license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1600 - mixed_precis... | acd3ef3046968ec1c6c79f503896e7da |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6182 | 0.95 | 200 | 0.6224 | 53.6706 | | 0.4364 | 1.9 | 400 | 0.5200 | 47.2071 | | 0.3668 | 2.84 | 600 | 0.4830 | 44.489... | 7adf4bb6f6f493049786d2228d7a57af |
mit | ['generated_from_keras_callback'] | false | roberta-blm-tweets This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3333 - Train Accuracy: 0.8964 - Validation Loss: 1.5482 - Validation Accuracy: 0.6106 - Epoch: 7 | dca7d9da4e7bbf1ab7ea284ae1d69e9f |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 1.4279 | 0.4908 | 1.3684 | 0.5442 | 0 | | 1.1474 | 0.6375 | 1.2189 | 0.5796 ... | 6e792c69c00278646595f61ed0c2532a |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/mt5-base-ruquad-qg` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generat... | b06ff3e927189cf09bd1b2187d53254d |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers`... | 69347b9b3a960a5153d564d2eae8cc7b |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:------... | 051c95a7c658bc37d641a593605a8c63 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 32 - epoch: 16 - b... | 5e7c27f2e279c992c029ed4811220152 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'tr'] | false | Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 8.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) All `validated` split except `test` split was used for training.
| 7447dde2085039b0908a75f6640d2978 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'tr'] | false | Training procedure
To support the datasets above, custom pre-processing and loading steps was performed and [wav2vec2-turkish](https://github.com/mpoyraz/wav2vec2-turkish) repo was used for that purpose.
| fcb411d28cd0475ed50794490c584ddd |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'tr'] | false | Training hyperparameters
The following hypermaters were used for finetuning:
- learning_rate 2.5e-4
- num_train_epochs 20
- warmup_steps 500
- freeze_feature_extractor
- mask_time_prob 0.1
- mask_feature_prob 0.1
- feat_proj_dropout 0.05
- attention_dropout 0.05
- final_dropout 0.1
- activation_dropout 0.05... | c05eb57e62a78037505362525b465d62 |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'tr'] | false | Language Model
N-gram language model is trained on a Turkish Wikipedia articles using KenLM and [ngram-lm-wiki](https://github.com/mpoyraz/ngram-lm-wiki) repo was used to generate arpa LM and convert it into binary format.
| 2c31c899d763bc7929a6049576c97f7d |
apache-2.0 | ['automatic-speech-recognition', 'common_voice', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'tr'] | false | Evaluation Commands
Please install [unicode_tr](https://pypi.org/project/unicode_tr/) package before running evaluation. It is used for Turkish text processing.
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id mpoyraz/wav2vec2-xls-r-300m-cv8-turkish --data... | fbce8754477dad1c091d93fd52f7423a |
apache-2.0 | ['generated_from_trainer'] | false | demo_irony_42 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.2905 - F1: 0.6858 | 1bcd774a984afa40687ffffdd5a3e67f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.7735294032820418e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 96151dcc069fecbc8bd0734b3c36fa4b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 1.0 | 358 | 0.5872 | 0.6786 | | 0.5869 | 2.0 | 716 | 0.6884 | 0.6952 | | 0.3417 | 3.0 | 1074 | 0.9824 | 0.6995 | |... | eb2e7681ceea61ba1e09c9490d3c39a9 |
mit | ['generated_from_trainer'] | false | xlnet-base-cased-finetuned-qqp This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the qqp dataset (part of glue dataset). It achieves the following results on the evaluation set: - eval_loss: 0.27 - eval_accuracy: 0.9084 - eval_f1: 0.8775 - epoch: 3 | 22238da96a62af2903f876dc495cfaf0 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - weight_decay: 0.01 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | ea76da6894b570ade1adbc5f6c75923a |
creativeml-openrail-m | [] | false | N-Diffusion-v0.1 N-Diffusion-v0.1 is a latent text-to-image diffusion model. N-Diffusion-v0.1 is a fine-tuning model of waifu diffusion v1.3 with AI output images (t2i). <img src=https://i.imgur.com/wijixYv.png > <img src=https://i.imgur.com/dVknDwI.png > <img src=https://i.imgur.com/ZlyBGhq.png > <img src=https://i... | 679a4bfca4fc312272f59cf678d356be |
apache-2.0 | ['generated_from_trainer'] | false | opus-mt-de-en-finetuned-de-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt14 dataset. It achieves the following results on the evaluation set: - eval_loss: 1.3411 - eval_bleu: 32.4395 - eval_gen_len: 29.6925 - eval_runtime: 2250.04... | bb86d892923ab8f1b90048223a04401e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 11 - mixed_precision_training: Native AMP | 02a7295a5474aec34a641385d5298814 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Georgeart Dreambooth model trained by Alexwww with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-dif... | 3bdbf2f18181ef40cf8bd449873dc83c |
mit | ['generated_from_trainer'] | false | poetry-gpt2-large-no_schiller_2 This model is a fine-tuned version of [benjamin/gerpt2-large](https://huggingface.co/benjamin/gerpt2-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0976 | be82e44b6b97805187fe24dd42858dde |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7036 | 1.0 | 20061 | 3.3410 | | 3.3474 | 2.0 | 40122 | 3.0976 | | 64ab2acc8f0bf999255e178cf5b7c3da |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-mrpc-custom-tokenizer-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-mrpc-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-glue-mrpc-custom-tokenizer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4321 - Accuracy: 0.... | d746bff53be64cf1131b5a325c0afeea |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.5322 | 0.04 | 500 | 0.5216 | 0.7117 | 0.6856 | | 0.4868 | 0.09 | 1000 | 0.4842 | 0.7449 | 0.7042 | | 0.4808 |... | accf3a7beb1ab2d449c75a8db3c6b592 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech-ml_s947 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) 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... | 9d7a9c0576fbefe2ecafaa4a32ba798b |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1627 - Accuracy: 0.9464 | 8ddc61fcb02e77612e788656860d3138 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2486 | 0.98 | 36 | 0.2120 | 0.9100 | | 0.1844 | 1.98 | 72 | 0.3417 | 0.8563 | | 0.1646 | 2.98 | 108 | 0.1627 | 0.... | 29ad47a3c1dea6880d1f0d1ce3a16a64 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-multilingual-cased-finetuned-albanian-ner This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset. | 12545f566097bbab4a64bb56b87ff292 |
apache-2.0 | ['generated_from_trainer'] | false | Usage ```python from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-albanian-ner") model = AutoModelForTokenClassification.from_pretrained("Kushtrim/bert-base-multilingual-cased-finetuned-albanian-... | bd24b02c73982956da51796f3a484e3d |
wtfpl | ['stable-diffusion', 'text-to-image', 'embedding'] | false | Training Trained for 850 steps. 20 images, 6 vectors. Batch size of 3, 3 grad acc steps, learning rate of 0.0004:200, 0.0002:400, 0.0001:1000,0.00005     notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/The... | 5101b7bc69e5f33cdad2a2166e21c8a0 |
creativeml-openrail-m | ['text-to-image'] | false | jonny1 Dreambooth model trained by jonathan-reichardt with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/not... | 4d609c8a5bb4094caaa1c6202c91c44d |
apache-2.0 | ['conversational'] | false | Introduction Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question... | 1d9a66a566f84ce661c2f8d8bfa696c4 |
apache-2.0 | ['conversational'] | false | Citation Info ```text @article{ernie2.0, title = {PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable}, author = {Bao, Siqi and He, Huang and Wang, Fan and Wu, Hua and Wang, Haifeng}, journal={arXiv preprint arXiv:1910.07931}, year = {2019}, } ``` | 1d42f3fe7a5a50fc72b5047ea2a15333 |
mit | [] | false | Simple CNN-based Artist Classifier This repo contains a simple CNN-based Keras model which classifies images into one of 8 artistic trends. See also: `https://huggingface.co/jkang/drawing-artist-classifier` - The purpose of this model was for a quick prototyping - Data has been web-crawled using `https://github.com... | 970cf75c52e1ea79b6bceb65dcd66b70 |
mit | [] | false | How to use ```python import tensorflow as tf from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier") image_file = 'monet.jpg' img = tf.io.read_file(image_file) img = tf.io.decode_jpeg(img, channels=3) last_layer_activation, predictions = model(img[tf... | 984075d35a2083a49989ee3b786cc5ba |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-chinese-complaint-128 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3004 | 8c92e116b2a9b92ec02540f4955cc6f8 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 | 84272c94588a0e2188a2d120cfccbdc4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.3735 | 1.0 | 1250 | 2.4628 | | 2.2412 | 2.0 | 2500 | 2.0378 | | 1.9251 | 3.0 | 3750 | 1.8368 | | 1.7407 | 4.0 | 5000 | 1.6972 ... | 07c3529b956caf2074250adcd37ccaa0 |
apache-2.0 | ['generated_from_trainer'] | false | bert-large-cased-finetuned-lowR100-0-cased-DA-40 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9481 | 2f7952f0cd0a05c7a915022119262f45 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 1 | 1.8079 | | 2.0032 | 2.0 | 2 | 3.1228 | | 2.0032 | 3.0 | 3 | 1.9553 | | 1.9122 | 4.0 | 4 | 2.1789 ... | f8ab061f81f7b99452180af188806f98 |
apache-2.0 | ['roberta'] | false | Please use 'Roberta' related functions to load this model! This repository contains the resources in our paper **[Protest Stance Detection: Leveraging heterogeneous user interactions for extrapolation in out-of-sample country contexts]** *Ramon Villa-Cox, Evan Williams, Kathleen M. Carley* We pre-trained a BERT la... | 1c42de440d8bb3748662271b78b6cc6f |
apache-2.0 | ['generated_from_trainer'] | false | mobilebert_sa_GLUE_Experiment_logit_kd_data_aug_wnli_256 This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5755 - Accuracy: 0.1268 | d8cd96889ba4312cfec2f832119027eb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.337 | 1.0 | 435 | 0.5755 | 0.1268 | | 0.3007 | 2.0 | 870 | 0.5814 | 0.1127 | | 0.2921 | 3.0 | 1305 | 0.6514 | 0.... | cc5fdef5f34d313e6209a1fbd741317e |
apache-2.0 | ['generated_from_trainer'] | false | bert-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0474 - Precision: 0.8185 - Recall: 0.8754 - F1: 0.8460 - Accuracy: 0.9837 | df06d950d99573e514ff479f19b5d98c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0548 | 1.0 | 2112 | 0.0526 | 0.7847 | 0.8358 | 0.8094 | 0.9822 | | 0.0413 | 2.0 |... | 2381355036135220f9afd6e23fc3b509 |
apache-2.0 | [] | false | How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch mode_name = 'liam168/chat-DialoGPT-small-en' tokenizer = AutoTokenizer.from_pretrained(mode_name) model = AutoModelForCausalLM.from_pretrained(mode_na... | 323ecf1d34bd723d10db54e06272da4f |
apache-2.0 | ['code completion', 'code generation'] | false | NLGP natural model The NLGP natural model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **context*... | 42d601c685528c51277ea5cc2f37490f |
apache-2.0 | ['code completion', 'code generation'] | false | preprocessing functions num_spaces = [2, 4, 6, 8, 10, 12, 14, 16, 18] def preprocess(context, query): """ Encodes context + query as a single string and replaces whitespace with special tokens <|2space|>, <|4space|>, ... """ input_str = f"{context}\n{query} <|endofcomment|>\n" indentation_symb... | c2bed0d68425e43f147d8ecbec31d289 |
apache-2.0 | ['code completion', 'code generation'] | false | total = input + output input_and_output = model.generate( input_ids=input_ids, max_length=total_max_length, min_length=10, do_sample=False, num_beams=4, early_stopping=True, eos_token_id=tok.encode("<|cell|>")[0] ) output = input_and_output[:, input_ids.shape[-1]:] | 3accf465cfbe289cbb27072d1c2b95ea |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1000k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 0, Step 1000k MultiBERTs is a collection of checkpoints and a statistical library to support robust research on BERT. We provide 25 BERT-base models trained with similar hyper-parameters as [the original BERT model](https://github.com/google-research/bert) but with different... | f186dfe76904f57641c569f32b56428e |
apache-2.0 | ['multiberts', 'multiberts-seed_0', 'multiberts-seed_0-step_1000k'] | false | How to use Using code from [BERT-base uncased](https://huggingface.co/bert-base-uncased), here is an example based on Tensorflow: ``` from transformers import BertTokenizer, TFBertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_0-step_1000k') model = TFBertModel.from_pretrained("google/multib... | 2fc4187cedae4fbc8661f4a3cf1daf0c |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 0.6864 - Wer: 0.6707 | 7618e251f669df80a294b0a1520c11a5 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200.0 - mixed_precis... | 3194002afd22fd567c342f89649fb2ea |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 4.3322 | 14.81 | 400 | 3.7450 | 1.0 | | 3.2662 | 29.63 | 800 | 3.2571 | 0.9996 | | 1.6408 | 44.44 | 1200 | 0.9098 | 0.816... | 58365579713f42ba38b20c793c240d8c |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-de-en-lr2e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.0115 - Bleu: 9.12 - Gen Len: 17.4026 | f55d9b0f9701d5e81414b7332b0fd44e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 4eff966b2775ebaa6cd21ba6eedea357 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 188 | 2.0701 | 8.1225 | 17.4542 | | No log | 2.0 | 376 | 2.0316 | 8.5741 | 17.4229 | | 2.2224 | 3.0... | 2ccfbe54d4e1691e23782b0d80125f8a |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on heart1ohr2x9e to apply classification on target **Metrics of the best model:** accuracy 0.885854 average_precision 0.949471 roc_auc 0.050633 recall_macro 0.885324 f1_macro 0.885610 Name: LogisticRegression(class_weight='balanced', max_ite... | eeb2b742911a063d61d96a6cf0b1a7fe |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.co... | f26ecf9015e21dd1aa8b9343ba73ed57 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless age False False False ... False False False sex False False False ... False False False cp False False Fals... | 1e2eb1192b2ec0c96e07fd7f7dea4c14 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wra... | 926251b38e9a961abbdfd0b180f0a027 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">Eas... | 93df4dc7dc34511cca1b7e90c8b4880c |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_stsb_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.2840 - Pearson: 0.0810 - Spearmanr: 0.0645 - Combined Score: 0.0728 | 5cc7ae49b745c272c898b78475810e85 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 4.1199 | 1.0 | 23 | 2.4288 | 0.0129 | 0.0100 | 0.0114 | | 2.1794 | 2.0 | 46 ... | 0bbffc786932f7d4084c47779712bdc8 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased__sst2__train-8-0 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Accuracy: 0.5189 | 5003ded0190a97009ec8de2268e4e28c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6916 | 1.0 | 3 | 0.7035 | 0.25 | | 0.6852 | 2.0 | 6 | 0.7139 | 0.25 | | 0.6533 | 3.0 | 9 | 0.7192 | 0.... | 39f77716ce190a8a489a627a470cbba2 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4253 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9226 | 84c17cd65bbb6a0cfcc7103ca050e0b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 15 | 0.4677 | 0.0 | 0.0 | 0.0 | 0.9226 | | No log | 2.0 | 30 | 0... | 6c9c12d2e8aee1c087464c19a544581c |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: IPU - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - total_eval_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and ... | b16d100e839338452dbdc78802328336 |
mit | [] | false | cumbia peruana on Stable Diffusion This is the `<cumbia-peru>` 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... | 73d6c31ac516a743c6e79777da80702c |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | wav2vec2-base-keyword-spotting This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0746 - Accuracy: 0.9843 | a5b8b80bf57f59b7eeabfd28880940d1 |
apache-2.0 | ['audio-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.8279 | 1.0 | 399 | 0.6792 | 0.8558 | | 0.2961 | 2.0 | 798 | 0.1383 | 0.9798 | | 0.2069 | 3.0 | 1197 | 0.0972 | 0.... | bc8cfdf5e18284fb7993947583420fed |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | DreamBooth model for the clashgoblin concept trained by GV05 on the GV05/dreambooth-hackathon-images dataset. This is a Stable Diffusion model fine-tuned on the clashgoblin concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of clashgoblin person** This model was created as part of ... | 1d7754989004f41f9e2b6c0e220302b8 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'wildcard'] | false | Usage ```python from diffusers import StableDiffusionPipeline prompt = "clashgoblin person Portrait, by Jakub Rozalski." pipeline = StableDiffusionPipeline.from_pretrained('GV05/clashgoblin-person') image = pipeline(prompt, guidance_scale=9).images[0] image ``` | d77a338a47ff16d7e503834eb8487af9 |
apache-2.0 | ['generated_from_trainer'] | false | 20split_dataset_version3 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8310 | f2fa94a50369d0025e9e587edc43bf8c |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 | f45227cf19c77a4f8d2a3eabf56e9c20 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.1679 | 1.0 | 313 | 2.9768 | | 2.9869 | 2.0 | 626 | 2.9299 | | 2.8528 | 3.0 | 939 | 2.9176 | | 2.7435 | 4.0 | 1252 | 2.9104 ... | e6e7ef26adcb710b6cbc1e8683232ca6 |
apache-2.0 | ['generated_from_trainer'] | false | beit-base-patch16-224-pt22k-ft22k-finetuned-mnist This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the mnist dataset. It achieves the following results on the evaluation set: - Loss: 0.0202 - Accuracy: 0.9935 | bb46c7dd2ac716ae41db8815d3d4692f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3376 | 1.0 | 937 | 0.0446 | 0.9855 | | 0.318 | 2.0 | 1874 | 0.0262 | 0.9916 | | 0.2374 | 3.0 | 2811 | 0.0202 | 0.... | 388729acbe06fb1240b174dfb771ed3f |
mit | ['generated_from_trainer'] | false | predict-perception-bertino-cause-none This model is a fine-tuned version of [indigo-ai/BERTino](https://huggingface.co/indigo-ai/BERTino) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1988 - R2: 0.4467 | 6409fcc76dba0b7269ac0bfb306ae6b6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | R2 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.56 | 1.0 | 14 | 0.3460 | 0.0372 | | 0.3752 | 2.0 | 28 | 0.3082 | 0.1423 | | 0.147 | 3.0 | 42 | 0.2299 | 0.3603 | |... | 8b4234513dddea4b59382c7572d95ccf |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-prueba This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv3 dataset. It achieves the following results on the evaluation set: - Loss: 3.3077 | 7d173465790560bc5466c599846c7899 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 8 | 3.3077 | | No log | 2.0 | 16 | 3.3077 | | No log | 3.0 | 24 | 3.3077 | | No log | 4.0 | 32 | 3.3077 ... | 8545dd0fddd18c2ac84faba264b096c0 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'efficient', 'ilpost', 'summarization'] | false | IT5 Cased Small Efficient EL32 for News Summarization ✂️🗞️ 🇮🇹 *Shout-out to [Stefan Schweter](https://github.com/stefan-it) for contributing the pre-trained efficient model!* This repository contains the checkpoint for the [IT5 Cased Small Efficient EL32](https://huggingface.co/it5/it5-efficient-small-el32) model... | 060c0db5fb71b6cc414698437e5484d4 |
apache-2.0 | ['italian', 'sequence-to-sequence', 'fanpage', 'efficient', 'ilpost', 'summarization'] | false | Using the model Model checkpoints are available for usage in Tensorflow, Pytorch and JAX. They can be used directly with pipelines as: ```python from transformers import pipelines newsum = pipeline("summarization", model='it5/it5-efficient-small-el32-news-summarization') newsum("Dal 31 maggio è infine partita la pi... | a533740a68efa4c2a53bd4a26413f3df |
apache-2.0 | ['generated_from_keras_callback'] | false | kabelomalapane/Helsinki-NLP-opus-finetuned-en-to-zu This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-mul](https://huggingface.co/Helsinki-NLP/opus-mt-en-mul) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.5907 - Validation Loss: 1.6321 - Epoch: 2 | 73f109435beb2decacd4d9a2b9833841 |
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': 5e-05, 'decay_steps': 783, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': ... | c2000b2d28df0d99be57f76acc0838d7 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1622 | 1.7379 | 0 | | 1.7292 | 1.6529 | 1 | | 1.5907 | 1.6321 | 2 | | 139b3345bf80091b7671af23644c1ec2 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Sample images - 1500 steps model    notebook. You can al... | 6619b575e798e907837899d3fc5b1f0a |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | t5-base-TEDxJP-0front-1body-8rear This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4672 - Wer: 0.1759 - Mer: 0.1698 - Wil: 0.2574 - Wip: 0.7426 - Hits: 55537 - S... | ef34185e820522f1ffc18c6ae4783ffd |
cc-by-sa-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:| | 0.6417 ... | 7048b484d402b305a081a220a9376042 |
creativeml-openrail-m | [] | false | This is a fine-tuned version of the [Stable Diffusion model available from KerasCV](https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion). Fine-tuning code is available [here](https://github.com/sayakpaul/stabe-diffusion-keras-ft). The weights provided in this repository are **only for r... | f8109266fddced239b5eb6b5246af918 |
mit | ['generated_from_trainer'] | false | kobart_8_3e-5_datav2_min30_lp5.0_temperature1.0 This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7126 - Rouge1: 36.1419 - Rouge2: 13.0561 - Rougel: 23.9016 - Bleu1: 30.1... | 910a21ee96a25bc258a2559a7b83a6b5 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5.0 | f6a44b5ab6cb198f25984406968dad67 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|:------:|:-------:| | 2.6085 | 0.19 | 1000 | 3.085... | 956a9fb14621761b8851c0a9dc4c32a5 |
mit | [] | false | Ilya-Shkipin on Stable Diffusion This is the `<ilya-shkipin-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. Yo... | b863bec4c30165a7d509bd1f1663b52a |
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