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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4061 | 6.41 | 500 | 1.4142 | 0.6282 | | 0.0518 | 12.82 | 1000 | 2.5075 | 0.6101 | | 0.0239 | 19.23 | 1500 | 2.7691 | 0.... | 2de498b807d8419cd8667d2e5af9fa2d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | wav2vec2-large-xlsr-53-german-cv8-dropout This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - DE dataset. It achieves the following results on the evaluation set: - Loss: 0.1111 - Wer: 0.1117 | 7275ef9e7af0a053844ce9f4e0f02b5b |
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: 0.0003 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sc... | 09751d08a2df48caa91ec7edb1ab4324 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.2081 | 1.0 | 6815 | 0.1784 | 0.1910 | | 0.1686 | 2.0 | 13630 | 0.1621 | 0.1725 | | 0.1515 | 3.0 | 20445 | 0.1569 | 0.164... | d1ca664fd07fff7bcd2a8d2901ddaeb5 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Kannada Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Kannada using the [OpenSLR SLR79](http://openslr.org/79/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. | 2ac9fdd66f9a233bab09056f2066f137 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows, assuming you have a dataset with Kannada `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 51175cfbbaf91466a373ce1ca647aacc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For a sample, see the Colab link in Training Section. processor = Wav2Vec2Processor.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn") model = Wav2Vec2ForCTC.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn") resampler = torchaudio.transforms.Resample(48_000, 16_... | 2607c700cf9ffada8e0404b95a43d195 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["spee... | 724ed7e00557c8ed20202729d51a3b0c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on 10% of the Kannada data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re | 4ff92f61329eaeaafb38eb11f16bddcc |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | TODO: WRITE YOUR CODE TO LOAD THE TEST DATASET. For sample see the Colab link in Training Section. wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn") model = Wav2Vec2ForCTC.from_pretrained("amoghsgopadi/wav2vec2-large-xlsr-kn") model.to("cuda") chars_to_igno... | 9c2858636b38b1315cd73d52934c396d |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_... | 3d67b124843f33ccf632adb2f1ff929b |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training 90% of the OpenSLR Kannada dataset was used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/github/amoghgopadi/wav2vec2-xlsr-kannada/blob/main/Fine_Tune_XLSR_Wav2Vec2_on_Kannada_ASR.ipynb). | 83d7c1fadd3ca2584afe09253f6102da |
cc-by-4.0 | ['sentence-transformers', 'feature-extraction', 'sentence-similarity', 'transformers'] | false | HindSBERT-STS This is a HindSBERT model (l3cube-pune/hindi-sentence-bert-nli) fine-tuned on the STS dataset. <br> Released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP <br> More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11... | aa58efe2b38269b6c22d32f249a8bd96 |
mit | ['generated_from_keras_callback'] | false | turkishReviews-ds-mini This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 8.3867 - Validation Loss: 8.3741 - Epoch: 2 | a6e61c0c033a052bb285bfc398d5144b |
mit | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps... | e547f4589c977211153f497e84375f8a |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 10.2149 | 9.6891 | 0 | | 9.0695 | 8.7610 | 1 | | 8.3867 | 8.3741 | 2 | | be31708bb8441c9bd638d7e216224b39 |
apache-2.0 | ['generated_from_trainer'] | false | IMDB_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2309 - Accuracy: 0.9333 | 443fbe4484d40df99708161cfed72fcc |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6706 | 0.03 | 50 | 0.5746 | 0.8533 | | 0.4323 | 0.06 | 100 | 0.2900 | 0.9 | | 0.314 | 0.1 | 150 | 0.2334 | 0.... | bff79033b752866bb45e74ba5a5c7d44 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 ```bash cd espnet git checkout da1a26652f7d5a019cc24ad1e0e6e844f2b57e1b pip install -e . cd egs2/aishell4/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model Dan_Berrebbi_aishell4_asr ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 5ad89920a3da396e8ebc24f0d0a9e6d4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Tue Sep 21 09:36:01 EDT 2021` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a1` - pytorch version: `pytorch 1.9.0` - Git hash: `7887faeabbc2299922267928e190ed89cb032a36` - Commit date: `Mon Sep 20 16:25:02 2021 -0400` | 2ec023e33021321d1ee057ad7c59f0a8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_nuit_valid.loss.ave_asr_model_valid.acc.ave/dev|599|601|6.8|92.7|0.5|0.0|93.2|93.2| |decode_transformer_lm_lm_nuit_valid.loss.ave_asr_model_valid.acc.ave/dev|599|601|6.8|92.8|0.3|0.0|93.2|93.2| | 955fcfae9901ae19cba4ac2def415542 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_asr_rnn_lm_lm_nuit_valid.loss.ave_asr_model_valid.acc.ave/dev|599|15936|66.9|25.6|7.5|9.8|42.9|93.2| |decode_transformer_lm_lm_nuit_valid.loss.ave_asr_model_valid.acc.ave/dev|599|15936|64.7|27.6|7.7|11.0|46.3|93.2| | e25563e916c264164a45afcc8cf57365 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_conformer5.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_fine_tune5_100ep ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: nul... | e6f5f5c892d8e49d921fc2898ded6504 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | > - 水 - 百 - 今 - 太 - 最 - 报 - 怎 - 才 - 等 - 及 - 关 - <-> - 肯 - 火 - 机 - 流 - 制 - 送 - 手 - 确 - 法 - 写 - 玩 - 传 - 路 - 班 - 查 - 招 - 卖 - 几 - 正 - 合 - 够 - 五 - 引 - 容 - 只 - 男 - 日 - 四 - 宣 - 反 - 两 - 清 - 处 - 周 - 单 - 首 - 课 - 衣 - 便 - 身 - 气 - 针 - 奶 - 六 - 经 - 接 - 女 - 育 - 鲜 - 赠 - 试 - 停 - 晚 - 类 - 故 - 入 - 性 - 增 - 食 - 满 - 格 - 基 - 备 - 洗 - 培 - 质 - 美 ... | c515e59bc8f1c43645f761c8374ac8c8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | LM config <details><summary>expand</summary> ``` config: conf/train_lm_transformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/lm_nuit ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null loc... | bba684c3873ee4ce10feb622910dab29 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | > - 水 - 百 - 今 - 太 - 最 - 报 - 怎 - 才 - 等 - 及 - 关 - <-> - 肯 - 火 - 机 - 流 - 制 - 送 - 手 - 确 - 法 - 写 - 玩 - 传 - 路 - 班 - 查 - 招 - 卖 - 几 - 正 - 合 - 够 - 五 - 引 - 容 - 只 - 男 - 日 - 四 - 宣 - 反 - 两 - 清 - 处 - 周 - 单 - 首 - 课 - 衣 - 便 - 身 - 气 - 针 - 奶 - 六 - 经 - 接 - 女 - 育 - 鲜 - 赠 - 试 - 停 - 晚 - 类 - 故 - 入 - 性 - 增 - 食 - 满 - 格 - 基 - 备 - 洗 - 培 - 质 - 美 ... | 65c59ad0642f40023a620d6249d4fe3f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 125 | 0.5023 | 0.8116 | | 2e94b980a69604edace5fe3ce6256831 |
apache-2.0 | ['generated_from_trainer'] | false | openai/whisper-medium.en This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1748 - Wer: 2.7097 | a695a1e07a48e3a7f83639640ac9d00c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0329 | 5.0 | 500 | 0.1343 | 4.0125 | | 0.0013 | 10.01 | 1000 | 0.1531 | 2.8810 | | 0.0002 | 15.01 | 1500 | 0.1609 | 2.7321 | |... | cfb1ee08da51c2ca8f34912e5a28a646 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola 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: 0.8434 - Matthews Correlation: 0.5567 | 15570f8c2a753c2841b357c46d959c99 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5224 | 1.0 | 535 | 0.5360 | 0.4275 | | 0.3498 | 2.0 | 1070 | 0.5205 | 0.5078 | | 0.2... | 58a16a66d0205656c1b52520267c48e9 |
apache-2.0 | ['generated_from_keras_callback'] | false | Arandine/bert-finetuned-squad 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: - Train Loss: 0.5695 - Epoch: 2 | ee19e7a26b15bbe9de6354c946fd317c |
mit | ['exbert'] | false | How to use Here is how to use the ONNX models of gpt2 to get the features of a given text: Example using transformers.pipelines: ```python from transformers import AutoTokenizer, pipeline from optimum.onnxruntime import ORTModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gpt2") model = ORTModelForCausal... | 137163cd54084a80a535ff6fc1c53c39 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Serbian wav2vec2-xls-r-300m-sr-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.7302 - Wer: 0.4825 - Cer: 0.1847 Evaluation on mozilla-foundatio... | c9789b534ccb2631595c39808fd471a5 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 800 - mixed_precision_t... | 27b1226b6cd8bc5362fc388af7d9300d |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'generated_from_trainer', 'robust-speech-event', 'xlsr-fine-tuning-week', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 5.6536 | 15.0 | 1200 | 2.9744 | 1.0 | 1.0 | | 2.7935 | 30.0 | 2400 | 1.6613 | 0.8998 | 0.4670 | | 1.6538 | 45.... | 3c5001f9f6f7e6ef4c94ec72b50d4d36 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola 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: 0.7994 - Matthews Correlation: 0.5422 | 49767004cfa29d0c0e312e2b4b3ae0d4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.42 | 1.0 | 535 | 0.4631 | 0.5242 | | 0.2823 | 2.0 | 1070 | 0.5755 | 0.5056 | | 0.1... | 62eeadb33468972600a93e5463878e46 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 415 | 5.6748 | | 6.3694 | 2.0 | 830 | 5.4214 | | 5.413 | 3.0 | 1245 | 5.3563 | | 9d4d9ed8a9d95a8c4ce37b89a0b82f49 |
mit | [] | false | Description This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022) We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-... | e13a53914814d67903b86a425d15c3e2 |
mit | [] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline model_name = 'raw-label-epoch-3' tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased") full_model_path = f'MartinoMensio/racism-models-{model_name}' model = AutoModelForSequenceClassi... | 60cae3f991e133b69f5784c2990f9345 |
apache-2.0 | ['generated_from_trainer'] | false | nmt-mpst-id-en-lr_1e-05-ep_10-seq_128_bs-32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9022 - Bleu: 0.0284 - Meteor: 0.1159 | 145f8ea2529aa537b732129cdc38ce17 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | No log | 1.0 | 202 | 3.2021 | 0.0126 | 0.0683 | | No log | 2.0 | 404 | 3.0749 | 0.0219 | 0.0958 | | 3.559 | 3.0 |... | c68d4c34e956bb8013c3dcdb9b2c78dc |
apache-2.0 | ['translation', 'wmt16', 'allenai'] | false | How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "allenai/wmt16-en-de-dist-12-1" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "Machine learning is great, isn't it?" input_ids = tokenizer.encode(in... | 13fa87657f0d1ec741efeb9431691b38 |
apache-2.0 | ['translation', 'wmt16', 'allenai'] | false | Eval results Here are the BLEU scores: model | fairseq | transformers -------|---------|---------- wmt16-en-de-dist-12-1 | 28.3 | 27.52 The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measur... | 4efe6cba1ca97a2a29ca15d2113e73cd |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3061 - Accuracy: 0.8733 - F1: 0.8742 | eba647d231aa529924d2bbcb6f20c481 |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-respirator 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: 0.2124 - Accuracy: 0.9082 | 79ba403befba3ea04094ef5a3b124598 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4872 | 0.98 | 37 | 0.2124 | 0.9082 | | 0.4828 | 1.98 | 74 | 0.2124 | 0.9082 | | 0.4772 | 2.98 | 111 | 0.2124 | 0.... | b8b7a54e815f50e347c33ee19ec5c793 |
mit | ['generated_from_trainer'] | false | wnli_roberta-base_144_v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Accuracy: 0.5634 | 525b23242c524b53eab37af7690bb3cb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2178 - Accuracy: 0.9285 - F1: 0.9289 | 417a1e62332de49bd338ccaeba6e5cf3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8227 | 1.0 | 250 | 0.3212 | 0.8985 | 0.8932 | | 0.2463 | 2.0 | 500 | 0.2178 | 0.9285 | 0.9289 | | 4a22785fc8aa7825c6558ae38d05d480 |
apache-2.0 | ['automatic-speech-recognition', 'ru'] | false | exp_w2v2t_ru_unispeech_s607 Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-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 this model, make sure that you... | 971ce719100fabb968d29efb046c7bd5 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-imdb-target-rotten_tomatoes This model is a fine-tuned version of [muhtasham/small-mlm-wikitext](https://huggingface.co/muhtasham/small-mlm-wikitext) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3909 - Accuracy: 0.8021 - F1: 0.8017 | 7f38f4bc800da22b3f35b3694a681337 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Model description CycleGAN for unpaired image-to-image translation. Given two image domains A and B, the following components are trained end2end to translate between such domains: - A generator A to B, named G_AB conditioned on an image from A - A generator B to A, named G_BA conditioned on an image from ... | e49f16945d67bf39edfd2e05081acf99 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | How to use ```python import os from PIL import Image from torchvision import transforms as T from torchvision.transforms import Compose, Resize, ToTensor, Normalize, RandomCrop, RandomHorizontalFlip from torchvision.utils import make_grid from torch.utils.data import DataLoader from huggan.pytorch.cyclegan.modeling_c... | 3af5e234d4660151305e1241ab6e4721 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | .detach().cpu(), nrow=1, normalize=True) out_transform = Compose([ T.Resize(orig_shape[:2]), T.ToPILImage() ]) return out_transform(out_img) n_channels = 3 image_size = 512 input_shape = (image_size, image_size) transform = Compose([ ... | b76354141c2854a06976e41f82daf117 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Limitations and bias Due to the absence of paired data, some background parts of the synthetic images are seldom wrongly translated, e.g. sky is translated to vegetation. Additional pretext tasks in parallel to the discriminative classifier of fake and real samples could improve the result. One easy improvement ... | 23a8feb443323cbc3026e4dcbe5d8942 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Training data The CycleGAN model is trained on an unpaired dataset of samples from synthetic and real driving data, respectively from the GTA5 and Cityscapes datasets. To this end, the synthetic-to-real dataset can be loaded by means of the function load_dataset in the huggingface library, as follows. ```python f... | c9a4acad6425804f78ec051e585eff44 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Preprocessing The following transformations are applied to each input sample of synthetic and real data. The input size is fixed to RGB images of height, width = 512, 512. This choice has been made in order to limit the impact of upsampling the translated images to higher resolutions. ```python n_channels = 3 image... | 73c9d9e63dcad47f21296f7ff9b3c513 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Hyperparameters The following configuration has been kept fixed for all translation models: - learning rate 0.0002 - number of epochs 200 - learning rate decay activation at epoch 100 - number of residual blocks of the cyclegan 9 - image size 512x512 - number of channels=3 - cycle loss weight 10.0 - identity los... | dfcdbe27fe3d30f131abb00bb1fd1a48 |
mit | ['conditional-image-generation', 'image-to-image', 'gan', 'cyclegan'] | false | Generated Images In the provided images, row0 and row2 represent the synthetic and real images from the respective datasets. Row1 is the translation of the immediate above images in row0(synthetic) by means of the G_AB translation model, to the real world style. Row3 is the translation of the immediate above imag... | 59bc67c9e1f32bf4591142c4049d8278 |
apache-2.0 | [] | false | Results: ``` ***** eval metrics ***** epoch = 3.0 eval_accuracy = 0.9094 eval_loss = 0.3514 eval_runtime = 0:00:03.60 eval_samples = 872 eval_samples_per_second = 242.129 eval_steps_per_second = 30.266 ``` | e5bd77330de0031e435b5362a32391ee |
mit | [] | false | spider-gwen on Stable Diffusion This is the `<spider-gwen>` 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 al... | 5be90ec6622198873c5a105d8b302090 |
apache-2.0 | ['image-classification', 'keras'] | false | Train a Vision Transformer on small datasets Author: [Jónathan Heras](https://twitter.com/_Jonathan_Heras) [Keras Blog](https://keras.io/examples/vision/vit_small_ds/) | [Colab Notebook](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/vit_small_ds.ipynb) In the academi... | 8121f8c3ef6a7f334ed49398e62a5552 |
apache-2.0 | ['image-classification', 'keras'] | false | ARCHITECTURE LAYER_NORM_EPS = 1e-6 TRANSFORMER_LAYERS = 8 PROJECTION_DIM = 64 NUM_HEADS = 4 TRANSFORMER_UNITS = [ PROJECTION_DIM * 2, PROJECTION_DIM, ] MLP_HEAD_UNITS = [ 2048, 1024 ] ``` I have used the `AdamW` optimizer with cosine decay learning schedule. You can find the entire implementation in th... | cdbb3a7a8cf726963064b16a2f053e58 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-multilingual-cased-finetuned-squad This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.2269 | b539e53ce971dc327ea799561fd2033c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.1694 | 1.0 | 5555 | 1.2091 | | 0.9263 | 2.0 | 11110 | 1.1691 | | 0.7769 | 3.0 | 16665 | 1.2269 | | 8f05b1e494c4f8e59b40f3d41a01d36e |
cc-by-4.0 | ['question generation'] | false | Model Card of `lmqg/t5-small-squad-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). | 112a8d30a26ae7f6184dc0e56206c158 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/t5-small-squad-qg") output =... | 9cdc3b7e7d7afb0ec006711b115be709 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/t5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:----------... | d58dfe8f82b0dc39852b2292e1145067 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 9 - batch: 6... | 1b38827c72c6781de5555274396f6e75 |
apache-2.0 | ['BERTweet'] | false | BERTweet-FA: A pre-trained language model for Persian (a.k.a Farsi) Tweets --- BERTweet-FA is a transformer-based model trained on 20665964 Persian tweets. The model has been trained on the data only for 1 epoch (322906 steps), and yet it has the ability to recognize the meaning of most of the conversational sentence... | 7690fcc9178dc2c5d03b31cbe470a046 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-300m-hindi-epochs15-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 3.5705 - Wer: 1.0 | 977b15129ca2bae0aa4441225ea2f405 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sch... | 0b647796db5bb6bcd85afbe11be7c3be |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 20.2764 | 5.53 | 50 | 8.1197 | 1.0 | | 5.2964 | 11.11 | 100 | 3.5705 | 1.0 | | 74ff2a9e0f13e0ce92074a07be6e0bc0 |
mit | ['roberta-base', 'roberta-base-epoch_59'] | false | RoBERTa, Intermediate Checkpoint - Epoch 59 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 ... | d76d3c6f77e67b065969f8354a4d70ae |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2r_es_xls-r_age_teens-5_sixties-5_s530 Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure th... | 36354806dd2c5c28f3f9d0d9e0222636 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small Vi - Shiv Kumar Ganesh This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7220 - Wer: 46.6769 | 106643686a8f4a7ff24383b2b1d66168 |
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: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 130683e8a1d49658e46d2d5463d2ec44 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7433 | 1.02 | 100 | 1.6824 | 155.0559 | | 0.5929 | 2.04 | 200 | 0.8475 | 55.5824 | | 0.1188 | 3.05 | 300 | 0.6646 | 47... | d88235d8a2c22cc83e1f08e00fcb3da9 |
mit | ['roberta-base', 'roberta-base-epoch_20'] | false | RoBERTa, Intermediate Checkpoint - Epoch 20 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 ... | ffac281dabf8222dbf3b02d2be58e65c |
cc-by-4.0 | [] | false | MalayalamBERT MalayalamBERT is a Malayalam BERT model trained on publicly available Malayalam monolingual datasets. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>] . Citing: ``` @article{joshi2022l3cubehind, title={L3Cub... | 528b7eaa039a024ed3c33e4263ff1463 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | UL2-base-nl36 for Finnish Pretrained T5 model on Finnish language using a UL2 (Mixture-of-Denoisers) objective. T5 model was introduced in [this paper](https://arxiv.org/abs/1910.10683) and first released at [this page](https://github.com/google-research/text-to-text-transfer-transformer). The UL2 objective was intro... | d9368f66dc3d04d928037b821c3e7ce0 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | t511) improvements compared to the original T5 model during the pretraining: - GEGLU activation in feed-forward hidden layer, rather than ReLU - see [here](https://arxiv.org/abs/2002.05202) - Dropout was turned off in pretraining (quality win). Dropout should be re-enabled during fine-tuning - Pretrained on self-superv... | 504111d99da4673fe95222bfcb1dcde7 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | How to use Here is how to use this model in PyTorch: ```python from transformers import T5Tokenizer, T5ForConditionalGeneration tokenizer = T5Tokenizer.from_pretrained("Finnish-NLP/ul2-base-nl36-finnish") model = T5ForConditionalGeneration.from_pretrained("Finnish-NLP/ul2-base-nl36-finnish") ``` and in TensorFlow:... | 029d3b048225ee10d5e4cc7b8aaeec11 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Pretraining The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps with a batch size of 64 (in total 33B tokens). The optimizer used was a AdaFactor with learning rate warmup for 10K steps with a constant learning rate of 1e-2, and the... | 30b674696c21e8ad1bb4e2e789df2417 |
apache-2.0 | ['finnish', 't5', 't5x', 'seq2seq', 'ul2'] | false | Evaluation results Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length... | 87444ecb480727eb11df712b422504dc |
mit | ['generated_from_keras_callback'] | false | ishaankul67/Wayback_Machine-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: 0.2638 - Train End Logits Accuracy: 0.9444 - Train Start Logit... | 4aa219a08ad339b3b95f5aef20cfa94a |
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 | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:----------... | 8ce10afdf70d0dba990cd0f7bc6a681e |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xlsr-53-demo1 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.9692 - Wer: 0.8462 | 8e1052b1950bb78e5e41b329668a8404 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 5 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 0695e421d884dd4fdb96afe63e33932d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.978 | 0.06 | 100 | 3.5377 | 1.0 | | 3.5026 | 0.13 | 200 | 3.4366 | 1.0 | | 3.4084 | 0.19 | 300 | 3.3831 | 1.0 | |... | 269dc20b2aa07a507356ec89039a942e |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli_384 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 - Accuracy: 0.5634 | e255a5ef3c3fecf9208f8b29ec857767 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3584 | 1.0 | 3 | 0.3470 | 0.5634 | | 0.3544 | 2.0 | 6 | 0.3488 | 0.4366 | | 0.348 | 3.0 | 9 | 0.3445 | 0.... | 6a26f81d256f8bd0b9ccfd95baef2579 |
apache-2.0 | ['generated_from_trainer'] | false | resnet-50-finetuned-eurosat This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.7123 - Accuracy: 0.5630 | 525cff9b975ce7a943d3ef384ea61fa1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7579 | 1.0 | 190 | 1.7123 | 0.5630 | | 285f4fd99e80955e335051049f7f9874 |
apache-2.0 | ['generated_from_keras_callback'] | false | whisper3_0010 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7159 - Train Accuracy: 0.0297 - Validation Loss: 0.7918 - Validation Accuracy: 0.0300 - Epoch: 9 | ba6ec12cddf044d9773a82d3ad53d11d |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 5.0832 | 0.0116 | 4.4298 | 0.0124 | 0 | | 4.3130 | 0.0131 | 4.0733 | 0.0141 ... | ccdcdd13aa13d3043e4549c472af24cb |
apache-2.0 | ['automatic-speech-recognition', 'de'] | false | exp_w2v2r_de_vp-100k_age_teens-0_sixties-10_s278 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 (de)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using t... | ebe53598adfdc11c4c609290d0a096c2 |
mit | ['generated_from_trainer'] | false | gpt2-finetuned-comp2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7788 - Precision: 0.3801 - Recall: 0.6854 - F1: 0.4800 - Accuracy: 0.4800 | f16631f7aaf4b534322c5302232fb454 |
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