53.9600 | | 0.3288 | 0.17 | 2000 | 14.8016 | 0.3468 | 44.2102 | | 0.2737 | 0.26 | 3000 | 12.3471 | 0.3020 | 36.1700 | | 0.2558 | 0.35 | 4000 | 11.7171 | 0.2824 | 34.1709 | | 0.2406 | 0.43 | 5000 | 10.3551 | 0.2594 | 31.1230 | | 0.218 | 0.52 | 6000 | 9.7815 | 0.2452 | 29.6865 | | 0.2253 | 0.61 | 7000 | 9.6712 | 0.2344 | 29.2932 | | 0.2071 | 0.69 | 8000 | 9.4261 | 0.2283 | 28.5067 | | 0.2051 | 0.78 | 9000 | 9.0656 | 0.2224 | 27.4033 | | 0.2064 | 0.87 | 10000 | 8.7851 | 0.2138 | 26.7206 | | 0.193 | 0.95 | 11000 | 8.5021 | 0.2089 | 25.5790 | | 0.1577 | 1.04 | 12000 | 8.2873 | 0.2072 | 25.6118 | | 0.1397 | 1.13 | 13000 | 8.2368 | 0.2046 | 25.1147 | | 0.1526 | 1.21 | 14000 | 8.7615 | 0.2065 | 26.4638 | | 0.1497 | 1.3 | 15000 | 0.2004 | 24.4866 | 7.9588 | | 0.1569 | 1.39 | 16000 | 0.1990 | 24.2244 | 7.9554 | | 0.1416 | 1.47 | 17000 | 0.2001 | 24.2298 | 7.8754 | | 0.1371 | 1.56 | 18000 | 0.1932 | 23.6072 | 7.8072 | | 0.1379 | 1.65 | 19000 | 0.1916 | 23.1320 | 7.5452 | | 0.1305 | 1.73 | 20000 | 0.1880 | 23.1101 | 7.4290 | | 0.1395 | 1.82 | 21000 | 0.1877 | 22.9845 | 7.4635 | | 0.1418 | 1.91 | 22000 | 0.1862 | 22.9080 | 7.5907 | | 0.1432 | 1.99 | 23000 | 0.1847 | 22.7114 | 7.4290 | | 0.0965 | 2.08 | 24000 | 0.1931 | 21.7391 | 7.0399 | | 0.0723 | 2.17 | 25000 | 0.1961 | 22.3236 | 7.2698 | | 0.0773 | 2.25 | 26000 | 0.1977 | 22.0505 | 7.0752 | | 0.0862 | 2.34 | 27000 | 0.1959 | 21.9522 | 7.0820 | | 0.0739 | 2.43 | 28000 | 0.1982 | 21.7719 | 7.1494 | | 0.0843 | 2.51 | 29000 | 0.1963 | 21.8921 | 7.1241 | | 0.0734 | 2.6 | 30000 | 0.1980 | 21.7883 | 7.1317 | | 0.0785 | 2.69 | 31000 | 0.1955 | 21.8757 | 7.1948 | | 0.0691 | 2.77 | 32000 | 0.1978 | 21.7446 | 7.0938 | | 0.0834 | 2.86 | 33000 | 0.1953 | 21.3240 | 7.0121 | | 0.0675 | 2.95 | 34000 | 0.1958 | 21.7719 | 7.0769 | | 0.042 | 3.03 | 35000 | 0.2053 | 21.3404 | 6.9624 | | 0.0474 | 3.12 | 36000 | 0.2097 | 21.5534 | 7.0306 | | 0.0428 | 3.21 | 37000 | 0.2107 | 21.3185 | 6.9809 | | 0.0343 | 3.29 | 38000 | 0.2111 | 21.3896 | 6.9514 | | 0.0378 | 3.38 | 39000 | 0.2103 | 21.2585 | 6.8756 | | 0.0361 | 3.47 | 40000 | 0.2106 | 21.3677 | 6.9009 | | c1c198afaf85211cec5a441810c135e3 |
apache-2.0 | ['generated_from_trainer'] | false | Tagged_Uni_50v4_NER_Model_3Epochs_AUGMENTED This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the tagged_uni50v4_wikigold_split dataset. It achieves the following results on the evaluation set: - Loss: 0.5415 - Precision: 0.2717 - Recall: 0.0754 - F1: 0.1180 - Accuracy: 0.8048 | d8398af0cc04592f3ce3eae303e62ba1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 25 | 0.6079 | 0.3333 | 0.0015 | 0.0029 | 0.7792 | | No log | 2.0 | 50 | 0.5345 | 0.2762 | 0.0678 | 0.1089 | 0.8022 | | No log | 3.0 | 75 | 0.5415 | 0.2717 | 0.0754 | 0.1180 | 0.8048 | | 8cc3bb4591666315135fe10b53af6289 |
apache-2.0 | ['generated_from_trainer'] | false | recipe-lr8e06-wd0.1-bs16 This model is a fine-tuned version of [paola-md/recipe-distilroberta-Is](https://huggingface.co/paola-md/recipe-distilroberta-Is) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2795 - Rmse: 0.5287 - Mse: 0.2795 - Mae: 0.4342 | aff963cd7dd0c19a7bd5a51f29648293 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rmse | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:| | 0.2767 | 1.0 | 1245 | 0.2745 | 0.5239 | 0.2745 | 0.4140 | | 0.2741 | 2.0 | 2490 | 0.2760 | 0.5253 | 0.2760 | 0.4222 | | 0.2729 | 3.0 | 3735 | 0.2795 | 0.5287 | 0.2795 | 0.4342 | | 47ca926d8b2e09645e30859717e15aa0 |
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