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 | ['text generation', 'pytorch', 'causal-lm'] | false | ReGPT-125M-200G This model was trained on GPT-Neo-125M with [Mengzi Retrieval LM](https://github.com/Langboat/mengzi-retrieval-lm). For more details, please refer to this [document](https://github.com/Langboat/mengzi-retrieval-lm/blob/main/README.md). | 2a081a389e134b7945e0b38f7da4c884 |
apache-2.0 | ['text generation', 'pytorch', 'causal-lm'] | false | How to use You have to use a forked transformers: https://github.com/Langboat/transformers ```python from transformers import Re_gptForCausalLM model = Re_gptForCausalLM.from_pretrained('Langboat/ReGPT-125M-200G') ``` | 8c8dc3c9e285e8432df5595010423ba1 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Galverse-Diffusion-wf-8888 Dreambooth model trained by jarvissan 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: Base model: Waifu-Diffusion Traning data: 8888 Galverse PFPs (512x512) taged with gal_{n}.png Created by Jarvis (@jarvissan22) in Collobration with the galverse team @galverseNFT | 90dfecbb200e0b550157ce06f4ad8e7f |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Example images 1 Dragon gal breaving fire fullbody, draong scales, wings, tail, short red hair, purple eyes, pose from above  2 Vampire gal laughing, pink hair, black clothes, pail white skinm green eyes, heart lips, fullbody , high detail  3 Gal working as a delivery girl, working, running, while holding a package, fullbody, wearing brown cap and work clothes, wide  4 Gal gishing, hoolding a fishing rod, fishing, green hair, yellow eyes, in the style of galverse  | 4f44016e46d823794cc9e78c6799c18b |
apache-2.0 | ['t5', 'contrastive learning', 'ranking', 'decoding', 'metric learning', 'pytorch', 'text generation', 'retrieval'] | false | Method-2: Loading the model with HuggingFace APIs ``` from transformers import T5Tokenizer, AutoModel tokenizer = T5Tokenizer.from_pretrained(f"google/t5-v1_1-xl") model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-xl-pg19", trust_remote_code=True) ``` | 0861d7ee1adc06da1b98d78b1e9f1c3c |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | Ayaka_DB Dreambooth model trained by Falon 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-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | e8a25f8c941ea30837a682bde3505f19 |
apache-2.0 | ['automatic-speech-recognition', 'fr'] | false | exp_w2v2r_fr_vp-100k_gender_male-0_female-10_s469 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. | 54b20833297f0a427255890a70322eef |
mit | ['generated_from_trainer'] | false | pegasus-base-qag-bg-finetuned-punctuation-bg This model is a fine-tuned version of [rmihaylov/pegasus-base-qag-bg](https://huggingface.co/rmihaylov/pegasus-base-qag-bg) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0318 | 1344b4ee15d0365524df5c4b8ec09772 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.0563 | 1.0 | 4063 | 0.0279 | | 0.0301 | 2.0 | 8126 | 0.0260 | | 0.0227 | 3.0 | 12189 | 0.0259 | | 0.0178 | 4.0 | 16252 | 0.0281 | | 0.0145 | 5.0 | 20315 | 0.0290 | | 0.0122 | 6.0 | 24378 | 0.0300 | | 0.0105 | 7.0 | 28441 | 0.0305 | | 0.0095 | 8.0 | 32504 | 0.0318 | | e0d26be9de4f1dc72a4b93ac00622bb7 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | CRDNN with CTC/Attention and RNNLM trained on LibriSpeech This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on LibriSpeech (EN) within SpeechBrain. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The performance of the model is the following: | Release | Test clean WER | Test other WER | GPUs | |:-------------:|:--------------:|:--------------:|:--------:| | 05-03-21 | 2.90 | 8.51 | 1xV100 16GB | | 9d19d6391183dae880278c3926d2966a |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Pipeline description This ASR system is composed of 3 different but linked blocks: 1. Tokenizer (unigram) that transforms words into subword units and trained with the train transcriptions of LibriSpeech. 2. Neural language model (Transformer LM) trained on the full 10M words dataset. 3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of N blocks of convolutional neural networks with normalization and pooling on the frequency domain. Then, a bidirectional LSTM with projection layers is connected to a final DNN to obtain the final acoustic representation that is given to the CTC and attention decoders. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed. | 22d180044084f81f64273b5d7680e833 |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Transcribing your own audio files (in English) ```python from speechbrain.pretrained import EncoderDecoderASR asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-transformerlm-librispeech", savedir="pretrained_models/asr-crdnn-transformerlm-librispeech") asr_model.transcribe_file("speechbrain/asr-crdnn-transformerlm-librispeech/example.wav") ``` | 5c626750f95c75d51ee98941bf801b9c |
apache-2.0 | ['automatic-speech-recognition', 'CTC', 'Attention', 'Tranformer', 'pytorch', 'speechbrain'] | false | Training The model was trained with SpeechBrain (Commit hash: 'eca313cc'). To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LibriSpeech/ASR/seq2seq python train.py hparams/train_BPE_5000.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1kSwdBT8kDhnmTLzrOPDL77LX_Eq-3Tzl?usp=sharing). | 011d72c1eebef0420de65b4d5642fa89 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | convnext_manuscript_iiif This model is a fine-tuned version of [facebook/convnext-base-224-22k](https://huggingface.co/facebook/convnext-base-224-22k) on the davanstrien/iiif_manuscripts_label_ge_50 dataset. It achieves the following results on the evaluation set: - Loss: 5.5856 - F1: 0.0037 | 2e1351883ae1fd2027fe1d8b2b3aa507 |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 - mixed_precision_training: Native AMP | a40a0216ebe801f29c7ec43f45d7aa0a |
apache-2.0 | ['image-classification', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.5753 | 1.0 | 2038 | 6.4121 | 0.0016 | | 5.9865 | 2.0 | 4076 | 5.9466 | 0.0021 | | 5.6521 | 3.0 | 6114 | 5.7645 | 0.0029 | | 5.3123 | 4.0 | 8152 | 5.6890 | 0.0033 | | 5.0337 | 5.0 | 10190 | 5.6692 | 0.0034 | | 4.743 | 6.0 | 12228 | 5.5856 | 0.0037 | | 4.4387 | 7.0 | 14266 | 5.5969 | 0.0042 | | 4.1422 | 8.0 | 16304 | 5.6711 | 0.0043 | | 3.8372 | 9.0 | 18342 | 5.6761 | 0.0044 | | 3.5244 | 10.0 | 20380 | 5.8469 | 0.0042 | | 3.2321 | 11.0 | 22418 | 5.8774 | 0.0045 | | 2.9004 | 12.0 | 24456 | 6.1186 | 0.0047 | | 2.5937 | 13.0 | 26494 | 6.2398 | 0.0046 | | 2.2983 | 14.0 | 28532 | 6.3732 | 0.0049 | | 2.0611 | 15.0 | 30570 | 6.5024 | 0.0045 | | 1.8153 | 16.0 | 32608 | 6.6585 | 0.0047 | | 1.6075 | 17.0 | 34646 | 6.8333 | 0.0043 | | 1.4342 | 18.0 | 36684 | 6.9529 | 0.0044 | | 1.2614 | 19.0 | 38722 | 7.1129 | 0.0046 | | 1.1463 | 20.0 | 40760 | 7.1977 | 0.0039 | | 1.0387 | 21.0 | 42798 | 7.2700 | 0.0044 | | 0.9635 | 22.0 | 44836 | 7.3375 | 0.0040 | | 0.8872 | 23.0 | 46874 | 7.4003 | 0.0039 | | 0.8156 | 24.0 | 48912 | 7.4884 | 0.0039 | | 0.7544 | 25.0 | 50950 | 7.4764 | 0.0039 | | 0.6893 | 26.0 | 52988 | 7.5153 | 0.0042 | | 0.6767 | 27.0 | 55026 | 7.5427 | 0.0043 | | 0.6098 | 28.0 | 57064 | 7.5547 | 0.0042 | | 0.5871 | 29.0 | 59102 | 7.5533 | 0.0041 | | 0.5696 | 30.0 | 61140 | 7.5595 | 0.0041 | | 0ec53aa0281b0a54853a2d0a5b67d9a9 |
mit | ['generated_from_trainer'] | false | deberta-v3-small-finetuned-Disaster-Tweets-Part1 This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4014 - Accuracy: 0.8564 - F1: 0.8557 | 688ca622b714186d03cb4db595a5e7f3 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 - mixed_precision_training: Native AMP | b8558af4cab2dc1558286583268ae9d6 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 203 | 0.3828 | 0.8415 | 0.8414 | | No log | 2.0 | 406 | 0.4014 | 0.8564 | 0.8557 | | 5989cdf3a810d9d305b1ca54e85cfff4 |
apache-2.0 | ['generated_from_keras_callback'] | false | evangeloc/t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.7203 - Validation Loss: 2.4006 - Train Rouge1: 28.1689 - Train Rouge2: 7.9798 - Train Rougel: 22.6998 - Train Rougelsum: 22.7228 - Train Gen Len: 18.865 - Epoch: 0 | 488140d48f802ee988ec26293fc2faf4 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.7203 | 2.4006 | 28.1689 | 7.9798 | 22.6998 | 22.7228 | 18.865 | 0 | | 36776201233d2abd364697dc1ab2c9f9 |
cc-by-4.0 | [] | false | Model description This is the T5-3B model for the "classify" component of System 4's "Classify then explain" pipeline, as described in our paper Just-DREAM-about-it: Figurative Language Understanding with DREAM-FLUTE, FigLang workshop @ EMNLP 2022 (Arxiv link: https://arxiv.org/abs/2210.16407) System 4: Two-step System - Classify then explain In contrast to Systems 1 to 3 where the entailment/contradiction label and associated explanation are predicted jointly, System 4 uses a two-step “classify then explain” pipeline. This current model is for the "classify" component of the pipeline. The input-output format is: ``` Input <Premise> <Hypothesis> Output <Label> ``` | 908c9d605218ef36a81317d1008a636a |
cc-by-4.0 | [] | false | How to use this model? We provide a quick example of how you can try out the "classify" component of System 4 in our paper with just a few lines of code: ``` >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM >>> model = AutoModelForSeq2SeqLM.from_pretrained("allenai/System4_classify_FigLang2022") >>> tokenizer = AutoTokenizer.from_pretrained("t5-3b") >>> input_string = "Premise: After releasing his rage he was like a ferocious wolf. Hypothesis: After letting off his rage he sat down like a lamb. Is there a contradiction or entailment between the premise and hypothesis? Answer : " >>> input_ids = tokenizer.encode(input_string, return_tensors="pt") >>> output = model.generate(input_ids, max_length=200) >>> tokenizer.batch_decode(output, skip_special_tokens=True) ['Contradiction'] ``` | 039522f469a786c04b93873b4a5e983d |
cc-by-4.0 | [] | false | Model details This model is a fine-tuned version of [t5-3b](https://huggingface.co/t5-3b). It achieves the following results on the evaluation set: - Loss: 0.0604 - Rouge1: 95.0232 - Rouge2: 0.0 - Rougel: 95.0232 - Rougelsum: 95.0232 - Gen Len: 3.4074 | 51d29be14168ef13ed1e1c9d012fd6f5 |
cc-by-4.0 | [] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 0.1221 | 0.33 | 1000 | 0.1460 | 91.7717 | 0.0 | 91.9044 | 91.8381 | 3.4751 | | 0.0957 | 0.66 | 2000 | 0.0904 | 93.6297 | 0.0 | 93.6961 | 93.6961 | 3.3955 | | 0.0721 | 1.0 | 3000 | 0.0720 | 94.8905 | 0.0 | 94.9569 | 94.8905 | 3.4061 | | 0.0413 | 1.33 | 4000 | 0.0786 | 94.5587 | 0.0 | 94.5587 | 94.5587 | 3.4346 | | 0.042 | 1.66 | 5000 | 0.0604 | 95.0232 | 0.0 | 95.0232 | 95.0232 | 3.4074 | | 0.0413 | 1.99 | 6000 | 0.0737 | 95.2223 | 0.0 | 95.2223 | 95.2223 | 3.4413 | | 0.0198 | 2.32 | 7000 | 0.1045 | 95.0896 | 0.0 | 95.1559 | 95.1559 | 3.4101 | | 0.0253 | 2.65 | 8000 | 0.0836 | 95.2887 | 0.0 | 95.2887 | 95.2887 | 3.4393 | | 0.0198 | 2.99 | 9000 | 0.0922 | 94.7578 | 0.0 | 94.7578 | 94.7578 | 3.4180 | | a800432b40bb42a230d58ad0ebe81a50 |
apache-2.0 | ['generated_from_trainer'] | false | long-t5-local-base-finetuned This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 9.2722 - Rouge1: 3.8848 - Rouge2: 0.5914 - Rougel: 3.5038 - Rougelsum: 3.7022 - Gen Len: 19.0 | 57e3d32d9d17682808b2f202a6558094 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50000 | 5423def48af084a29b26d704fbbc53d1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 0.16 | 100 | 342.4395 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 | | No log | 0.31 | 200 | 323.6985 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 | | No log | 0.47 | 300 | 303.8767 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 | | No log | 0.62 | 400 | 284.7559 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 | | 295.8376 | 0.78 | 500 | 263.0420 | 0.0 | 0.0 | 0.0 | 0.0 | 19.0 | | 295.8376 | 0.93 | 600 | 243.2220 | 0.0242 | 0.0 | 0.0223 | 0.0242 | 19.0 | | 295.8376 | 1.09 | 700 | 224.4514 | 0.0493 | 0.0 | 0.0507 | 0.0513 | 19.0 | | 295.8376 | 1.24 | 800 | 203.9065 | 0.0656 | 0.0 | 0.0634 | 0.0658 | 19.0 | | 295.8376 | 1.4 | 900 | 184.8686 | 0.0609 | 0.0 | 0.058 | 0.0616 | 19.0 | | 199.938 | 1.55 | 1000 | 167.5315 | 0.0638 | 0.0 | 0.0626 | 0.063 | 19.0 | | 199.938 | 1.71 | 1100 | 151.2369 | 0.0421 | 0.0 | 0.0411 | 0.0413 | 19.0 | | 199.938 | 1.86 | 1200 | 137.2366 | 0.0358 | 0.0 | 0.0346 | 0.0342 | 19.0 | | 199.938 | 2.02 | 1300 | 125.3076 | 0.0173 | 0.0 | 0.0157 | 0.0157 | 19.0 | | 199.938 | 2.17 | 1400 | 114.5600 | 0.0173 | 0.0 | 0.0157 | 0.0157 | 19.0 | | 136.1309 | 2.33 | 1500 | 105.9237 | 0.0361 | 0.0 | 0.0344 | 0.0363 | 19.0 | | 136.1309 | 2.48 | 1600 | 97.4123 | 0.0526 | 0.0 | 0.051 | 0.054 | 19.0 | | 136.1309 | 2.64 | 1700 | 89.0873 | 0.0427 | 0.0 | 0.0407 | 0.0418 | 19.0 | | 136.1309 | 2.79 | 1800 | 82.0562 | 0.0496 | 0.0 | 0.0462 | 0.0462 | 19.0 | | 136.1309 | 2.95 | 1900 | 76.2360 | 0.0361 | 0.0 | 0.0345 | 0.0363 | 19.0 | | 99.2229 | 3.1 | 2000 | 70.0604 | 0.0438 | 0.0 | 0.0425 | 0.0439 | 19.0 | | 99.2229 | 3.26 | 2100 | 65.1038 | 0.0454 | 0.0 | 0.0441 | 0.0447 | 19.0 | | 99.2229 | 3.41 | 2200 | 59.1831 | 0.0344 | 0.0 | 0.0318 | 0.0318 | 19.0 | | 99.2229 | 3.57 | 2300 | 53.0313 | 0.0471 | 0.0 | 0.0448 | 0.0454 | 19.0 | | 99.2229 | 3.72 | 2400 | 48.2110 | 0.0369 | 0.0 | 0.0369 | 0.0369 | 19.0 | | 73.4208 | 3.88 | 2500 | 44.2004 | 0.0425 | 0.0 | 0.0427 | 0.044 | 19.0 | | 73.4208 | 4.03 | 2600 | 40.1925 | 0.0632 | 0.0 | 0.0619 | 0.0612 | 19.0 | | 73.4208 | 4.19 | 2700 | 36.3698 | 0.0887 | 0.0 | 0.0873 | 0.086 | 19.0 | | 73.4208 | 4.34 | 2800 | 33.2154 | 0.164 | 0.0 | 0.1652 | 0.1705 | 19.0 | | 73.4208 | 4.5 | 2900 | 30.9366 | 0.1106 | 0.0 | 0.1138 | 0.1144 | 19.0 | | 55.6661 | 4.65 | 3000 | 28.5672 | 0.1289 | 0.0 | 0.1295 | 0.131 | 19.0 | | 55.6661 | 4.81 | 3100 | 27.0910 | 0.2501 | 0.0 | 0.2514 | 0.2527 | 19.0 | | 55.6661 | 4.96 | 3200 | 25.6666 | 0.318 | 0.0 | 0.3322 | 0.3203 | 19.0 | | 55.6661 | 5.12 | 3300 | 24.6176 | 0.6319 | 0.0 | 0.6419 | 0.6299 | 19.0 | | 55.6661 | 5.27 | 3400 | 23.6474 | 1.6632 | 0.0033 | 1.665 | 1.6244 | 19.0 | | 45.1105 | 5.43 | 3500 | 22.7063 | 3.1374 | 0.0 | 3.1331 | 3.1333 | 19.0 | | 45.1105 | 5.58 | 3600 | 21.9191 | 5.0757 | 0.0 | 5.0694 | 5.0456 | 19.0 | | 45.1105 | 5.74 | 3700 | 21.3359 | 5.6576 | 0.0 | 5.689 | 5.6772 | 19.0 | | 45.1105 | 5.89 | 3800 | 20.6990 | 5.828 | 0.0 | 5.8801 | 5.8688 | 19.0 | | 45.1105 | 6.05 | 3900 | 20.1800 | 6.3727 | 0.0 | 6.3801 | 6.3716 | 19.0 | | 39.6923 | 6.2 | 4000 | 19.7415 | 6.2209 | 0.0 | 6.2347 | 6.2368 | 19.0 | | 39.6923 | 6.36 | 4100 | 19.2800 | 5.7215 | 0.0 | 5.7452 | 5.7295 | 19.0 | | 39.6923 | 6.51 | 4200 | 18.9683 | 6.1018 | 0.0062 | 6.1 | 6.0935 | 19.0 | | 39.6923 | 6.67 | 4300 | 18.5776 | 6.0354 | 0.0062 | 6.0227 | 6.0103 | 19.0 | | 39.6923 | 6.82 | 4400 | 18.2629 | 5.4438 | 0.0062 | 5.441 | 5.4629 | 19.0 | | 36.1688 | 6.98 | 4500 | 18.0268 | 5.3214 | 0.0091 | 5.3093 | 5.2992 | 19.0 | | 36.1688 | 7.13 | 4600 | 17.7740 | 5.2223 | 0.0123 | 5.2132 | 5.2084 | 19.0 | | 36.1688 | 7.29 | 4700 | 17.5345 | 5.178 | 0.0231 | 5.1615 | 5.1243 | 19.0 | | 36.1688 | 7.44 | 4800 | 17.3846 | 5.3899 | 0.0277 | 5.3414 | 5.3534 | 19.0 | | 36.1688 | 7.6 | 4900 | 17.1999 | 5.315 | 0.0272 | 5.2572 | 5.2477 | 19.0 | | 33.5745 | 7.75 | 5000 | 17.0078 | 5.9014 | 0.028 | 5.8181 | 5.8058 | 19.0 | | 33.5745 | 7.91 | 5100 | 16.6418 | 5.7546 | 0.0242 | 5.6903 | 5.6746 | 19.0 | | 33.5745 | 8.06 | 5200 | 16.6330 | 6.6893 | 0.0182 | 6.6354 | 6.6178 | 19.0 | | 33.5745 | 8.22 | 5300 | 16.3423 | 6.1679 | 0.0072 | 6.1518 | 6.128 | 19.0 | | 33.5745 | 8.37 | 5400 | 16.2373 | 6.7659 | 0.0139 | 6.7271 | 6.7076 | 19.0 | | 31.9486 | 8.53 | 5500 | 16.1523 | 7.1991 | 0.0139 | 7.1674 | 7.1283 | 19.0 | | 31.9486 | 8.68 | 5600 | 16.0607 | 7.7042 | 0.0169 | 7.6741 | 7.6537 | 19.0 | | 31.9486 | 8.84 | 5700 | 15.7647 | 7.1238 | 0.02 | 7.1113 | 7.0586 | 19.0 | | 31.9486 | 8.99 | 5800 | 15.6194 | 7.3055 | 0.0116 | 7.3311 | 7.2683 | 19.0 | | 31.9486 | 9.15 | 5900 | 15.4994 | 7.3365 | 0.0139 | 7.3026 | 7.2708 | 19.0 | | 30.5224 | 9.3 | 6000 | 15.4207 | 8.1959 | 0.0116 | 8.1917 | 8.1651 | 19.0 | | 30.5224 | 9.46 | 6100 | 15.2981 | 7.7936 | 0.0144 | 7.7826 | 7.7488 | 19.0 | | 30.5224 | 9.61 | 6200 | 15.2391 | 7.95 | 0.0144 | 7.9371 | 7.895 | 19.0 | | 30.5224 | 9.77 | 6300 | 15.0941 | 7.1669 | 0.0144 | 7.146 | 7.1251 | 19.0 | | 30.5224 | 9.92 | 6400 | 14.9979 | 6.2157 | 0.0076 | 6.2086 | 6.1774 | 19.0 | | 29.1236 | 10.08 | 6500 | 14.9523 | 7.4422 | 0.0137 | 7.3929 | 7.393 | 19.0 | | 29.1236 | 10.23 | 6600 | 14.9515 | 7.2375 | 0.0137 | 7.1728 | 7.1779 | 19.0 | | 29.1236 | 10.39 | 6700 | 14.8874 | 7.5071 | 0.0068 | 7.4544 | 7.4739 | 19.0 | | 29.1236 | 10.54 | 6800 | 14.8057 | 5.9608 | 0.0169 | 5.8754 | 5.8691 | 19.0 | | 29.1236 | 10.7 | 6900 | 14.6818 | 5.6345 | 0.021 | 5.5422 | 5.5331 | 19.0 | | 28.314 | 10.85 | 7000 | 14.5409 | 5.5799 | 0.0169 | 5.4915 | 5.4833 | 19.0 | | 28.314 | 11.01 | 7100 | 14.4512 | 4.3498 | 0.0368 | 4.2243 | 4.2193 | 19.0 | | 28.314 | 11.16 | 7200 | 14.4560 | 4.0453 | 0.0372 | 3.9481 | 3.9228 | 19.0 | | 28.314 | 11.32 | 7300 | 14.3851 | 5.1332 | 0.0426 | 5.0186 | 4.9882 | 19.0 | | 28.314 | 11.47 | 7400 | 14.2265 | 4.8944 | 0.0371 | 4.7869 | 4.7765 | 19.0 | | 27.5349 | 11.63 | 7500 | 14.1214 | 3.8846 | 0.0335 | 3.7882 | 3.7677 | 19.0 | | 27.5349 | 11.78 | 7600 | 14.1505 | 3.9992 | 0.0514 | 3.883 | 3.8385 | 19.0 | | 27.5349 | 11.94 | 7700 | 13.9923 | 3.4526 | 0.0664 | 3.325 | 3.3258 | 19.0 | | 27.5349 | 12.09 | 7800 | 14.0299 | 2.3086 | 0.0346 | 2.25 | 2.219 | 19.0 | | 27.5349 | 12.25 | 7900 | 13.9814 | 2.4402 | 0.0628 | 2.3282 | 2.3004 | 19.0 | | 26.4286 | 12.4 | 8000 | 13.8561 | 2.9869 | 0.0654 | 2.8769 | 2.8485 | 19.0 | | 26.4286 | 12.56 | 8100 | 13.8259 | 1.9609 | 0.0386 | 1.8863 | 1.8846 | 19.0 | | 26.4286 | 12.71 | 8200 | 13.8127 | 2.0628 | 0.0355 | 1.9915 | 1.9738 | 19.0 | | 26.4286 | 12.87 | 8300 | 13.7174 | 1.9904 | 0.081 | 1.888 | 1.9069 | 19.0 | | 26.4286 | 13.02 | 8400 | 13.6308 | 2.1398 | 0.1055 | 2.0204 | 2.0468 | 19.0 | | 26.108 | 13.18 | 8500 | 13.6490 | 1.8934 | 0.0788 | 1.7942 | 1.8188 | 19.0 | | 26.108 | 13.33 | 8600 | 13.5996 | 1.8746 | 0.0901 | 1.7441 | 1.8006 | 19.0 | | 26.108 | 13.49 | 8700 | 13.5394 | 1.7846 | 0.0895 | 1.6648 | 1.7331 | 19.0 | | 26.108 | 13.64 | 8800 | 13.5368 | 2.1345 | 0.1287 | 1.9808 | 2.0814 | 19.0 | | 26.108 | 13.8 | 8900 | 13.4793 | 2.5234 | 0.1611 | 2.3289 | 2.4292 | 19.0 | | 25.4931 | 13.95 | 9000 | 13.3633 | 2.8056 | 0.1953 | 2.5619 | 2.7088 | 19.0 | | 25.4931 | 14.11 | 9100 | 13.5182 | 3.087 | 0.2192 | 2.8182 | 2.9928 | 19.0 | | 25.4931 | 14.26 | 9200 | 13.3372 | 2.6353 | 0.175 | 2.4145 | 2.589 | 19.0 | | 25.4931 | 14.42 | 9300 | 13.2822 | 2.7577 | 0.1905 | 2.5277 | 2.7215 | 19.0 | | 25.4931 | 14.57 | 9400 | 13.2011 | 3.1891 | 0.2381 | 2.9276 | 3.142 | 19.0 | | 24.9241 | 14.73 | 9500 | 13.2201 | 2.609 | 0.1683 | 2.4162 | 2.5905 | 19.0 | | 24.9241 | 14.88 | 9600 | 13.2206 | 3.1083 | 0.2241 | 2.8627 | 3.0606 | 19.0 | | 24.9241 | 15.04 | 9700 | 13.2157 | 3.6233 | 0.2731 | 3.338 | 3.5642 | 19.0 | | 24.9241 | 15.19 | 9800 | 13.1195 | 3.1785 | 0.2318 | 2.9449 | 3.1306 | 19.0 | | 24.9241 | 15.35 | 9900 | 13.0481 | 3.0249 | 0.2192 | 2.7991 | 2.9925 | 19.0 | | 24.4511 | 15.5 | 10000 | 13.0693 | 3.1189 | 0.2287 | 2.8726 | 3.0669 | 19.0 | | 24.4511 | 15.66 | 10100 | 12.9204 | 2.6405 | 0.1899 | 2.4337 | 2.61 | 19.0 | | 24.4511 | 15.81 | 10200 | 12.9200 | 2.9037 | 0.2148 | 2.6775 | 2.8683 | 19.0 | | 24.4511 | 15.97 | 10300 | 12.9203 | 2.8847 | 0.2034 | 2.6586 | 2.8438 | 19.0 | | 24.4511 | 16.12 | 10400 | 12.8723 | 2.8195 | 0.1976 | 2.5922 | 2.7803 | 19.0 | | 23.8949 | 16.28 | 10500 | 12.9749 | 3.2658 | 0.2217 | 2.9905 | 3.2262 | 19.0 | | 23.8949 | 16.43 | 10600 | 12.7975 | 2.9762 | 0.1844 | 2.7295 | 2.9474 | 19.0 | | 23.8949 | 16.59 | 10700 | 12.7497 | 2.5496 | 0.1406 | 2.3536 | 2.5269 | 19.0 | | 23.8949 | 16.74 | 10800 | 12.6485 | 2.5509 | 0.1454 | 2.343 | 2.5182 | 19.0 | | 23.8949 | 16.9 | 10900 | 12.6574 | 2.1914 | 0.1281 | 2.0113 | 2.1574 | 19.0 | | 23.4963 | 17.05 | 11000 | 12.6919 | 2.1748 | 0.1299 | 1.9909 | 2.1229 | 19.0 | | 23.4963 | 17.21 | 11100 | 12.5660 | 2.3751 | 0.1177 | 2.1417 | 2.326 | 19.0 | | 23.4963 | 17.36 | 11200 | 12.5866 | 2.6893 | 0.1344 | 2.4378 | 2.6318 | 19.0 | | 23.4963 | 17.52 | 11300 | 12.5427 | 2.5546 | 0.1411 | 2.3175 | 2.5073 | 19.0 | | 23.4963 | 17.67 | 11400 | 12.5011 | 2.347 | 0.1223 | 2.1322 | 2.3077 | 19.0 | | 23.1492 | 17.83 | 11500 | 12.5168 | 2.2304 | 0.1141 | 2.0657 | 2.1951 | 19.0 | | 23.1492 | 17.98 | 11600 | 12.4043 | 2.4485 | 0.1209 | 2.2548 | 2.4114 | 19.0 | | 23.1492 | 18.14 | 11700 | 12.4192 | 2.0551 | 0.0887 | 1.8996 | 2.0199 | 19.0 | | 23.1492 | 18.29 | 11800 | 12.3799 | 2.1076 | 0.0932 | 1.9464 | 2.0589 | 19.0 | | 23.1492 | 18.45 | 11900 | 12.4263 | 2.4136 | 0.1152 | 2.2172 | 2.357 | 19.0 | | 22.7005 | 18.6 | 12000 | 12.3218 | 2.1197 | 0.1105 | 1.9997 | 2.0873 | 19.0 | | 22.7005 | 18.76 | 12100 | 12.3297 | 2.1883 | 0.1102 | 2.0414 | 2.1267 | 19.0 | | 22.7005 | 18.91 | 12200 | 12.3026 | 1.966 | 0.0954 | 1.8387 | 1.9469 | 19.0 | | 22.7005 | 19.07 | 12300 | 12.3030 | 2.0179 | 0.0955 | 1.8834 | 1.9858 | 19.0 | | 22.7005 | 19.22 | 12400 | 12.2478 | 1.9549 | 0.0948 | 1.8437 | 1.9092 | 19.0 | | 22.3178 | 19.38 | 12500 | 12.1803 | 1.6396 | 0.0648 | 1.5296 | 1.6208 | 19.0 | | 22.3178 | 19.53 | 12600 | 12.1732 | 1.5568 | 0.0769 | 1.4894 | 1.5387 | 19.0 | | 22.3178 | 19.69 | 12700 | 12.1342 | 1.6861 | 0.0782 | 1.6105 | 1.666 | 19.0 | | 22.3178 | 19.84 | 12800 | 12.1313 | 2.023 | 0.0965 | 1.9295 | 2.0072 | 19.0 | | 22.3178 | 20.0 | 12900 | 12.1315 | 1.5878 | 0.0701 | 1.5153 | 1.5467 | 19.0 | | 21.8344 | 20.16 | 13000 | 12.0611 | 1.6406 | 0.0637 | 1.5665 | 1.6033 | 19.0 | | 21.8344 | 20.31 | 13100 | 12.0327 | 1.5913 | 0.0544 | 1.5209 | 1.552 | 19.0 | | 21.8344 | 20.47 | 13200 | 12.0466 | 1.3618 | 0.0494 | 1.3186 | 1.33 | 19.0 | | 21.8344 | 20.62 | 13300 | 12.0787 | 1.4445 | 0.0451 | 1.4073 | 1.41 | 19.0 | | 21.8344 | 20.78 | 13400 | 11.9829 | 1.3465 | 0.0494 | 1.3247 | 1.3167 | 19.0 | | 21.6309 | 20.93 | 13500 | 11.9072 | 1.4165 | 0.0519 | 1.3761 | 1.3839 | 19.0 | | 21.6309 | 21.09 | 13600 | 11.9261 | 1.3969 | 0.0502 | 1.3606 | 1.3618 | 19.0 | | 21.6309 | 21.24 | 13700 | 11.8313 | 1.3337 | 0.0337 | 1.2974 | 1.316 | 19.0 | | 21.6309 | 21.4 | 13800 | 11.7709 | 1.3045 | 0.0371 | 1.2746 | 1.2889 | 19.0 | | 21.6309 | 21.55 | 13900 | 11.8402 | 1.6106 | 0.0391 | 1.5678 | 1.5697 | 19.0 | | 21.2262 | 21.71 | 14000 | 11.7132 | 1.3261 | 0.0222 | 1.296 | 1.3051 | 19.0 | | 21.2262 | 21.86 | 14100 | 11.7206 | 1.41 | 0.0252 | 1.374 | 1.3985 | 19.0 | | 21.2262 | 22.02 | 14200 | 11.7033 | 1.6231 | 0.0478 | 1.5632 | 1.5851 | 19.0 | | 21.2262 | 22.17 | 14300 | 11.7385 | 1.8974 | 0.0618 | 1.8339 | 1.8583 | 19.0 | | 21.2262 | 22.33 | 14400 | 11.6519 | 1.8998 | 0.0541 | 1.8285 | 1.8552 | 19.0 | | 20.8055 | 22.48 | 14500 | 11.6039 | 1.9561 | 0.0582 | 1.859 | 1.9073 | 19.0 | | 20.8055 | 22.64 | 14600 | 11.6322 | 1.7731 | 0.0442 | 1.7061 | 1.7303 | 19.0 | | 20.8055 | 22.79 | 14700 | 11.6046 | 1.8874 | 0.0618 | 1.8083 | 1.8539 | 19.0 | | 20.8055 | 22.95 | 14800 | 11.5051 | 1.4271 | 0.016 | 1.3996 | 1.4086 | 19.0 | | 20.8055 | 23.1 | 14900 | 11.5564 | 1.743 | 0.0451 | 1.6787 | 1.727 | 19.0 | | 20.6263 | 23.26 | 15000 | 11.5024 | 1.9313 | 0.0575 | 1.8357 | 1.887 | 19.0 | | 20.6263 | 23.41 | 15100 | 11.5281 | 2.082 | 0.0435 | 1.9865 | 2.0327 | 19.0 | | 20.6263 | 23.57 | 15200 | 11.4223 | 1.9773 | 0.0332 | 1.9038 | 1.9432 | 19.0 | | 20.6263 | 23.72 | 15300 | 11.4675 | 1.7845 | 0.0831 | 1.6835 | 1.7414 | 19.0 | | 20.6263 | 23.88 | 15400 | 11.3882 | 2.1183 | 0.0715 | 1.9965 | 2.0725 | 19.0 | | 20.3154 | 24.03 | 15500 | 11.4197 | 2.4045 | 0.1336 | 2.2302 | 2.3024 | 19.0 | | 20.3154 | 24.19 | 15600 | 11.3558 | 1.9596 | 0.1196 | 1.8152 | 1.8748 | 19.0 | | 20.3154 | 24.34 | 15700 | 11.3438 | 2.0931 | 0.111 | 1.9469 | 1.999 | 19.0 | | 20.3154 | 24.5 | 15800 | 11.3021 | 2.2159 | 0.1257 | 2.0511 | 2.1345 | 19.0 | | 20.3154 | 24.65 | 15900 | 11.3178 | 2.093 | 0.132 | 1.9083 | 1.9969 | 19.0 | | 20.0858 | 24.81 | 16000 | 11.2377 | 1.6589 | 0.1129 | 1.5625 | 1.6245 | 19.0 | | 20.0858 | 24.96 | 16100 | 11.2058 | 1.6667 | 0.0854 | 1.5597 | 1.6223 | 19.0 | | 20.0858 | 25.12 | 16200 | 11.1602 | 2.0907 | 0.1219 | 1.9297 | 1.9988 | 19.0 | | 20.0858 | 25.27 | 16300 | 11.1666 | 1.86 | 0.1092 | 1.7398 | 1.7993 | 19.0 | | 20.0858 | 25.43 | 16400 | 11.1807 | 1.8879 | 0.1818 | 1.7579 | 1.8335 | 19.0 | | 19.7588 | 25.58 | 16500 | 11.1310 | 2.0377 | 0.1612 | 1.8653 | 1.9538 | 19.0 | | 19.7588 | 25.74 | 16600 | 11.1577 | 2.1441 | 0.1767 | 1.9546 | 2.0518 | 19.0 | | 19.7588 | 25.89 | 16700 | 11.0748 | 1.8679 | 0.1892 | 1.7249 | 1.7822 | 19.0 | | 19.7588 | 26.05 | 16800 | 11.1048 | 2.2775 | 0.2072 | 2.0566 | 2.1521 | 19.0 | | 19.7588 | 26.2 | 16900 | 11.0498 | 1.8117 | 0.161 | 1.6879 | 1.7357 | 19.0 | | 19.4627 | 26.36 | 17000 | 11.0435 | 1.7875 | 0.1627 | 1.6626 | 1.7306 | 19.0 | | 19.4627 | 26.51 | 17100 | 10.9406 | 1.7333 | 0.1645 | 1.6051 | 1.6671 | 19.0 | | 19.4627 | 26.67 | 17200 | 10.9242 | 1.596 | 0.1426 | 1.4747 | 1.5341 | 19.0 | | 19.4627 | 26.82 | 17300 | 10.9571 | 1.9874 | 0.2109 | 1.8109 | 1.9061 | 19.0 | | 19.4627 | 26.98 | 17400 | 10.9265 | 1.6999 | 0.1353 | 1.5574 | 1.6402 | 19.0 | | 19.2619 | 27.13 | 17500 | 10.8919 | 1.7543 | 0.1709 | 1.587 | 1.6605 | 19.0 | | 19.2619 | 27.29 | 17600 | 10.8382 | 2.126 | 0.2056 | 1.8609 | 2.0021 | 19.0 | | 19.2619 | 27.44 | 17700 | 10.8936 | 1.9626 | 0.1726 | 1.7402 | 1.8665 | 19.0 | | 19.2619 | 27.6 | 17800 | 10.8565 | 1.7668 | 0.1673 | 1.5914 | 1.7099 | 19.0 | | 19.2619 | 27.75 | 17900 | 10.9047 | 2.0972 | 0.1867 | 1.8519 | 2.0224 | 19.0 | | 19.0457 | 27.91 | 18000 | 10.7900 | 2.7761 | 0.2904 | 2.4403 | 2.6936 | 19.0 | | 19.0457 | 28.06 | 18100 | 10.7191 | 2.3652 | 0.2431 | 2.0989 | 2.2767 | 19.0 | | 19.0457 | 28.22 | 18200 | 10.7462 | 3.3125 | 0.361 | 2.847 | 3.1506 | 19.0 | | 19.0457 | 28.37 | 18300 | 10.7721 | 2.9247 | 0.3 | 2.5443 | 2.806 | 19.0 | | 19.0457 | 28.53 | 18400 | 10.7208 | 2.5398 | 0.2812 | 2.2211 | 2.4312 | 19.0 | | 18.8301 | 28.68 | 18500 | 10.6708 | 2.5902 | 0.281 | 2.2765 | 2.4881 | 19.0 | | 18.8301 | 28.84 | 18600 | 10.7220 | 2.276 | 0.2061 | 1.9904 | 2.1922 | 19.0 | | 18.8301 | 28.99 | 18700 | 10.6855 | 2.8678 | 0.3496 | 2.52 | 2.751 | 19.0 | | 18.8301 | 29.15 | 18800 | 10.6550 | 2.5232 | 0.2724 | 2.2108 | 2.4314 | 19.0 | | 18.8301 | 29.3 | 18900 | 10.6488 | 2.5629 | 0.2203 | 2.2361 | 2.4261 | 19.0 | | 18.5872 | 29.46 | 19000 | 10.6123 | 2.5052 | 0.1923 | 2.1381 | 2.3821 | 19.0 | | 18.5872 | 29.61 | 19100 | 10.6105 | 3.7779 | 0.3653 | 3.2404 | 3.5759 | 19.0 | | 18.5872 | 29.77 | 19200 | 10.5823 | 3.8282 | 0.3743 | 3.2645 | 3.6077 | 19.0 | | 18.5872 | 29.92 | 19300 | 10.5606 | 3.0976 | 0.277 | 2.6041 | 2.8838 | 19.0 | | 18.5872 | 30.08 | 19400 | 10.5846 | 3.638 | 0.3482 | 3.0804 | 3.4294 | 19.0 | | 18.2839 | 30.23 | 19500 | 10.4722 | 2.6173 | 0.2326 | 2.2268 | 2.4656 | 19.0 | | 18.2839 | 30.39 | 19600 | 10.5211 | 3.5085 | 0.3377 | 2.9751 | 3.2889 | 19.0 | | 18.2839 | 30.54 | 19700 | 10.4735 | 2.4781 | 0.2097 | 2.1099 | 2.3338 | 19.0 | | 18.2839 | 30.7 | 19800 | 10.4545 | 3.1459 | 0.3022 | 2.6844 | 2.9559 | 19.0 | | 18.2839 | 30.85 | 19900 | 10.4525 | 3.6095 | 0.3637 | 3.0873 | 3.3886 | 19.0 | | 18.1352 | 31.01 | 20000 | 10.4409 | 4.0556 | 0.4621 | 3.3857 | 3.7778 | 19.0 | | 18.1352 | 31.16 | 20100 | 10.4132 | 3.8346 | 0.3863 | 3.2323 | 3.6266 | 19.0 | | 18.1352 | 31.32 | 20200 | 10.4468 | 2.3736 | 0.1977 | 2.0195 | 2.236 | 19.0 | | 18.1352 | 31.47 | 20300 | 10.3896 | 3.6954 | 0.3512 | 3.1402 | 3.4667 | 19.0 | | 18.1352 | 31.63 | 20400 | 10.3546 | 3.5158 | 0.3558 | 3.0575 | 3.3116 | 19.0 | | 17.9834 | 31.78 | 20500 | 10.3632 | 3.179 | 0.3374 | 2.7634 | 2.9846 | 19.0 | | 17.9834 | 31.94 | 20600 | 10.3168 | 3.9121 | 0.4012 | 3.3812 | 3.687 | 19.0 | | 17.9834 | 32.09 | 20700 | 10.2772 | 3.6148 | 0.3667 | 3.1059 | 3.3541 | 19.0 | | 17.9834 | 32.25 | 20800 | 10.3173 | 3.1448 | 0.2924 | 2.6948 | 2.9338 | 19.0 | | 17.9834 | 32.4 | 20900 | 10.2154 | 2.4611 | 0.1922 | 2.1597 | 2.3288 | 19.0 | | 17.6192 | 32.56 | 21000 | 10.2957 | 3.3177 | 0.3762 | 2.8085 | 3.0595 | 19.0 | | 17.6192 | 32.71 | 21100 | 10.2064 | 3.4663 | 0.3819 | 3.0229 | 3.2201 | 19.0 | | 17.6192 | 32.87 | 21200 | 10.2235 | 3.245 | 0.3179 | 2.7618 | 3.0066 | 19.0 | | 17.6192 | 33.02 | 21300 | 10.2193 | 2.5572 | 0.2775 | 2.216 | 2.3892 | 19.0 | | 17.6192 | 33.18 | 21400 | 10.2467 | 3.4873 | 0.3934 | 3.02 | 3.2701 | 19.0 | | 17.5532 | 33.33 | 21500 | 10.2378 | 2.8087 | 0.3049 | 2.4001 | 2.6218 | 19.0 | | 17.5532 | 33.49 | 21600 | 10.2086 | 3.8967 | 0.4801 | 3.3678 | 3.603 | 19.0 | | 17.5532 | 33.64 | 21700 | 10.2384 | 2.6534 | 0.3239 | 2.3276 | 2.4692 | 19.0 | | 17.5532 | 33.8 | 21800 | 10.1929 | 2.6025 | 0.2845 | 2.2653 | 2.4507 | 19.0 | | 17.5532 | 33.95 | 21900 | 10.1016 | 3.3244 | 0.377 | 2.8311 | 3.0784 | 19.0 | | 17.3872 | 34.11 | 22000 | 10.1407 | 3.4245 | 0.4024 | 3.044 | 3.1865 | 19.0 | | 17.3872 | 34.26 | 22100 | 10.0760 | 3.9251 | 0.4272 | 3.4064 | 3.6497 | 19.0 | | 17.3872 | 34.42 | 22200 | 10.0998 | 3.3034 | 0.3438 | 2.8977 | 3.1141 | 19.0 | | 17.3872 | 34.57 | 22300 | 10.0834 | 2.4967 | 0.266 | 2.2301 | 2.3647 | 19.0 | | 17.3872 | 34.73 | 22400 | 9.9902 | 4.0828 | 0.4867 | 3.5482 | 3.7861 | 19.0 | | 17.1744 | 34.88 | 22500 | 10.0366 | 3.5772 | 0.4377 | 3.1153 | 3.3199 | 19.0 | | 17.1744 | 35.04 | 22600 | 10.0299 | 3.5342 | 0.433 | 3.0501 | 3.2176 | 19.0 | | 17.1744 | 35.19 | 22700 | 9.9912 | 3.7754 | 0.4445 | 3.3191 | 3.502 | 19.0 | | 17.1744 | 35.35 | 22800 | 9.9580 | 4.5086 | 0.5514 | 3.8986 | 4.1987 | 19.0 | | 17.1744 | 35.5 | 22900 | 9.9676 | 3.526 | 0.3942 | 3.0859 | 3.3082 | 19.0 | | 17.0687 | 35.66 | 23000 | 9.9874 | 3.7058 | 0.5139 | 3.2353 | 3.4611 | 19.0 | | 17.0687 | 35.81 | 23100 | 9.9536 | 3.6588 | 0.4552 | 3.1591 | 3.3554 | 19.0 | | 17.0687 | 35.97 | 23200 | 9.8948 | 3.6279 | 0.3933 | 3.1403 | 3.3426 | 19.0 | | 17.0687 | 36.12 | 23300 | 9.8397 | 3.8101 | 0.4971 | 3.3152 | 3.5133 | 19.0 | | 17.0687 | 36.28 | 23400 | 9.8995 | 3.3201 | 0.4209 | 2.9101 | 3.0903 | 19.0 | | 16.7686 | 36.43 | 23500 | 9.9085 | 4.0108 | 0.6389 | 3.5055 | 3.7286 | 19.0 | | 16.7686 | 36.59 | 23600 | 9.8688 | 3.6051 | 0.5164 | 3.1651 | 3.3781 | 19.0 | | 16.7686 | 36.74 | 23700 | 9.8673 | 4.4987 | 0.6051 | 3.8789 | 4.1868 | 19.0 | | 16.7686 | 36.9 | 23800 | 9.8848 | 3.6926 | 0.5635 | 3.1681 | 3.3902 | 19.0 | | 16.7686 | 37.05 | 23900 | 9.8497 | 3.518 | 0.4283 | 3.1159 | 3.3112 | 19.0 | | 16.7432 | 37.21 | 24000 | 9.8044 | 3.3369 | 0.3772 | 2.9784 | 3.147 | 19.0 | | 16.7432 | 37.36 | 24100 | 9.7768 | 3.5862 | 0.3819 | 3.1273 | 3.3535 | 19.0 | | 16.7432 | 37.52 | 24200 | 9.7536 | 4.1823 | 0.5884 | 3.645 | 3.8843 | 19.0 | | 16.7432 | 37.67 | 24300 | 9.7953 | 4.3981 | 0.6441 | 3.7941 | 4.0623 | 19.0 | | 16.7432 | 37.83 | 24400 | 9.6742 | 3.7833 | 0.4755 | 3.3516 | 3.5543 | 19.0 | | 16.5714 | 37.98 | 24500 | 9.7946 | 3.3839 | 0.495 | 3.0021 | 3.156 | 19.0 | | 16.5714 | 38.14 | 24600 | 9.7544 | 4.3873 | 0.6486 | 3.8188 | 4.0653 | 19.0 | | 16.5714 | 38.29 | 24700 | 9.7586 | 3.4403 | 0.4756 | 3.0402 | 3.2405 | 19.0 | | 16.5714 | 38.45 | 24800 | 9.7895 | 3.6822 | 0.6247 | 3.2612 | 3.4746 | 19.0 | | 16.5714 | 38.6 | 24900 | 9.6964 | 3.8743 | 0.6209 | 3.4159 | 3.6051 | 19.0 | | 16.3393 | 38.76 | 25000 | 9.7190 | 4.1508 | 0.635 | 3.5925 | 3.8753 | 19.0 | | 16.3393 | 38.91 | 25100 | 9.6435 | 3.6755 | 0.4777 | 3.268 | 3.4572 | 19.0 | | 16.3393 | 39.07 | 25200 | 9.6390 | 2.9478 | 0.4049 | 2.6531 | 2.7782 | 19.0 | | 16.3393 | 39.22 | 25300 | 9.6300 | 2.9973 | 0.3897 | 2.6662 | 2.7943 | 19.0 | | 16.3393 | 39.38 | 25400 | 9.6229 | 3.6726 | 0.4182 | 3.2207 | 3.4595 | 19.0 | | 16.3076 | 39.53 | 25500 | 9.6392 | 2.9691 | 0.3692 | 2.6709 | 2.8182 | 19.0 | | 16.3076 | 39.69 | 25600 | 9.5978 | 2.8167 | 0.3437 | 2.593 | 2.7155 | 19.0 | | 16.3076 | 39.84 | 25700 | 9.6111 | 3.5135 | 0.5453 | 3.1415 | 3.3042 | 19.0 | | 16.3076 | 40.0 | 25800 | 9.6118 | 3.459 | 0.4963 | 3.1351 | 3.2809 | 19.0 | | 16.3076 | 40.16 | 25900 | 9.5994 | 3.5735 | 0.539 | 3.2556 | 3.3904 | 19.0 | | 16.0684 | 40.31 | 26000 | 9.5526 | 3.3388 | 0.4689 | 2.9753 | 3.1562 | 19.0 | | 16.0684 | 40.47 | 26100 | 9.5365 | 3.0882 | 0.392 | 2.8072 | 2.9556 | 19.0 | | 16.0684 | 40.62 | 26200 | 9.5571 | 3.0022 | 0.4109 | 2.7108 | 2.8575 | 19.0 | | 16.0684 | 40.78 | 26300 | 9.5240 | 3.506 | 0.5734 | 3.1577 | 3.3378 | 19.0 | | 16.0684 | 40.93 | 26400 | 9.4913 | 3.5936 | 0.5165 | 3.2452 | 3.4134 | 19.0 | | 15.9425 | 41.09 | 26500 | 9.5297 | 3.7802 | 0.6862 | 3.4061 | 3.5436 | 19.0 | | 15.9425 | 41.24 | 26600 | 9.4657 | 3.8433 | 0.6105 | 3.4621 | 3.638 | 19.0 | | 15.9425 | 41.4 | 26700 | 9.5049 | 3.5822 | 0.6462 | 3.231 | 3.3745 | 19.0 | | 15.9425 | 41.55 | 26800 | 9.4739 | 2.9668 | 0.4426 | 2.7345 | 2.8134 | 19.0 | | 15.9425 | 41.71 | 26900 | 9.4868 | 3.7458 | 0.6934 | 3.3708 | 3.5492 | 19.0 | | 15.7779 | 41.86 | 27000 | 9.4683 | 3.5254 | 0.6006 | 3.1629 | 3.3011 | 19.0 | | 15.7779 | 42.02 | 27100 | 9.4108 | 4.2731 | 0.7412 | 3.8236 | 4.0171 | 19.0 | | 15.7779 | 42.17 | 27200 | 9.3994 | 3.5014 | 0.5738 | 3.1525 | 3.3306 | 19.0 | | 15.7779 | 42.33 | 27300 | 9.3760 | 3.4929 | 0.4954 | 3.1402 | 3.3028 | 19.0 | | 15.7779 | 42.48 | 27400 | 9.4201 | 4.2777 | 0.7152 | 3.7943 | 4.0349 | 19.0 | | 15.7238 | 42.64 | 27500 | 9.3913 | 3.6489 | 0.6371 | 3.2903 | 3.4528 | 19.0 | | 15.7238 | 42.79 | 27600 | 9.4269 | 3.5269 | 0.6042 | 3.2049 | 3.3528 | 19.0 | | 15.7238 | 42.95 | 27700 | 9.3847 | 3.4735 | 0.5963 | 3.1522 | 3.2796 | 19.0 | | 15.7238 | 43.1 | 27800 | 9.3474 | 3.8327 | 0.6428 | 3.406 | 3.5698 | 19.0 | | 15.7238 | 43.26 | 27900 | 9.3293 | 3.5475 | 0.6313 | 3.1725 | 3.3367 | 19.0 | | 15.5108 | 43.41 | 28000 | 9.3802 | 4.249 | 0.7997 | 3.7924 | 3.9849 | 19.0 | | 15.5108 | 43.57 | 28100 | 9.2588 | 3.4476 | 0.4676 | 3.1758 | 3.2993 | 19.0 | | 15.5108 | 43.72 | 28200 | 9.3447 | 4.0267 | 0.7081 | 3.6208 | 3.7957 | 19.0 | | 15.5108 | 43.88 | 28300 | 9.2853 | 4.0105 | 0.7799 | 3.5848 | 3.7619 | 19.0 | | 15.5108 | 44.03 | 28400 | 9.2753 | 3.1833 | 0.4678 | 2.9068 | 3.0168 | 19.0 | | 15.4004 | 44.19 | 28500 | 9.2345 | 3.6778 | 0.5955 | 3.3212 | 3.4724 | 19.0 | | 15.4004 | 44.34 | 28600 | 9.3130 | 3.9958 | 0.6892 | 3.5871 | 3.772 | 19.0 | | 15.4004 | 44.5 | 28700 | 9.2984 | 4.1868 | 0.696 | 3.7194 | 3.9197 | 19.0 | | 15.4004 | 44.65 | 28800 | 9.2722 | 3.8848 | 0.5914 | 3.5038 | 3.7022 | 19.0 | | df91981972f2d9ecbaee6c5d4893e968 |
mit | ['conversational'] | false | DialoGPT Trained on the Speech of a TV Series Character This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a TV series character, Sheldon from [The Big Bang Theory](https://en.wikipedia.org/wiki/The_Big_Bang_Theory). The data comes from [a Kaggle TV series script dataset](https://www.kaggle.com/mitramir5/the-big-bang-theory-series-transcript). Chat with the model: ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("spirax/DialoGPT-medium-sheldon") model = AutoModelWithLMHead.from_pretrained("spirax/DialoGPT-medium-sheldon") | 70ef5fb0e5a6e7ca744c5c08df3cfa7d |
mit | [] | false | Caitlin Fairchild, character, gen13 comics, by J. Scott Campbell on Stable Diffusion This is the `<Caitlin-Fairchild>` 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 an `object`:            | 47dd259dabc999be8e6f28079bc4e27f |
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: 0.3399 - Accuracy: 0.901 - F1: 0.8976 | f0f117b251cb46617978ab7b2e017004 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 125 | 0.5129 | 0.8465 | 0.8300 | | 0.7331 | 2.0 | 250 | 0.3399 | 0.901 | 0.8976 | | d2ca07523bf7d3686abbaa8d57a893c4 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-xlsr-53-espeak-cv-ft-mhr2-ntsema-colab This model is a fine-tuned version of [facebook/wav2vec2-xlsr-53-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-xlsr-53-espeak-cv-ft) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7562 - Wer: 0.7993 | f7a4245f9f416bd28c759a7902dbb819 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.5636 | 5.79 | 400 | 1.8357 | 1.0 | | 1.6348 | 11.59 | 800 | 0.6797 | 0.8528 | | 0.8624 | 17.39 | 1200 | 0.6651 | 0.8194 | | 0.5248 | 23.19 | 1600 | 0.6892 | 0.7826 | | 0.3328 | 28.98 | 2000 | 0.7562 | 0.7993 | | 30783920c9fab77908373c2b776d617d |
mit | [] | false | model by no3 This your the Stable Diffusion model fine-tuned the azura-sd-1.4-beta3 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **sks_azura** 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). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) If you have issues or questions feel free to visit the Community Tab and start discussion about it. Here are the images used for training this concept:       | 9d8e23b7a629dae79aa0a4fb159d7b2e |
apache-2.0 | ['generated_from_trainer'] | false | distilgpt2-finetuned-restaurant-reviews This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on a subset of the Yelp restaurant reviews dataset. It achieves the following results on the evaluation set: - Loss: 3.4668 | fa2a9aa8611bc968ebb38ce0b9d25f5e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.6331 | 1.0 | 2536 | 3.5280 | | 3.5676 | 2.0 | 5072 | 3.4793 | | 3.5438 | 3.0 | 7608 | 3.4668 | | 3c93c87492465d74d7f7af3380785370 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Marathi Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. Note that this data contains only female voices. Please keep this in mind before using the model for your task, although it works very well for male voice too. When using this model, make sure that your speech input is sampled at 16kHz. | 4a1e301796b749c46285bbc53a2b8e10 |
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 Marathi `sentence` and `path` fields: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | 1f38b0b9d8329ae2dddaae0a7bf83315 |
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. processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 6e2c0c9071ccbae74add6cdd97980a09 |
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 Marathi data on OpenSLR. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re | 47e6f37565c87584bf57aa4b86a4cefb |
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("gchhablani/wav2vec2-large-xlsr-mr") model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\–\…]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | f1b0d13876cee9f0d02e12e089960374 |
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_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 14.53 % | e66e725898ed7871d62dee10b26bca85 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training 90% of the OpenSLR Marathi dataset was used for training. The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing). | 1fac2811dd2fd2ce23b968d6f714aa81 |
mit | [] | false | TEST on Stable Diffusion This is the `<AIO>` 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`:     | 64e872836a46cddb71ab2c3c0761eca0 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned-ner-finegrained 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: 0.3198 - Precision: 0.6498 - Recall: 0.6861 - F1: 0.6674 - Accuracy: 0.9083 | 1affb930c954955181a13240514833ae |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.3214 | 1.0 | 16472 | 0.3173 | 0.6260 | 0.6728 | 0.6486 | 0.9040 | | 0.266 | 2.0 | 32944 | 0.3115 | 0.6430 | 0.6857 | 0.6636 | 0.9070 | | 0.2163 | 3.0 | 49416 | 0.3198 | 0.6498 | 0.6861 | 0.6674 | 0.9083 | | 899ca10d3f6183239272ac8957e1d9d4 |
mit | ['bart', 'pytorch'] | false | BART-IT: Italian pretraining for BART sequence to sequence model BART-IT is a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a [large corpus of Italian text](https://huggingface.co/datasets/gsarti/clean_mc4_it), and can be fine-tuned on a variety of tasks. | 9c28f1744f7bb0750c455dfe02b7a63f |
mit | ['bart', 'pytorch'] | false | Fine-tuning The model in this repository is a pre-trained model without any fine-tuning. In order to use the model for a specific task, you can fine-tune it on a specific dataset. The model has been fine-tuned for the abstractive summarization task on 3 different Italian datasets: - [FanPage](https://huggingface.co/datasets/ARTeLab/fanpage) - finetuned model [here](https://huggingface.co/morenolq/bart-it-fanpage) - [IlPost](https://huggingface.co/datasets/ARTeLab/ilpost) - finetuned model [here](https://huggingface.co/morenolq/bart-it-ilpost) - [WITS](https://huggingface.co/datasets/Silvia/WITS) - finetuned model [here](https://huggingface.co/morenolq/bart-it-WITS) | 3fb1b858f2286d2467d0dac021db4799 |
mit | ['bart', 'pytorch'] | false | Usage In order to use the model, you can use the following code: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("morenolq/bart-it") model = AutoModelForSeq2SeqLM.from_pretrained("morenolq/bart-it") input_ids = tokenizer.encode("Il modello BART-IT è stato pre-addestrato su un corpus di testo italiano", return_tensors="pt") outputs = model.generate(input_ids, max_length=40, num_beams=4, early_stopping=True) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` | 3a841a2f63cef234d486c7fb2202a79d |
apache-2.0 | ['generated_from_trainer'] | false | jlg-model This model is a fine-tuned version of [datificate/gpt2-small-spanish](https://huggingface.co/datificate/gpt2-small-spanish) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4882 | f838e704f9af6f6c9d7e91b5e0de2f87 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 42 | 3.5391 | | No log | 2.0 | 84 | 3.5001 | | No log | 3.0 | 126 | 3.4882 | | 6a78915bfb1deff083ac2d7c5b4dbef3 |
mit | ['generated_from_trainer'] | false | bart-cnn-pubmed-arxiv-pubmed-v3-e100 This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1806 - Rouge1: 59.4159 - Rouge2: 48.867 - Rougel: 51.9013 - Rougelsum: 58.3382 - Gen Len: 142.0 | 0ea4ec8edcdb4ffcd8e90a1048780f29 |
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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP | fbc8dfc252eb268999b561ecb66d4436 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | 1.2541 | 1.0 | 795 | 0.9350 | 52.5594 | 32.6314 | 35.2302 | 50.1767 | 142.0 | | 0.7018 | 2.0 | 1590 | 0.8022 | 53.4804 | 35.4649 | 37.1673 | 51.2428 | 142.0 | | 0.5266 | 3.0 | 2385 | 0.7752 | 52.9462 | 34.3697 | 36.611 | 50.6922 | 142.0 | | 0.3475 | 4.0 | 3180 | 0.7771 | 53.4605 | 35.4738 | 38.5714 | 51.3798 | 142.0 | | 0.2691 | 5.0 | 3975 | 0.7424 | 54.1132 | 35.7289 | 39.2653 | 51.6822 | 141.4259 | | 0.182 | 6.0 | 4770 | 0.8037 | 53.7969 | 35.7324 | 38.4764 | 51.4929 | 141.7778 | | 0.1446 | 7.0 | 5565 | 0.7686 | 55.0274 | 38.7813 | 42.6251 | 52.9847 | 142.0 | | 0.1191 | 8.0 | 6360 | 0.7807 | 55.4651 | 38.6537 | 41.2746 | 53.578 | 141.8704 | | 0.0976 | 9.0 | 7155 | 0.8045 | 55.2843 | 40.2358 | 42.8464 | 54.0957 | 142.0 | | 0.0882 | 10.0 | 7950 | 0.8533 | 56.8288 | 41.6714 | 44.3961 | 54.9406 | 142.0 | | 0.0721 | 11.0 | 8745 | 0.8962 | 55.3187 | 40.1599 | 43.2103 | 54.1964 | 142.0 | | 0.0597 | 12.0 | 9540 | 0.8653 | 55.5706 | 40.2321 | 44.0075 | 53.9883 | 142.0 | | 0.054 | 13.0 | 10335 | 0.8566 | 55.6622 | 40.0252 | 42.6907 | 54.0548 | 142.0 | | 0.0476 | 14.0 | 11130 | 0.8900 | 57.5046 | 43.6309 | 46.449 | 55.9909 | 142.0 | | 0.0432 | 15.0 | 11925 | 0.9149 | 55.604 | 39.9591 | 43.1729 | 54.3703 | 142.0 | | 0.0403 | 16.0 | 12720 | 0.9258 | 55.1275 | 39.6566 | 42.3852 | 53.7656 | 142.0 | | 0.0351 | 17.0 | 13515 | 0.9184 | 58.2352 | 44.6109 | 47.3863 | 56.9529 | 142.0 | | 0.032 | 18.0 | 14310 | 0.9275 | 55.9687 | 41.2482 | 44.0076 | 54.0707 | 142.0 | | 0.0313 | 19.0 | 15105 | 0.9635 | 56.3574 | 41.2113 | 44.8358 | 54.6279 | 142.0 | | 0.0258 | 20.0 | 15900 | 0.9478 | 57.8445 | 44.297 | 46.8836 | 56.2003 | 142.0 | | 0.0277 | 21.0 | 16695 | 0.9363 | 58.4823 | 46.0943 | 48.7817 | 57.5883 | 141.6667 | | 0.0219 | 22.0 | 17490 | 0.9705 | 57.6022 | 43.9147 | 47.3054 | 56.3866 | 142.0 | | 0.0231 | 23.0 | 18285 | 0.9857 | 56.5809 | 42.9124 | 46.789 | 55.3897 | 142.0 | | 0.021 | 24.0 | 19080 | 1.0155 | 56.9745 | 43.8859 | 46.6109 | 55.708 | 142.0 | | 0.02 | 25.0 | 19875 | 1.0095 | 57.9702 | 45.1809 | 48.2856 | 56.6941 | 142.0 | | 0.0175 | 26.0 | 20670 | 0.9634 | 57.7023 | 45.1577 | 48.2398 | 56.5282 | 142.0 | | 0.0161 | 27.0 | 21465 | 1.0197 | 58.739 | 46.3307 | 49.2328 | 57.5778 | 142.0 | | 0.0186 | 28.0 | 22260 | 0.9790 | 56.1661 | 42.9731 | 45.8654 | 54.4365 | 142.0 | | 0.0145 | 29.0 | 23055 | 0.9883 | 55.8554 | 41.7405 | 45.177 | 54.478 | 142.0 | | 0.013 | 30.0 | 23850 | 0.9977 | 55.5831 | 41.2429 | 44.8063 | 53.886 | 142.0 | | 0.0131 | 31.0 | 24645 | 0.9765 | 57.4478 | 44.8905 | 48.1376 | 56.102 | 141.463 | | 0.0118 | 32.0 | 25440 | 1.0000 | 58.4282 | 46.6557 | 49.4122 | 57.1979 | 142.0 | | 0.0117 | 33.0 | 26235 | 0.9924 | 57.1995 | 44.4177 | 47.6248 | 56.0251 | 141.2407 | | 0.011 | 34.0 | 27030 | 1.0698 | 57.8918 | 45.925 | 49.0505 | 56.9352 | 142.0 | | 0.0093 | 35.0 | 27825 | 1.0297 | 57.7003 | 45.4556 | 47.9919 | 56.5134 | 141.8148 | | 0.0112 | 36.0 | 28620 | 1.0429 | 58.4039 | 46.6401 | 49.3897 | 57.4753 | 142.0 | | 0.0101 | 37.0 | 29415 | 1.0761 | 59.2768 | 47.5384 | 50.2152 | 57.9493 | 142.0 | | 0.0095 | 38.0 | 30210 | 1.0254 | 58.6205 | 47.246 | 50.87 | 57.7829 | 142.0 | | 0.0087 | 39.0 | 31005 | 1.0216 | 57.7667 | 44.7762 | 48.067 | 56.6006 | 142.0 | | 0.0082 | 40.0 | 31800 | 1.0587 | 58.4703 | 45.8371 | 48.5321 | 57.2036 | 142.0 | | 0.0075 | 41.0 | 32595 | 1.0621 | 58.5629 | 46.8885 | 49.5943 | 57.4579 | 142.0 | | 0.0079 | 42.0 | 33390 | 1.0845 | 57.664 | 45.5954 | 48.408 | 56.661 | 141.9815 | | 0.0076 | 43.0 | 34185 | 1.0705 | 58.1776 | 46.0435 | 49.3126 | 57.138 | 142.0 | | 0.0074 | 44.0 | 34980 | 1.0636 | 58.1022 | 46.4877 | 48.7985 | 56.9073 | 142.0 | | 0.007 | 45.0 | 35775 | 1.0810 | 57.8251 | 44.8767 | 47.8991 | 56.5977 | 142.0 | | 0.0057 | 46.0 | 36570 | 1.0560 | 58.5086 | 46.3448 | 49.2576 | 57.4386 | 142.0 | | 0.0062 | 47.0 | 37365 | 1.0903 | 58.8772 | 47.2886 | 49.9502 | 57.611 | 142.0 | | 0.0058 | 48.0 | 38160 | 1.0847 | 59.4672 | 48.3847 | 51.602 | 58.4588 | 142.0 | | 0.0061 | 49.0 | 38955 | 1.0798 | 59.5308 | 48.0396 | 50.8641 | 58.5016 | 142.0 | | 0.0062 | 50.0 | 39750 | 1.0795 | 59.5026 | 48.5319 | 51.7426 | 58.7111 | 142.0 | | 0.0051 | 51.0 | 40545 | 1.0842 | 57.7941 | 46.1198 | 48.7341 | 56.7164 | 142.0 | | 0.0057 | 52.0 | 41340 | 1.0777 | 58.6131 | 46.3924 | 49.0787 | 57.1278 | 142.0 | | 0.0039 | 53.0 | 42135 | 1.1133 | 57.6447 | 45.6699 | 48.5207 | 56.6447 | 142.0 | | 0.0038 | 54.0 | 42930 | 1.0714 | 58.1462 | 46.4616 | 49.273 | 57.2771 | 142.0 | | 0.004 | 55.0 | 43725 | 1.0852 | 58.6577 | 47.2095 | 50.4702 | 57.7724 | 142.0 | | 0.0044 | 56.0 | 44520 | 1.1152 | 59.0564 | 47.1621 | 50.2807 | 58.3122 | 142.0 | | 0.0042 | 57.0 | 45315 | 1.0831 | 58.1767 | 46.8127 | 49.9166 | 57.1833 | 142.0 | | 0.0038 | 58.0 | 46110 | 1.1156 | 57.8515 | 46.3229 | 48.6843 | 56.7218 | 142.0 | | 0.0038 | 59.0 | 46905 | 1.1105 | 57.9332 | 45.8354 | 49.27 | 57.1209 | 142.0 | | 0.0034 | 60.0 | 47700 | 1.1104 | 60.0207 | 49.2067 | 51.8751 | 58.9484 | 142.0 | | 0.0028 | 61.0 | 48495 | 1.1533 | 58.3432 | 46.8835 | 50.2868 | 57.5427 | 141.6111 | | 0.0026 | 62.0 | 49290 | 1.1441 | 58.6838 | 46.9472 | 49.9524 | 57.5287 | 142.0 | | 0.0028 | 63.0 | 50085 | 1.1232 | 58.0202 | 45.5855 | 48.6554 | 56.8368 | 141.9444 | | 0.0037 | 64.0 | 50880 | 1.1520 | 58.3905 | 47.0348 | 49.8478 | 57.3665 | 142.0 | | 0.0029 | 65.0 | 51675 | 1.1358 | 59.231 | 48.7251 | 51.6138 | 58.5718 | 142.0 | | 0.0026 | 66.0 | 52470 | 1.1559 | 58.9482 | 47.2137 | 49.4299 | 57.7235 | 142.0 | | 0.0025 | 67.0 | 53265 | 1.1272 | 59.3333 | 47.7419 | 50.7018 | 58.326 | 142.0 | | 0.0026 | 68.0 | 54060 | 1.1613 | 58.6404 | 47.3218 | 50.255 | 57.4646 | 142.0 | | 0.0015 | 69.0 | 54855 | 1.1575 | 58.7927 | 47.7018 | 50.695 | 57.796 | 142.0 | | 0.0018 | 70.0 | 55650 | 1.1463 | 58.9455 | 47.2691 | 50.176 | 57.9997 | 142.0 | | 0.0023 | 71.0 | 56445 | 1.1622 | 58.5943 | 46.9325 | 49.4159 | 57.2131 | 142.0 | | 0.0024 | 72.0 | 57240 | 1.1258 | 58.2779 | 47.4119 | 49.9836 | 57.4867 | 142.0 | | 0.0019 | 73.0 | 58035 | 1.1333 | 58.9185 | 47.5755 | 50.0765 | 57.8661 | 142.0 | | 0.0017 | 74.0 | 58830 | 1.1469 | 60.5037 | 49.4508 | 52.2863 | 59.6675 | 141.963 | | 0.0017 | 75.0 | 59625 | 1.1349 | 59.4264 | 47.4554 | 50.0383 | 58.3103 | 142.0 | | 0.0025 | 76.0 | 60420 | 1.1215 | 58.2795 | 46.9852 | 49.5787 | 57.4501 | 142.0 | | 0.0012 | 77.0 | 61215 | 1.1272 | 58.2248 | 47.0914 | 50.2569 | 57.1888 | 142.0 | | 0.001 | 78.0 | 62010 | 1.1648 | 59.3808 | 48.4901 | 51.118 | 58.6251 | 142.0 | | 0.0011 | 79.0 | 62805 | 1.1433 | 58.8697 | 47.6232 | 50.0226 | 57.6299 | 142.0 | | 0.001 | 80.0 | 63600 | 1.1486 | 59.0608 | 47.1931 | 50.1354 | 57.8687 | 142.0 | | 0.0011 | 81.0 | 64395 | 1.1695 | 58.341 | 47.0306 | 49.9269 | 57.339 | 142.0 | | 0.001 | 82.0 | 65190 | 1.1589 | 58.9283 | 48.4586 | 51.2319 | 57.9485 | 142.0 | | 0.0009 | 83.0 | 65985 | 1.1868 | 59.1377 | 48.2469 | 50.8486 | 58.1111 | 142.0 | | 0.001 | 84.0 | 66780 | 1.1664 | 58.7706 | 47.5868 | 50.5937 | 57.7824 | 142.0 | | 0.0009 | 85.0 | 67575 | 1.1719 | 57.8121 | 45.5997 | 48.2442 | 56.5272 | 142.0 | | 0.0006 | 86.0 | 68370 | 1.1662 | 58.5204 | 47.5947 | 50.1839 | 57.6431 | 142.0 | | 0.0007 | 87.0 | 69165 | 1.1668 | 59.2416 | 48.2985 | 51.0347 | 58.2794 | 142.0 | | 0.0007 | 88.0 | 69960 | 1.1619 | 58.6933 | 47.5716 | 50.6785 | 57.5726 | 142.0 | | 0.0003 | 89.0 | 70755 | 1.1765 | 59.2853 | 48.6451 | 51.3017 | 58.2603 | 142.0 | | 0.0005 | 90.0 | 71550 | 1.1766 | 59.248 | 48.5642 | 50.9843 | 58.1706 | 142.0 | | 0.0005 | 91.0 | 72345 | 1.1983 | 59.0009 | 48.311 | 51.0192 | 57.9822 | 142.0 | | 0.0006 | 92.0 | 73140 | 1.1721 | 59.1248 | 49.0902 | 51.9937 | 58.2288 | 142.0 | | 0.0003 | 93.0 | 73935 | 1.1799 | 58.2448 | 47.4011 | 49.987 | 57.515 | 142.0 | | 0.0005 | 94.0 | 74730 | 1.1900 | 59.931 | 49.6663 | 52.3233 | 58.962 | 142.0 | | 0.0004 | 95.0 | 75525 | 1.1868 | 59.5898 | 49.0004 | 51.4835 | 58.6463 | 142.0 | | 0.0093 | 96.0 | 76320 | 1.1831 | 59.9405 | 49.83 | 52.4355 | 59.0702 | 142.0 | | 0.0004 | 97.0 | 77115 | 1.1841 | 59.7379 | 49.5435 | 52.5255 | 58.8526 | 142.0 | | 0.0004 | 98.0 | 77910 | 1.1790 | 59.5515 | 49.0724 | 51.9888 | 58.5488 | 142.0 | | 0.0003 | 99.0 | 78705 | 1.1786 | 59.7712 | 49.0557 | 51.8137 | 58.7144 | 142.0 | | 0.0002 | 100.0 | 79500 | 1.1806 | 59.4159 | 48.867 | 51.9013 | 58.3382 | 142.0 | | e19102faaf5a4a701eb303ad5c32568a |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Small dysarthric Dutch This model is a fine-tuned version of [qmeeus/whisper-small-nl](https://huggingface.co/qmeeus/whisper-small-nl) on the data/copas copas-full dataset. It achieves the following results on the evaluation set: - Loss: 0.4702 - Wer: 22.1638 | ed5070e1160f3ae8f88b64141c6b2637 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP | 8d31cfbde8b1cef01844d2bdff18bdd0 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.1618 | 0.05 | 500 | 0.3787 | 28.9235 | | 0.0583 | 1.05 | 1000 | 0.3732 | 25.7702 | | 0.0382 | 2.05 | 1500 | 0.4001 | 25.4621 | | 0.0316 | 3.05 | 2000 | 0.4081 | 24.7010 | | 0.0169 | 4.05 | 2500 | 0.4325 | 24.1935 | | 0.0153 | 5.05 | 3000 | 0.4325 | 33.4179 | | 0.0074 | 6.05 | 3500 | 0.4367 | 23.9398 | | 0.0096 | 7.05 | 4000 | 0.4390 | 23.3055 | | 0.0054 | 8.05 | 4500 | 0.4441 | 23.7042 | | 0.0032 | 9.04 | 5000 | 0.4493 | 23.2693 | | 0.004 | 10.04 | 5500 | 0.4524 | 23.3418 | | 0.0048 | 11.04 | 6000 | 0.4498 | 23.7224 | | 0.001 | 12.04 | 6500 | 0.4577 | 22.8887 | | 0.0002 | 13.04 | 7000 | 0.4577 | 22.0913 | | 0.0001 | 14.04 | 7500 | 0.4616 | 22.1276 | | 0.0001 | 15.04 | 8000 | 0.4639 | 22.2726 | | 0.0001 | 16.04 | 8500 | 0.4662 | 22.1095 | | 0.0001 | 17.04 | 9000 | 0.4684 | 22.1457 | | 0.0001 | 18.04 | 9500 | 0.4697 | 22.1457 | | 0.0001 | 19.04 | 10000 | 0.4702 | 22.1638 | | 9dd9b2192235d78ccd22b634352ef172 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_8_0', 'robust-speech-event', 'generated_from_trainer', 'hf-asr-leaderboard'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1200 - num_epochs: 30.0 - mixed_precision_training: Native AMP | f9a31d3de420018308a65b3a3692074e |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_age_teens-5_sixties-5_s870 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 45a4c5a5033b29771eab466a52291731 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-issues-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: 1.2551 | 42fa619ecdacfaaff783144e35433e2a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0984 | 1.0 | 291 | 1.7081 | | 1.6512 | 2.0 | 582 | 1.4289 | | 1.4854 | 3.0 | 873 | 1.3845 | | 1.3924 | 4.0 | 1164 | 1.3844 | | 1.3375 | 5.0 | 1455 | 1.1944 | | 1.2969 | 6.0 | 1746 | 1.2848 | | 1.2443 | 7.0 | 2037 | 1.2678 | | 1.1998 | 8.0 | 2328 | 1.2151 | | 1.1805 | 9.0 | 2619 | 1.1638 | | 1.1396 | 10.0 | 2910 | 1.2131 | | 1.1333 | 11.0 | 3201 | 1.1966 | | 1.0974 | 12.0 | 3492 | 1.1687 | | 1.0822 | 13.0 | 3783 | 1.2283 | | 1.0736 | 14.0 | 4074 | 1.1640 | | 1.0595 | 15.0 | 4365 | 1.1207 | | 1.0515 | 16.0 | 4656 | 1.2551 | | ae182e2d082314b4dc1b99b016871343 |
apache-2.0 | ['generated_from_trainer'] | false | swin-finetuned-food101-e3 This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset. It achieves the following results on the evaluation set: - Loss: 0.2714 - Accuracy: 0.9227 | c5cca3809876df483a4822ee35c543c0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5565 | 1.0 | 1183 | 0.3939 | 0.8856 | | 0.3466 | 2.0 | 2366 | 0.2936 | 0.9156 | | 0.1172 | 3.0 | 3549 | 0.2714 | 0.9227 | | 33bf5cc088824f49a652d7da754b3c34 |
gpl-2.0 | ['corenlp'] | false | Core NLP model for english-kbp CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. Find more about it in [our website](https://stanfordnlp.github.io/CoreNLP) and our [GitHub repository](https://github.com/stanfordnlp/CoreNLP). This card and repo were automatically prepared with `hugging_corenlp.py` in the `stanfordnlp/huggingface-models` repo Last updated 2023-01-21 01:36:45.937 | 404a1c993b8591c331754ee51b4c408f |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_500k'] | false | MultiBERTs, Intermediate Checkpoint - Seed 2, Step 500k 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 random seeds, which causes variations in the initial weights and order of training instances. The aim is to distinguish findings that apply to a specific artifact (i.e., a particular instance of the model) from those that apply to the more general procedure. We also provide 140 intermediate checkpoints captured during the course of pre-training (we saved 28 checkpoints for the first 5 runs). The models were originally released through [http://goo.gle/multiberts](http://goo.gle/multiberts). We describe them in our paper [The MultiBERTs: BERT Reproductions for Robustness Analysis](https://arxiv.org/abs/2106.16163). This is model | 44b4930bebff558d10416af2e48a252a |
apache-2.0 | ['multiberts', 'multiberts-seed_2', 'multiberts-seed_2-step_500k'] | 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_2-step_500k') model = TFBertModel.from_pretrained("google/multiberts-seed_2-step_500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='tf') output = model(encoded_input) ``` PyTorch version: ``` from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('google/multiberts-seed_2-step_500k') model = BertModel.from_pretrained("google/multiberts-seed_2-step_500k") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | af1aeb36352b556b91cdf4eeee1b5ad9 |
creativeml-openrail-m | ['text-to-image'] | false | training params ```json { "pretrained_model_name_or_path": "CompVis/stable-diffusion-v1-4", "instance_data_dir": "./a9054d36-59d1-4374-ab1f-2ca457b539e2/instance_data", "class_data_dir": "./class_data/a-portrait-of-a-person", "output_dir": "./a9054d36-59d1-4374-ab1f-2ca457b539e2/", "with_prior_preservation": true, "prior_loss_weight": 1.0, "instance_prompt": "a portrait of [V]", "class_prompt": "a portrait of a person", "resolution": 512, "train_batch_size": 1, "gradient_accumulation_steps": 1, "gradient_checkpointing": true, "use_8bit_adam": true, "learning_rate": 5e-06, "lr_scheduler": "constant", "lr_warmup_steps": 0, "num_class_images": 200, "max_train_steps": 1050, "mixed_precision": "fp16" } ``` | 5afc05f88b14de14e01778ea17e62762 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Wed Apr 27 09:30:57 EDT 2022` - python version: `3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0]` - espnet version: `espnet 0.10.7a1` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `21d19be00089678ca27f7fce474ef8d787689512` - Commit date: `Wed Mar 16 08:06:52 2022 -0400` | 0b3e0db38fb019ee3c8c5f7f32068b2a |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|54402|97.7|2.1|0.2|0.3|2.6|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|50948|93.8|5.6|0.6|0.6|6.8|50.8| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_clean|2620|52576|97.5|2.3|0.2|0.3|2.8|32.7| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_other|2939|52343|94.1|5.3|0.6|0.7|6.6|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|54402|98.0|1.8|0.2|0.2|2.2|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|50948|94.8|4.5|0.7|0.5|5.7|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|52576|97.9|1.9|0.2|0.3|2.4|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|52343|94.9|4.3|0.7|0.5|5.6|47.0| | dd2574f95e783de79b6ef60830ff7ee6 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|288456|99.4|0.4|0.3|0.2|0.9|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|265951|97.7|1.4|0.9|0.8|3.0|50.8| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_clean|2620|281530|99.4|0.4|0.3|0.3|0.9|32.7| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_other|2939|272758|97.9|1.2|0.9|0.8|2.8|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|288456|99.4|0.3|0.3|0.2|0.8|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|265951|97.9|1.1|1.0|0.6|2.7|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|281530|99.4|0.3|0.3|0.2|0.9|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|272758|98.1|0.9|1.0|0.6|2.5|47.0| | f33eb38f6213854b0a000b58ea2f5587 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_clean|2703|68010|97.2|2.1|0.7|0.4|3.3|31.5| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/dev_other|2864|63110|92.7|5.6|1.7|1.2|8.6|50.8| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_clean|2620|65818|97.0|2.2|0.9|0.4|3.4|32.7| |decode_lm_weight0.0_asr_model_valid.loss.ave_10best/test_other|2939|65101|93.0|5.1|1.9|1.0|8.0|51.8| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_clean|2703|68010|97.5|1.8|0.8|0.4|2.9|28.2| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/dev_other|2864|63110|93.5|4.5|1.9|0.9|7.4|45.1| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_clean|2620|65818|97.3|1.9|0.8|0.4|3.0|29.3| |decode_lm_weight0.4_lm_lm_train_lm_transformer2_en_bpe5000_17epoch_asr_model_valid.loss.ave_10best/test_other|2939|65101|93.9|4.1|1.9|0.8|6.9|47.0| | 5955694316a7ba60e38ebec6caabf2f8 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/transducer/train_conformer-rnn_transducer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_conformer-rnn_transducer_raw_en_bpe5000_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 0 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 35239 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 25 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss - min keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 10000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_960_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_960_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - kaldi_ark - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0015 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - ▁THE - S - ▁AND - ▁OF - ▁TO - ▁A - ▁IN - ▁I - ▁HE - ▁THAT - ▁WAS - ED - ▁IT - '''' - ▁HIS - ING - ▁YOU - ▁WITH - ▁FOR - ▁HAD - T - ▁AS - ▁HER - ▁IS - ▁BE - ▁BUT - ▁NOT - ▁SHE - D - ▁AT - ▁ON - LY - ▁HIM - ▁THEY - ▁ALL - ▁HAVE - ▁BY - ▁SO - ▁THIS - ▁MY - ▁WHICH - ▁ME - ▁SAID - ▁FROM - ▁ONE - Y - E - ▁WERE - ▁WE - ▁NO - N - ▁THERE - ▁OR - ER - ▁AN - ▁WHEN - ▁ARE - ▁THEIR - ▁WOULD - ▁IF - ▁WHAT - ▁THEM - ▁WHO - ▁OUT - M - ▁DO - ▁WILL - ▁UP - ▁BEEN - P - R - ▁MAN - ▁THEN - ▁COULD - ▁MORE - C - ▁INTO - ▁NOW - ▁VERY - ▁YOUR - ▁SOME - ▁LITTLE - ES - ▁TIME - RE - ▁CAN - ▁LIKE - LL - ▁ABOUT - ▁HAS - ▁THAN - ▁DID - ▁UPON - ▁OVER - IN - ▁ANY - ▁WELL - ▁ONLY - B - ▁SEE - ▁GOOD - ▁OTHER - ▁TWO - L - ▁KNOW - ▁GO - ▁DOWN - ▁BEFORE - A - AL - ▁OUR - ▁OLD - ▁SHOULD - ▁MADE - ▁AFTER - ▁GREAT - ▁DAY - ▁MUST - ▁COME - ▁HOW - ▁SUCH - ▁CAME - LE - ▁WHERE - ▁US - ▁NEVER - ▁THESE - ▁MUCH - ▁DE - ▁MISTER - ▁WAY - G - ▁S - ▁MAY - ATION - ▁LONG - OR - ▁AM - ▁FIRST - ▁BACK - ▁OWN - ▁RE - ▁AGAIN - ▁SAY - ▁MEN - ▁WENT - ▁HIMSELF - ▁HERE - NESS - ▁THINK - V - IC - ▁EVEN - ▁THOUGHT - ▁HAND - ▁JUST - ▁O - ▁UN - VE - ION - ▁ITS - 'ON' - ▁MAKE - ▁MIGHT - ▁TOO - K - ▁AWAY - ▁LIFE - TH - ▁WITHOUT - ST - ▁THROUGH - ▁MOST - ▁TAKE - ▁DON - ▁EVERY - F - O - ▁SHALL - ▁THOSE - ▁EYES - AR - ▁STILL - ▁LAST - ▁HOUSE - ▁HEAD - ABLE - ▁NOTHING - ▁NIGHT - ITY - ▁LET - ▁MANY - ▁OFF - ▁BEING - ▁FOUND - ▁WHILE - EN - ▁SAW - ▁GET - ▁PEOPLE - ▁FACE - ▁YOUNG - CH - ▁UNDER - ▁ONCE - ▁TELL - AN - ▁THREE - ▁PLACE - ▁ROOM - ▁YET - ▁SAME - IL - US - U - ▁FATHER - ▁RIGHT - EL - ▁THOUGH - ▁ANOTHER - LI - RI - ▁HEART - IT - ▁PUT - ▁TOOK - ▁GIVE - ▁EVER - ▁E - ▁PART - ▁WORK - ERS - ▁LOOK - ▁NEW - ▁KING - ▁MISSUS - ▁SIR - ▁LOVE - ▁MIND - ▁LOOKED - W - RY - ▁ASKED - ▁LEFT - ET - ▁LIGHT - CK - ▁DOOR - ▁MOMENT - RO - ▁WORLD - ▁THINGS - ▁HOME - UL - ▁THING - LA - ▁WHY - ▁MOTHER - ▁ALWAYS - ▁FAR - FUL - ▁WATER - CE - IVE - UR - ▁HEARD - ▁SOMETHING - ▁SEEMED - I - LO - ▁BECAUSE - OL - ▁END - ▁TOLD - ▁CON - ▁YES - ▁GOING - ▁GOT - RA - IR - ▁WOMAN - ▁GOD - EST - TED - ▁FIND - ▁KNEW - ▁SOON - ▁EACH - ▁SIDE - H - TON - MENT - ▁OH - NE - Z - LING - ▁AGAINST - TER - ▁NAME - ▁MISS - ▁QUITE - ▁WANT - ▁YEARS - ▁FEW - ▁BETTER - ENT - ▁HALF - ▁DONE - ▁ALSO - ▁BEGAN - ▁HAVING - ▁ENOUGH - IS - ▁LADY - ▁WHOLE - LESS - ▁BOTH - ▁SEEN - ▁SET - ▁WHITE - ▁COURSE - IES - ▁VOICE - ▁CALLED - ▁D - ▁EX - ATE - ▁TURNED - ▁GAVE - ▁C - ▁POOR - MAN - UT - NA - ▁DEAR - ISH - ▁GIRL - ▁MORNING - ▁BETWEEN - LED - ▁NOR - IA - ▁AMONG - MA - ▁ - ▁SMALL - ▁REST - ▁WHOM - ▁FELT - ▁HANDS - ▁MYSELF - ▁HIGH - ▁M - ▁HOWEVER - ▁HERSELF - ▁P - CO - ▁STOOD - ID - ▁KIND - ▁HUNDRED - AS - ▁ROUND - ▁ALMOST - TY - ▁SINCE - ▁G - AM - ▁LA - SE - ▁BOY - ▁MA - ▁PERHAPS - ▁WORDS - ATED - ▁HO - X - ▁MO - ▁SAT - ▁REPLIED - ▁FOUR - ▁ANYTHING - ▁TILL - ▁UNTIL - ▁BLACK - TION - ▁CRIED - RU - TE - ▁FACT - ▁HELP - ▁NEXT - ▁LOOKING - ▁DOES - ▁FRIEND - ▁LAY - ANCE - ▁POWER - ▁BROUGHT - VER - ▁FIRE - ▁KEEP - PO - FF - ▁COUNTRY - ▁SEA - ▁WORD - ▁CAR - ▁DAYS - ▁TOGETHER - ▁IMP - ▁REASON - KE - ▁INDEED - TING - ▁MATTER - ▁FULL - ▁TEN - TIC - ▁LAND - ▁RATHER - ▁AIR - ▁HOPE - ▁DA - ▁OPEN - ▁FEET - ▁EN - ▁FIVE - ▁POINT - ▁CO - OM - ▁LARGE - ▁B - ▁CL - ME - ▁GONE - ▁CHILD - INE - GG - ▁BEST - ▁DIS - UM - ▁HARD - ▁LORD - OUS - ▁WIFE - ▁SURE - ▁FORM - DE - ▁DEATH - ANT - ▁NATURE - ▁BA - ▁CARE - ▁BELIEVE - PP - ▁NEAR - ▁RO - ▁RED - ▁WAR - IE - ▁SPEAK - ▁FEAR - ▁CASE - ▁TAKEN - ▁ALONG - ▁CANNOT - ▁HEAR - ▁THEMSELVES - CI - ▁PRESENT - AD - ▁MASTER - ▁SON - ▁THUS - ▁LI - ▁LESS - ▁SUN - ▁TRUE - IM - IOUS - ▁THOUSAND - ▁MONEY - ▁W - ▁BEHIND - ▁CHILDREN - ▁DOCTOR - AC - ▁TWENTY - ▁WISH - ▁SOUND - ▁WHOSE - ▁LEAVE - ▁ANSWERED - ▁THOU - ▁DUR - ▁HA - ▁CERTAIN - ▁PO - ▁PASSED - GE - TO - ▁ARM - ▁LO - ▁STATE - ▁ALONE - TA - ▁SHOW - ▁NEED - ▁LIVE - ND - ▁DEAD - ENCE - ▁STRONG - ▁PRE - ▁TI - ▁GROUND - SH - TI - ▁SHORT - IAN - UN - ▁PRO - ▁HORSE - MI - ▁PRINCE - ARD - ▁FELL - ▁ORDER - ▁CALL - AT - ▁GIVEN - ▁DARK - ▁THEREFORE - ▁CLOSE - ▁BODY - ▁OTHERS - ▁SENT - ▁SECOND - ▁OFTEN - ▁CA - ▁MANNER - MO - NI - ▁BRING - ▁QUESTION - ▁HOUR - ▁BO - AGE - ▁ST - ▁TURN - ▁TABLE - ▁GENERAL - ▁EARTH - ▁BED - ▁REALLY - ▁SIX - 'NO' - IST - ▁BECOME - ▁USE - ▁READ - ▁SE - ▁VI - ▁COMING - ▁EVERYTHING - ▁EM - ▁ABOVE - ▁EVENING - ▁BEAUTIFUL - ▁FEEL - ▁RAN - ▁LEAST - ▁LAW - ▁ALREADY - ▁MEAN - ▁ROSE - WARD - ▁ITSELF - ▁SOUL - ▁SUDDENLY - ▁AROUND - RED - ▁ANSWER - ICAL - ▁RA - ▁WIND - ▁FINE - ▁WON - ▁WHETHER - ▁KNOWN - BER - NG - ▁TA - ▁CAPTAIN - ▁EYE - ▁PERSON - ▁WOMEN - ▁SORT - ▁ASK - ▁BROTHER - ▁USED - ▁HELD - ▁BIG - ▁RETURNED - ▁STRANGE - ▁BU - ▁PER - ▁FREE - ▁EITHER - ▁WITHIN - ▁DOUBT - ▁YEAR - ▁CLEAR - ▁SIGHT - ▁GRA - ▁LOST - ▁KEPT - ▁F - PE - ▁BAR - ▁TOWN - ▁SLEEP - ARY - ▁HAIR - ▁FRIENDS - ▁DREAM - ▁FELLOW - PER - ▁DEEP - QUE - ▁BECAME - ▁REAL - ▁PAST - ▁MAKING - RING - ▁COMP - ▁ACT - ▁BAD - HO - STER - ▁YE - ▁MEANS - ▁RUN - MEN - ▁DAUGHTER - ▁SENSE - ▁CITY - ▁SOMETIMES - ▁TOWARDS - ▁ROAD - ▁SP - ▁LU - ▁READY - ▁FOOT - ▁COLD - ▁SA - ▁LETTER - ▁ELSE - ▁MAR - ▁STA - BE - ▁TRUTH - ▁LE - BO - ▁BUSINESS - CHE - ▁JOHN - ▁SUBJECT - ▁COURT - ▁IDEA - ILY - ▁RIVER - ATING - ▁FAMILY - HE - ▁DIDN - ▁GLAD - ▁SEVERAL - IAL - ▁UNDERSTAND - ▁SC - ▁POSSIBLE - ▁DIFFERENT - ▁RETURN - ▁ARMS - ▁LOW - ▁HOLD - ▁TALK - ▁RU - ▁WINDOW - ▁INTEREST - ▁SISTER - SON - ▁SH - ▁BLOOD - ▁SAYS - ▁CAP - ▁DI - ▁HUMAN - ▁CAUSE - NCE - ▁THANK - ▁LATE - GO - ▁CUT - ▁ACROSS - ▁STORY - NT - ▁COUNT - ▁ABLE - DY - LEY - ▁NUMBER - ▁STAND - ▁CHURCH - ▁THY - ▁SUPPOSE - LES - BLE - OP - ▁EFFECT - BY - ▁K - ▁NA - ▁SPOKE - ▁MET - ▁GREEN - ▁HUSBAND - ▁RESPECT - ▁PA - ▁FOLLOWED - ▁REMEMBER - ▁LONGER - ▁AGE - ▁TAKING - ▁LINE - ▁SEEM - ▁HAPPY - LAND - EM - ▁STAY - ▁PLAY - ▁COMMON - ▁GA - ▁BOOK - ▁TIMES - ▁OBJECT - ▁SEVEN - QUI - DO - UND - ▁FL - ▁PRETTY - ▁FAIR - WAY - ▁WOOD - ▁REACHED - ▁APPEARED - ▁SWEET - ▁FALL - BA - ▁PASS - ▁SIGN - ▁TREE - IONS - ▁GARDEN - ▁ILL - ▁ART - ▁REMAIN - ▁OPENED - ▁BRIGHT - ▁STREET - ▁TROUBLE - ▁PAIN - ▁CONTINUED - ▁SCHOOL - OUR - ▁CARRIED - ▁SAYING - HA - ▁CHANGE - ▁FOLLOW - ▁GOLD - ▁SW - ▁FEELING - ▁COMMAND - ▁BEAR - ▁CERTAINLY - ▁BLUE - ▁NE - CA - ▁WILD - ▁ACCOUNT - ▁OUGHT - UD - ▁T - ▁BREATH - ▁WANTED - ▁RI - ▁HEAVEN - ▁PURPOSE - ▁CHARACTER - ▁RICH - ▁PE - ▁DRESS - OS - FA - ▁TH - ▁ENGLISH - ▁CHANCE - ▁SHIP - ▁VIEW - ▁TOWARD - AK - ▁JOY - ▁JA - ▁HAR - ▁NEITHER - ▁FORCE - ▁UNCLE - DER - ▁PLAN - ▁PRINCESS - DI - ▁CHIEF - ▁HAT - ▁LIVED - ▁AB - ▁VISIT - ▁MOR - TEN - ▁WALL - UC - ▁MINE - ▁PLEASURE - ▁SMILE - ▁FRONT - ▁HU - ▁DEAL - OW - ▁FURTHER - GED - ▁TRIED - DA - VA - ▁NONE - ▁ENTERED - ▁QUEEN - ▁PAY - ▁EL - ▁EXCEPT - ▁SHA - ▁FORWARD - ▁EIGHT - ▁ADDED - ▁PUBLIC - ▁EIGHTEEN - ▁STAR - ▁HAPPENED - ▁LED - ▁WALKED - ▁ALTHOUGH - ▁LATER - ▁SPIRIT - ▁WALK - ▁BIT - ▁MEET - LIN - ▁FI - LT - ▁MOUTH - ▁WAIT - ▁HOURS - ▁LIVING - ▁YOURSELF - ▁FAST - ▁CHA - ▁HALL - ▁BEYOND - ▁BOAT - ▁SECRET - ENS - ▁CHAIR - RN - ▁RECEIVED - ▁CAT - RESS - ▁DESIRE - ▁GENTLEMAN - UGH - ▁LAID - EVER - ▁OCCASION - ▁WONDER - ▁GU - ▁PARTY - DEN - ▁FISH - ▁SEND - ▁NEARLY - ▁TRY - CON - ▁SEEMS - RS - ▁BELL - ▁BRA - ▁SILENCE - IG - ▁GUARD - ▁DIE - ▁DOING - ▁TU - ▁COR - ▁EARLY - ▁BANK - ▁FIGURE - 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▁DWELT - ▁NEPHEW - ▁RAILROAD - ▁SEPTEMBER - ▁DEVICE - ▁WHEAT - ▁GILBERT - ▁ELEGANT - ▁ADVERTISE - ▁RATIONAL - ▁TURTLE - ▁BROOD - ▁ASSEMBLY - ▁CULTIVATE - ▁EDITOR - ▁SPECIMEN - ▁UNDOUBTEDLY - ▁WHALE - ▁DROPPING - ▁BALLOON - ▁MEDICAL - COMB - ▁COMPOSITION - ▁FOOTSTEPS - ▁LAUNCELOT - ▁DISCOURSE - ▁ERRAND - ▁CONVERSE - ▁ADVANCING - ▁DOWNSTAIRS - ▁TUMULT - ▁CORRUPT - ▁SUFFICE - ▁ANGUISH - ▁SHAGGY - ▁RETIRE - ▁TIMBER - ▁BLAZE - ▁ABSTRACT - ▁EMBROIDER - ▁PHOTOGRAPH - ▁PROSPERITY - ▁TERRIBLY - ▁TERRITORY - ▁THRESHOLD - ▁PAVEMENT - ▁INJURED - ▁LIMP - ▁AGITATION - ▁RASCAL - ▁PRESUME - ▁OBSERVING - ▁OBSTACLE - ▁SIMPLICITY - ▁SLUMBER - ▁SUPPLIED - ▁COMBINATION - ▁DRAIN - ▁WILDERNESS - ▁BELIEVING - ▁VILLAIN - ▁RECKLESS - ▁INJURY - ▁CLAPP - ▁FRIDAY - ▁HERCULES - ▁KENNEDY - ▁SYMPTOM - ▁SLEDGE - ▁CEILING - ▁LEMON - ▁PLAGUE - ▁MONDAY - ▁CANVAS - ▁IMPATIENCE - ▁UNCOMFORTABLE - ▁ACCESS - ▁FROZEN - ▁SENATOR - ▁FRANZ - ▁SWIMMING - ▁BARRIER - ▁ADJUST - ▁COMPARISON - ▁PROCLAIM - ▁WRINKL - ▁OVERLOOK - ▁MITYA - ▁GUILT - ▁PERCEPTION - ▁PRECAUTION - ▁SPECTATOR - ▁SURPRISING - ▁DISTRACT - ▁DISDAIN - ▁BONNET - ▁MAGNET - ▁PROFESS - ▁CONFOUND - ▁NARRATIVE - ▁STRUCTURE - ▁SKETCH - ▁ULTIMATE - ▁GLOBE - ▁INSECT - FICIENCY - ▁ORCHARD - ▁AMIABLE - ▁DESCENT - ▁INDEPENDENCE - ▁MANUFACTURE - ▁SPRINKLE - ▁NIGHTINGALE - ▁CUSHION - ▁EMINENT - ▁SCOTT - ▁ARRAY - ▁COSETTE - ▁WAVING - ▁EXTRACT - ▁IRREGULAR - ▁PERSECUT - ▁DERIVED - ▁WITHDREW - ▁CAUTION - ▁SUSPICIOUS - ▁MEMORIES - ▁NOWHERE - ▁SUBTLE - ▁THOROUGH - Q - ▁APPROPRIATE - ▁SLAUGHTER - ▁YOURSELVES - ▁THUMB - ▁TWAS - ▁ABODE - ▁BIDDING - ▁CONSPICUOUS - ▁REBECCA - ▁SERGEANT - ▁APRON - ▁ANTICIPATE - ▁DISCIPLINE - ▁GLANCING - ▁PILGRIM - ▁SULLEN - ▁CONTRIBUTE - ▁PRAIRIE - ▁CARVED - ▁COMMERCE - ▁EXCLAMATION - ▁MUSCULAR - ▁NOVEMBER - ▁PHENOMENA - ▁SYMBOL - ▁UMBRELLA - ▁DIMINISH - ▁PARLOUR - ▁THREATENING - ▁STUMP - ▁EXTENSIVE - ▁PLEASING - ▁REMEMBRANCE - ▁COMBINED - ▁SHERIFF - ▁SHAFT - ▁LAURA - ▁INTERCOURSE - ▁STRICKEN - ▁SUPPLIES - ▁LANDLORD - ▁SHRINK - ▁PRICK - ▁CAESAR - ▁DRUG - ▁BEWILDERED - ▁NAUTILUS - ▁BRUTAL - ▁COMMERCIAL - ▁MAGGIE - ▁SPHERE - ▁VIRGIN - ▁BRETHREN - ▁DESTINY - ▁POLICY - ▁TERRIFIED - ▁HOUSEKEEPER - ▁CRAZY - ▁ARDENT - ▁DISCERN - ▁WRAP - ▁MARQUIS - ▁RUSSIA - MOUTH - ▁BRITAIN - ▁HARBOUR - ▁CONCERT - ▁DONKEY - ▁DAMAGE - ▁SLIM - ABOUT - ▁LUXURY - ▁MONSTROUS - ▁TENDENCY - ▁PARADISE - ▁CULTURE - ▁JULIUS - ▁RAOUL - ▁REMEDY - ▁DECAY - ▁SCOLD - ▁SPLIT - ▁ASSAULT - ▁DECEMBER - ▁MOSCOW - ▁EXPLORE - ▁TROUSERS - ▁WRIST - PIECE - ▁MUSKET - ▁VALENTINE - ▁TYRANT - ▁ABRAHAM - ▁MEDIUM - ▁ARTIFICIAL - ▁FACULTY - ▁OBLIGATION - ▁RESEMBLANCE - ▁INQUIRIES - ▁DETAIN - ▁SWARM - ▁PLEDGE - ▁ADMIRABLE - ▁DEFECT - ▁SUPERINTEND - ▁PATRIOT - ▁CLUNG - ▁DISMAL - ▁RECIT - ▁IGNOR - ▁AMELIA - ▁JUSTIFY - ▁ELEPHANT - ▁ESTIMATE - ▁KNELT - ▁SERVING - ▁WHIM - ▁SHRILL - ▁STUDIO - ▁TEXT - ▁ALEXANDER - ▁WROUGHT - ▁ABUNDANT - ▁SITUATED - ▁REGAIN - ▁FIERY - ▁SNEER - ▁SWEAT - ▁GLARE - ▁NIGH - ▁ESCORT - ▁INEVITABLE - ▁PSMITH - ▁RELUCTANT - ▁PRECEDING - ▁RESORT - ▁OUTRAGE - ▁AMBASSADOR - ▁CONSOLATION - ▁RECOGNITION - ▁REMORSE - ▁BEHALF - ▁FORMIDABLE - ▁GRAVITY - ▁DIVIDE - ▁CONFRONT - ▁GIGANTIC - ▁OCTOBER - ▁FLANK - ▁SLEW - ▁CLARA - ▁FILM - ▁BULK - ▁POMP - ▁ELEANOR - ▁EMPHASIS - ▁JAPANESE - ▁CAVALRY - ▁EXCLUSIVE - ▁PERFUME - ▁BRONZE - ▁FEDERAL - ▁LIQUID - ▁RUBBING - ▁OVEN - DOLPH - ▁CONVULS - ▁DEPRIVED - ▁RESPONSIBILITY - ▁SIGNIFICANT - ▁WAISTCOAT - ▁CLUSTER - ▁MARTHA - ▁REVERSE - ▁ATTORNEY - ▁DROOP - ▁SKILFUL - ▁HABITUAL - ▁PUMP - ▁INTERVEN - ▁OWL - ▁CONJECTURE - ▁FANTASTIC - ▁RESPONSIBLE - ▁DESTINED - ▁DOCUMENT - ▁THEREUPON - ▁GODDESS - ▁PACIFIC - ▁WARRANT - ▁COSTUME - ▁BRIDLE - ▁CALIFORNIA - ▁DEMOCRATIC - ▁EUSTACE - ▁SQUIRREL - ▁UNCOMMON - ▁MARVELLOUS - ▁PLOUGH - ▁TRAGEDY - ▁VAULT - ▁HESITATE - ▁REFRAIN - ▁ADMIRING - ▁CORPORAL - ▁ENTITLED - ▁SHREWD - ▁SQUEEZ - ▁ACCURATE - ▁TEMPEST - ▁MONUMENT - ▁SIEGE - ▁CHINESE - ▁RAVEN - ▁LOUNG - ▁ASSASSIN - ▁INFLICT - ▁AGITATED - ▁DESIRABLE - ▁EARLIEST - ▁LAUNCH - ▁PILOT - ▁PULSE - ▁MUTE - LEIGH - ▁LIQUOR - ▁SCARECROW - ▁SKULL - ▁DESOLATE - ▁SUBLIME - ▁SERENE - ▁RECESS - ▁WAKING - ▁CHARLOTTE - ▁CIRCULAR - ▁INJUSTICE - ▁PINOCCHIO - ▁PRISCILLA - ▁THYSELF - ▁OCCURRENCE - ▁CASUAL - ▁FRANTIC - ▁LEGEND - ▁FERTIL - ▁BACKGROUND - ▁DELICACY - ▁ESTRALLA - ▁MANUSCRIPT - ▁RESPONSE - ▁UNIVERSITY - ▁WOLVES - ▁SCANDAL - ▁STUMBLE - ▁HOARSE - ▁BODILY - ▁CONVENT - ▁EXAMINING - ▁INCAPABLE - ▁PERCEIVING - ▁PHILADELPHIA - ▁SUBSEQUENT - ▁THIEVES - ▁ACCUMULAT - ▁DAMSEL - ▁SCOTCH - ▁UNDERNEATH - ▁NOBILITY - ▁SMASH - ▁REVOLT - ▁ENGAGE - ▁CATHEDRAL - ▁CHAMPION - ▁DESPATCH - ▁ETERNITY - ▁JANUARY - ▁PLEADED - ▁PROBABILITY - ▁JIMMIE - ▁PARALLEL - ▁FISHERMAN - ▁JERRY - ▁SWORE - ▁DRAUGHT - ▁OPPONENT - ▁PRIMITIVE - ▁SIGNIFICANCE - ▁SUBSTANTIAL - ▁AMAZED - ▁DUNBAR - ▁COMMEND - ▁CONTEMPLATE - ▁TESTIMONY - ▁IMPERIAL - ▁ADAPT - ▁JUICE - ▁CALAMIT - CULAR - ▁CHATEAU - ▁PHOENIX - ▁PRUDENT - ▁SOLUTION - ▁VILLEFORT - ▁REACTION - ▁RELAX - ▁YU - ▁PROHIBIT - ▁DISTRUST - ▁PLUNDER - ▁WELFARE - ▁NAVIGAT - ▁PARLOR - ▁LAZY - ▁DETACH - OMETER - ▁PRIV - ▁DISCOURAGE - ▁OBSTINATE - ▁REJOICING - ▁SERMON - ▁VEHICLE - ▁FANCIES - ▁ENLIGHTEN - ▁ACUTE - ▁ILLUSION - ▁ANTHEA - ▁MARTIAN - ▁EXCITE - ▁GENEROSITY - OLOGIST - ▁AMAZING - ▁UNWORTHY - ▁INTERNAL - ▁INCENSE - ▁VIBRAT - ▁ADHERE - ROACH - ▁FEBRUARY - ▁MEXICAN - ▁POTATOES - ▁INCESSANT - ▁INTERPOSED - ▁PARCEL - ▁VEXED - ▁PROMOTE - MIDST - ▁ARISTOCRAT - ▁CYRIL - ▁EMBARK - ▁ABUNDANCE - ▁LITERALLY - ▁SURGEON - ▁TERRACE - ▁ATLANTIC - ▁MARTYR - ▁SPECK - ▁SENATE - ▁LOAF - ▁ADMINISTER - ▁APPREHEND - ▁SUBDUED - ▁TEMPORARY - ▁DOMINION - ▁ELABORATE - ▁DIGNIFIED - ▁ELIZA - ▁SPLASH - ▁CONSEIL - ▁DEXTER - ▁UNSEEN - ▁TRAGIC - VOCATION - ▁GRATIFY - ▁BACHELOR - ▁DEFENSE - ▁EXCURSION - ▁FACULTIES - ▁PROPRIETOR - ▁SYMPATHETIC - ▁UNNECESSARY - ▁RADIANT - ▁VACANT - ▁OUNCE - ▁SCREW - ▁PHENOMENON - ▁PROMINENT - ▁WORRIED - ▁STUDIES - ▁CLIMATE - ▁KEITH - ▁ARAMIS - ▁BLISS - ▁CONTINUAL - ▁SURPASS - ▁HEBREW - ▁IDENTITY - ▁PROVOKE - ▁TEMPERAMENT - ▁CHARIOT - ▁HARBOR - ▁NINTH - ▁PRIOR - ▁DESIROUS - ▁JERUSALEM - ▁UNDERTAKING - ▁EDISON - ▁MIRTH - ▁SCOUT - ▁APPARATUS - ▁ILLUSTRATION - ▁INTELLIGIBLE - ▁INVARIABLY - ▁PIERCED - ▁REVIEW - ▁FLICKER - ▁HAZARD - ▁REVELATION - ▁DIXON - ▁EXCITING - ▁GOSPEL - ▁CONSTANCE - ▁OVERTAKE - ▁GUINEA - ▁ALADDIN - ▁CHICAGO - ▁TULLIVER - ▁HAMILTON - ▁GARRISON - ▁DISCIPLE - ▁INTENSITY - ▁TRAITOR - ▁CHANCELLOR - ▁PROVERB - ▁DAGGER - ▁FORESEE - ▁CONFIDE - ▁GLIMMER - ▁CHAUVELIN - ▁ILLUSTRATE - ▁VOLUNTEER - ▁JUNGLE - ▁STREAK - ▁SUNRISE - ▁DISSOLV - ▁QUEST - ▁AWHILE - ▁FELICITY - ▁LEGISLATURE - ▁LEONORA - ▁MAGAZINE - ▁PITIFUL - ▁COLONY - ▁SHAWL - ▁ARRIVING - ▁FUNDAMENTAL - ▁CARPENTER - ▁OVERFLOW - ▁EXPAND - ▁HARVEST - ▁FEMININE - ▁INNUMERABLE - ▁SCRAMBLE - ▁TWENTIETH - ▁TRIFLING - ▁GHASTL - ▁CONQUEST - ▁DANIEL - ▁FACILIT - ▁FORSAKE - ▁BEHAVIOUR - ▁GORGEOUS - ▁PRODUCING - ▁HAPPIER - ▁PROMISING - ▁RAINBOW - ▁INSTINCTIVELY - ▁DECREE - ▁EYEBROWS - ▁IRRESISTIBLE - ▁PHARAOH - ▁SCROOGE - ▁UNNATURAL - ▁CRUMBS - ▁REFINED - ▁DREARY - ▁TRENCH - ▁CONVINCE - ▁FRINGE - ▁EXTREMITY - ▁INTIMACY - ▁SCOUNDREL - ▁SUFFRAGE - ▁UNEASINESS - ▁BARRICADE - ▁CIRCULAT - ▁SAMUEL - ▁BRUCE - ▁DARCY - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true joint_net_conf: joint_space_size: 640 model_conf: ctc_weight: 0.3 report_cer: true report_wer: true use_preprocessor: true token_type: bpe bpemodel: data/en_token_list/bpe_unigram5000/bpe.model non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' frontend: default frontend_conf: n_fft: 512 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transducer decoder_conf: rnn_type: lstm num_layers: 1 hidden_size: 512 dropout: 0.1 dropout_embed: 0.2 required: - output_dir - token_list version: 0.10.7a1 distributed: true ``` </details> | 5fe8ebedbdf0f1696fb0ef7f45db27c5 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` | d0eb12a64082e6cda7667fb18267906b |
apache-2.0 | [] | false | Cross-Encoder for MS MARCO - EN-DE This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task. The model can be used for Information Retrieval: See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html). The training code is available in this repository, see `train_script.py`. | 460de442989ec7de68263b60cb60e475 |
apache-2.0 | [] | false | Usage with SentenceTransformers When you have [SentenceTransformers](https://www.sbert.net/) installed, you can use the model like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('model_name', max_length=512) query = 'How many people live in Berlin?' docs = ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'] pairs = [(query, doc) for doc in docs] scores = model.predict(pairs) ``` | f2cb87118b22f6a2ddb35d2c0108a75c |
apache-2.0 | [] | false | Usage with Transformers With the transformers library, you can use the model like this: ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('model_name') tokenizer = AutoTokenizer.from_pretrained('model_name') features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt") model.eval() with torch.no_grad(): scores = model(**features).logits print(scores) ``` | d87d5132f29f054791fe3d89021f0141 |
apache-2.0 | [] | false | Performance The performance was evaluated on three datasets: - **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47. - **TREC-DL19 DE-EN**: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10. - **GermanDPR DE-DE**: The [GermanDPR](https://www.deepset.ai/germanquad) dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27. We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search. | Model-Name | TREC-DL19 EN-EN | TREC-DL19 DE-EN | GermanDPR DE-DE | Docs / Sec | | ------------- |:-------------:| :-----: | :---: | :----: | | BM25 | 45.46 | - | 35.85 | -| | **Cross-Encoder Re-Rankers** | | | | | [cross-encoder/msmarco-MiniLM-L6-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L6-en-de-v1) | 72.43 | 65.53 | 46.77 | 1600 | | [cross-encoder/msmarco-MiniLM-L12-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L12-en-de-v1) | 72.94 | 66.07 | 49.91 | 900 | | [svalabs/cross-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/cross-electra-ms-marco-german-uncased) (DE only) | - | - | 53.67 | 260 | | [deepset/gbert-base-germandpr-reranking](https://huggingface.co/deepset/gbert-base-germandpr-reranking) (DE only) | - | - | 53.59 | 260 | | **Bi-Encoders (re-ranking)** | | | | | [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned) | 63.38 | 58.28 | 37.88 | 940 | | [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch) | 65.51 | 58.69 | 38.32 | 940 | | [svalabs/bi-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/bi-electra-ms-marco-german-uncased) (DE only) | - | - | 34.31 | 450 | | [deepset/gbert-base-germandpr-question_encoder](https://huggingface.co/deepset/gbert-base-germandpr-question_encoder) (DE only) | - | - | 42.55 | 450 | Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU. | f5772d6224d5bd825bdddd74096f52a5 |
apache-2.0 | ['generated_from_trainer'] | false | all-roberta-large-v1-home-8-16-5 This model is a fine-tuned version of [sentence-transformers/all-roberta-large-v1](https://huggingface.co/sentence-transformers/all-roberta-large-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3789 - Accuracy: 0.3356 | 0ccf58b2104a69aa54726b5fe8735636 |
creativeml-openrail-m | [] | false | Basic explanation Token and Class words are what guide the AI to produce images similar to the trained style/object/character. Include any mix of these words in the prompt to produce verying results, or exclude them to have a less pronounced effect. There is usually at least a slight stylistic effect even without the words, but it is recommended to include at least one. Adding token word/phrase class word/phrase at the start of the prompt in that order produces results most similar to the trained concept, but they can be included elsewhere as well. Some models produce better results when not including all token/class words. | e2febb228ae84886585a850489fe96e0 |
creativeml-openrail-m | [] | false | Usage When using this model by itself, it is not necessary to use any keywords, but they will strengthen the style effect. Rossmix produces the best results, while ross-any also works quite well. Ross based on wd has a more of an illustration feel, but works best when mixed with other models. Rossmix and ross-any may work better with clip-skip 2, while ross most likely works better with clip skip 1. | 9989dbae12db5db14edf313a8a1f455a |
creativeml-openrail-m | [] | false | Example images Example images also include 3 extra mixes that include ross or ross-any. Positive: `m_ross, (illustration), (masterpiece), ((best quality)), (ultra-detailed), (official art), ((portrait of a beautiful girl)), upper body`\ Negative: `lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name` Ground truth  Examples   768x1024 without highres fix  | 4a4c7d07ecaa46ca0fca58df4ade1951 |
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: 0.3554 - Accuracy: 0.902 - F1: 0.9001 | b43b456b902568ca52ad589bd2f2a083 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0993 | 1.0 | 125 | 0.5742 | 0.8045 | 0.7747 | | 0.4436 | 2.0 | 250 | 0.3554 | 0.902 | 0.9001 | | 66b28d179e0c949398d742da005cea9c |
apache-2.0 | ['generated_from_trainer'] | false | vit-for-kaggle-mayo-clinic This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5538 - Accuracy: 0.7616 | 0bb8d3be41f9780f5104bc93d2b1a940 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 | 5e4bf0cc8746fcf8d00ace5ae396cda2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 10 | 0.5944 | 0.7483 | | No log | 2.0 | 20 | 0.5640 | 0.7483 | | No log | 3.0 | 30 | 0.5582 | 0.7483 | | No log | 4.0 | 40 | 0.5585 | 0.7483 | | No log | 5.0 | 50 | 0.5598 | 0.7483 | | No log | 6.0 | 60 | 0.5484 | 0.7483 | | No log | 7.0 | 70 | 0.5524 | 0.7417 | | No log | 8.0 | 80 | 0.5538 | 0.7616 | | 6280c55635aec42fe94033f580ae9c12 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Large v2 Azerbaijani This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the mozilla-foundation/common_voice_11_0 az dataset. It achieves the following results on the evaluation set: - Loss: 0.9435 - Wer: 38.4615 | d64e1704e72253c5827045118f97f281 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP | 3dc9d2e5a2a8f35373dcd09e66b62fda |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.0 | 999.0 | 1000 | 0.8373 | 39.6450 | | 0.0 | 1999.0 | 2000 | 0.9435 | 38.4615 | | 0.0 | 2999.0 | 3000 | 1.0010 | 43.1953 | | 0.0 | 3999.0 | 4000 | 1.0380 | 44.3787 | | 0.0 | 4999.0 | 5000 | 1.0529 | 43.7870 | | fe82c6bb8baf5b3f941554d3b797d064 |
apache-2.0 | ['generated_from_trainer'] | false | small-mlm-glue-qnli-target-glue-qqp This model is a fine-tuned version of [muhtasham/small-mlm-glue-qnli](https://huggingface.co/muhtasham/small-mlm-glue-qnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3296 - Accuracy: 0.8511 - F1: 0.8117 | 88a7b05faf24623e1a8e8aaf8d73e744 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.4762 | 0.04 | 500 | 0.4247 | 0.7897 | 0.7473 | | 0.4188 | 0.09 | 1000 | 0.3880 | 0.8126 | 0.7702 | | 0.4011 | 0.13 | 1500 | 0.3760 | 0.8194 | 0.7750 | | 0.387 | 0.18 | 2000 | 0.3779 | 0.8189 | 0.7866 | | 0.3802 | 0.22 | 2500 | 0.3642 | 0.8320 | 0.7958 | | 0.3606 | 0.26 | 3000 | 0.3526 | 0.8358 | 0.7972 | | 0.3604 | 0.31 | 3500 | 0.3337 | 0.8495 | 0.8010 | | 0.3538 | 0.35 | 4000 | 0.3341 | 0.8483 | 0.8102 | | 0.3582 | 0.4 | 4500 | 0.3293 | 0.8503 | 0.8106 | | 0.345 | 0.44 | 5000 | 0.3296 | 0.8511 | 0.8117 | | a8a2d8aa0971b8e9cde30642afe6c693 |
apache-2.0 | ['Tensorflow'] | false | Tensorpacks Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables. The model and its training code has been mainly taken from: [Tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) . Regarding the dataset, please check: [Xu Zhong et. all. - Image-based table recognition: data, model, and evaluation](https://arxiv.org/abs/1911.10683). The model has been trained on detecting rows and columns for tables. As rows and column bounding boxes are not a priori an element of the annotations they are calculated using the bounding boxes of the cells and the intrinsic structure of the enclosed HTML. The code has been adapted so that it can be used in a **deep**doctection pipeline. | 57b952cbb6512e8f73008db454dd2198 |
apache-2.0 | ['Tensorflow'] | false | This is an inference model only To reduce the size of the checkpoint we removed all variables that are not necessary for inference. Therefore it cannot be used for fine-tuning. To fine tune this model please check this [model](https://huggingface.co/deepdoctection/tp_casc_rcnn_X_32xd4_50_FPN_GN_2FC_pubtabnet_rc). | bd0695692260d3a7269350dbe0968835 |
apache-2.0 | ['Tensorflow'] | false | How this model was trained. To recreate the model run on the **deep**doctection framework, run: ```python >>> import os >>> from deep_doctection.datasets import DatasetRegistry >>> from deep_doctection.eval import MetricRegistry >>> from deep_doctection.utils import get_configs_dir_path >>> from deep_doctection.train import train_faster_rcnn pubtabnet = DatasetRegistry.get_dataset("pubtabnet") pubtabnet.dataflow.categories.set_cat_to_sub_cat({"ITEM":"row_col"}) pubtabnet.dataflow.categories.filter_categories(categories=["ROW","COLUMN"]) path_config_yaml=os.path.join(get_configs_dir_path(),"tp/rows/conf_frcnn_rows.yaml") path_weights = "" dataset_train = pubtabnet config_overwrite=["TRAIN.STEPS_PER_EPOCH=500","TRAIN.STARTING_EPOCH=1", "TRAIN.CHECKPOINT_PERIOD=50"] build_train_config=["max_datapoints=500000","rows_and_cols=True"] dataset_val = pubtabnet build_val_config = ["max_datapoints=2000","rows_and_cols=True"] coco_metric = MetricRegistry.get_metric("coco") coco_metric.set_params(max_detections=[50,200,600], area_range=[[0,1000000],[0,200],[200,800],[800,1000000]]) train_faster_rcnn(path_config_yaml=path_config_yaml, dataset_train=dataset_train, path_weights=path_weights, config_overwrite=config_overwrite, log_dir="/path/to/dir", build_train_config=build_train_config, dataset_val=dataset_val, build_val_config=build_val_config, metric=coco_metric, pipeline_component_name="ImageLayoutService" ) ``` | eb144d6d863aaca8bd0434a1e56acc67 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 28695114f2771ac3d2a9cc0b5fb30a2c3262e49a pip install -e . cd egs2/librimix/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librimix_asr_train_asr_transformer_multispkr_raw_en_char_sp ``` <!-- Generated by scripts/utils/show_asr_result.sh --> | 6d3e47101561e670154df5458d394ae4 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | Environments - date: `Thu Nov 10 14:58:09 EST 2022` - python version: `3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.12.1` - Git hash: `b3c185d5d707bb385b74f42df2cc59bcf7d7e754` - Commit date: `Wed Nov 9 22:00:30 2022 -0500` | 46256ed6bacb9277f264ca745a37bcaf |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_multi_asrtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test|6000|111243|80.4|17.4|2.2|3.8|23.5|88.0| | 5925b81e8aa5ab55e09b5324ad039ac3 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_multi_asrtrue_lm_lm_train_lm_transformer_en_char_valid.loss.ave_asr_model_valid.acc.ave/test|6000|590408|90.5|6.1|3.5|3.9|13.5|88.0| | 04f2c667389e2188fa37e25e1b625d96 |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/tuning/train_asr_transformer_multispkr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_multispkr_raw_en_char_sp 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 local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 45 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5.0 grad_clip_type: 2.0 grad_noise: false accum_grad: 1 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 5000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_char_sp/train/speech_shape - exp/asr_stats_raw_en_char_sp/train/text_shape.char - exp/asr_stats_raw_en_char_sp/train/text_spk2_shape.char valid_shape_file: - exp/asr_stats_raw_en_char_sp/valid/speech_shape - exp/asr_stats_raw_en_char_sp/valid/text_shape.char - exp/asr_stats_raw_en_char_sp/valid/text_spk2_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_sp/wav.scp - speech - sound - - dump/raw/train_sp/text_spk1 - text - text - - dump/raw/train_sp/text_spk2 - text_spk2 - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text_spk1 - text - text - - dump/raw/dev/text_spk2 - text_spk2 - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - <blank> - <unk> - <space> - E - T - A - O - N - I - H - S - R - D - L - U - M - C - W - F - G - Y - P - B - V - K - '''' - X - J - Q - Z - <sos/eos> init: xavier_uniform input_size: null ctc_conf: reduce: false joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: fs: 16k specaug: null specaug_conf: {} normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_char_sp/train/feats_stats.npz model: pit_espnet model_conf: ctc_weight: 0.2 lsm_weight: 0.1 length_normalized_loss: false num_inf: 2 num_ref: 2 preencoder: null preencoder_conf: {} encoder: transformer_multispkr encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 8 num_blocks_sd: 4 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true num_inf: 2 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 preprocessor: multi preprocessor_conf: text_name: - text - text_spk2 required: - output_dir - token_list version: '202209' distributed: false ``` </details> | 7b5f0b1e06822200fcb76cb5aa6997c5 |
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