license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
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apache-2.0 | ['lexical normalization'] | false | Fine-tuned ByT5-small for MultiLexNorm (Danish version)  This is the official release of the fine-tuned models for **the winning entry** to the [*W-NUT 2021: Multilingual Lexical Normalization (MultiLexNorm)* shared task](https://nois... | 000bf92aece21f9154d0ef6698707e3b |
creativeml-openrail-m | [] | false | Just Bunch of merged models. Don't actually remember formulas. Majority are anime related. BlueFish V1.5 I dislike the most,maybe because of the prompting. It is supposed to get more semi-realistic look. For me it's too plastic. Added comparisons between V1,1.5 and V2 Potenial Other Models that I used to merge those:... | b74c7c7d8543d883c974369e07e2e69a |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v1-finetuned-squad This model is a fine-tuned version of [albert-base-v1](https://huggingface.co/albert-base-v1) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 0.9426 | 20a7c2075df852f21b974c599f38498a |
apache-2.0 | ['generated_from_keras_callback'] | false | TestZee/t5-small-finetuned-custom-wion-test 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: 1.9773 - Validation Loss: 0.8028 - Epoch: 9 | 9df535c2a3c3207992a922c39ed8c50c |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.2933 | 0.9052 | 0 | | 2.3077 | 0.8923 | 1 | | 2.1972 | 0.8797 | 2 | | 2.1740 | 0.8677 | 3 | | 2.1535 | 0.8564 | 4 | | 2.1772 |... | 61ff1614d8eb18793ce6ccf5d6bd7ee6 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | Baseline Model trained on diabetespmxrsn1x to apply classification on diabetes **Metrics of the best model:** accuracy 0.871795 average_precision 0.518856 roc_auc 0.883333 recall_macro 0.883333 f1_macro 0.801996 Name: DecisionTreeClassifier(class_weight='balanced'... | f966cfe58cbce233831f335b3ed237f3 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.co... | 90b786f9b4a277f2f0e7ef7710c33f07 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;,EasyPreprocessor(types= continuous dirty_float ... free_string useless cholesterol True False ... False False glucose True False ... False False hdl_chol True False ... False False chol_hdl_ratio ... | b0e133d851ff16e0df8d023aae0607fc |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, max_depth=1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrap... | 8cd423db729be18ae80cf3653c59bbfa |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, max_depth=1))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">Easy... | 9b9fe87864ea8b178b3d396106fbeea6 |
apache-2.0 | ['tabular-classification', 'baseline-trainer'] | false | x27;, max_depth=1)</pre></div></div></div></div></div></div></div> **Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain). **Logs of training** including the models tried in the process can be found in logs.txt | 6ad2b7337c22b5af22cd72c01e928e45 |
mit | [] | false | model by misas4444 This your the Stable Diffusion model fine-tuned the mario action figure concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks action figure** You can also train your own concepts and upload them to the library by using [this notebook... | 6395b26818a47546e12af43ccd34666a |
mit | ['indo-gpt2-small'] | false | Indo GPT-2 Small Indo GPT-2 Small is a language model based on the [GPT-2 model](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). It was trained on the latest (late December 2020) Indonesian Wikipedia articles. The model was originally HuggingFace's pretrained [E... | 707d1f0e270fdf8790fb83173b4f9e9d |
mit | ['indo-gpt2-small'] | false | params | Arch. | Training /Validation data (text) | |-------------------|---------|-------------|---------------------------------------| | `indo-gpt2-small` | 124M | GPT-2 Small | Indonesian Wikipedia (3.1 GB of text) | | 47c07799bde8af32b885c4e5dfdb42eb |
mit | ['indo-gpt2-small'] | false | Evaluation Results The model was trained for only 1 epoch and the following is the final result once the training ended. | epoch | train loss | valid loss | perplexity | total time | |-------|------------|------------|------------|------------| | 0 | 2.981 | 2.936 | 18.85 | 2:45:25 | | 7990fcefd723b0494922f3a18ca7b6b1 |
mit | ['indo-gpt2-small'] | false | Load Model and Byte-level Tokenizer ```python from transformers import GPT2TokenizerFast, GPT2LMHeadModel pretrained_name = "w11wo/indo-gpt2-small" tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_name) tokenizer.model_max_length = 1024 model = GPT2LMHeadModel.from_pretrained(pretrained_name) ``` | 9216082faaab3667f8a931235551085c |
mit | ['indo-gpt2-small'] | false | generate output using top-k sampling sample_outputs = model.generate(input_ids, pad_token_id=50256, do_sample=True, max_length=40, min_length=40, top_k=40, ... | c1dc1bd88c08fb4e6f08b3dbb720e61c |
apache-2.0 | ['generated_from_trainer'] | false | swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0599 - Accuracy: 0.9793 | e1bbe3e9b6564ae17d51506a024c2337 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2282 | 1.0 | 190 | 0.1057 | 0.9656 | | 0.1751 | 2.0 | 380 | 0.0798 | 0.9730 | | 0.1449 | 3.0 | 570 | 0.0599 | 0.... | bae18955bdfadc54720a94f15c4cc192 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1583 - F1: 0.8563 | 8fa8c5bb4082566eea130f54c03940c1 |
mit | [] | false | Midjourney style on Stable Diffusion This is the `<midjourney-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. ... | 2b3caae47481584a14fc21dd969474d2 |
apache-2.0 | ['microsoft/deberta-v3-xsmall'] | false | Cross-Encoder for Natural Language Inference This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall) | 33acc4efc5fbc30078f34f3e6870473a |
apache-2.0 | ['microsoft/deberta-v3-xsmall'] | false | Performance - Accuracy on SNLI-test dataset: 91.64 - Accuracy on MNLI mismatched set: 87.77 For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html | ccc396cfd669e4d7103a3ff982349176 |
apache-2.0 | ['microsoft/deberta-v3-xsmall'] | false | Usage Pre-trained models can be used like this: ```python from sentence_transformers import CrossEncoder model = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall') scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving... | d4abfffc9083a632196103e195da68c4 |
apache-2.0 | ['microsoft/deberta-v3-xsmall'] | false | Usage with Transformers AutoModel You can use the model also directly with Transformers library (without SentenceTransformers library): ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-debert... | edac7acba42a638cc2e5b03edb1862a1 |
apache-2.0 | ['microsoft/deberta-v3-xsmall'] | false | Zero-Shot Classification This model can also be used for zero-shot-classification: ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-xsmall') sent = "Apple just announced the newest iPhone X" candidate_labels = ["technology", "sports", "... | 92e678a0a90e26d52bc6234adf4c68fc |
apache-2.0 | ['translation'] | false | opus-mt-fi-tvl * source languages: fi * target languages: tvl * OPUS readme: [fi-tvl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fi-tvl/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](http... | 244b2f318652478baa704405a520db35 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ES dataset. It achieves the following results on the evaluation set: - Loss: 0.1461 - Wer: 1.0063 | 5ef0d699f2504a2690aeda27802ab79d |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-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_sch... | 90e4906e7847a9c88a4b2311b3272ce7 |
apache-2.0 | ['automatic-speech-recognition', 'generated_from_trainer', 'hf-asr-leaderboard', 'mozilla-foundation/common_voice_7_0', 'robust-speech-event'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.953 | 0.15 | 1000 | 2.9528 | 1.0 | | 1.1519 | 0.3 | 2000 | 0.3735 | 1.0357 | | 1.0278 | 0.45 | 3000 | 0.2529 | 1.039... | e89878c45a16e7a31c8b5ccbf8d24526 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4707 - Wer: 0.3411 | a00e0c020591315337c06cefeb81ed6c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4575 | 4.0 | 500 | 1.3367 | 0.9724 | | 0.594 | 8.0 | 1000 | 0.4365 | 0.4390 | | 0.2195 | 12.0 | 1500 | 0.4438 | 0.3955 | |... | 74ba0b6f9b00560e560f4b145f09c097 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | 6510ee877297f138cae09d632141e3c9 |
apache-2.0 | ['generated_from_trainer'] | false | bert-base-uncased-finetuned-mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4520 - Accuracy: 0.8578 - F1: 0.9003 | 35ba889466a0a06cfb8225934899cddb |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.4169 | 0.8039 | 0.8639 | | No log | 2.0 | 460 | 0.4299 | 0.8137 | 0.875 | | 0.4242 |... | f607b79eb369b570f370f3d6c84122fd |
mit | ['generated_from_trainer'] | false | 22_12_13_luther_blocks_xl_fp16_5ep This model is a fine-tuned version of [malteos/gpt2-xl-wechsel-german](https://huggingface.co/malteos/gpt2-xl-wechsel-german) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8833 - Accuracy: 0.4196 | 64f7cce250defce16659305f6b3fb1cf |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epoc... | 1a15ebb22bcd764dc0a05bcd976fe8c8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.19 | 50 | 3.0276 | 0.3997 | | No log | 0.38 | 100 | 2.9185 | 0.4143 | | No log | 0.58 | 150 | 2.8846 | 0.... | c5fd882f18048b666d05cd1be629b58a |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_unispeech-ml_s756 Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When... | fa73cceab3dd96f54c89f01b8fef8c29 |
apache-2.0 | ['generated_from_keras_callback'] | false | TEdetection_distiBERT_mLM_V2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: | 41e5d7dbc11e061cb00bdf5d43e6fdab |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | SD-1-5-Ram Dreambooth model trained by RamAnanth1 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... | 672ac818301cdfecbe9638fad6473b08 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-ro-lr0.001 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.8309 - Bleu: 5.8837 - Gen Len: 18.2656 | 8a79316526a753e82500b623196bf6ae |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.01 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP | 68894af5ed6fb150f58c98095737ecf1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | 0.9442 | 1.0 | 7629 | 1.8309 | 5.8837 | 18.2656 | | 10e6f807a12ed62c3fdf25a79e34e3c1 |
apache-2.0 | [] | false | RAG This is a non-finetuned version of the RAG-Sequence model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al. Rag consits of a *question encoder*, *retriever* and a *generator*. The ret... | 1c6a7d1b0ab8d418b7bf419bc0c5e6aa |
apache-2.0 | [] | false | Usage: *Note*: the model uses the *dummy* retriever as a default. Better results are obtained by using the full retriever, by setting `config.index_name="legacy"` and `config.use_dummy_dataset=False`. The model can be fine-tuned as follows: ```python from transformers import RagTokenizer, RagRetriever, RagTokenForG... | db130bfb23400c1e8233517d34f00d27 |
apache-2.0 | ['stanza', 'token-classification'] | false | Stanza model for Faroese (fo) Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Find more about it in [our website](htt... | 3e3a60ff1a7bba1380aa71604a8453d9 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | SD-1-5-Felix Dreambooth model trained by fpeters with [buildspace's DreamBooth](https://colab.research.google.com/github/buildspace/diffusers/blob/main/examples/dreambooth/DreamBooth_Stable_Diffusion.ipynb) notebook Build your own using the [AI Avatar project](https://buildspace.so/builds/ai-avatar)! To get started... | 9a02ffb6cf459ebe6e7827665e9c9763 |
apache-2.0 | ['generated_from_keras_callback'] | false | tmplujkwod0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5292 - Train Accuracy: 0.875 - Validation Loss: 0.5870 - Validation Accuracy: 0.5 - Epoch: 1 | 1ed677e727d825a136d7379e5f1253fa |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.6565 | 0.625 | 0.7534 | 0.5 | 0 | | 0.5292 | 0.875 | 0.5870 | 0.5 ... | b57586958738d60625a2c4f6b1311ddb |
apache-2.0 | ['generated_from_trainer'] | false | XLRS-torgo This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 1.6074 | e35f55b32f60b7b5c21b8c376801774d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.554 | 3.54 | 400 | inf | 1.6325 | | 2.3441 | 7.08 | 800 | inf | 1.6406 | | 1.7386 | 10.62 | 1200 | inf | 1.5875 | |... | 37b8854bb5700a9b07aafe367c842edb |
mit | ['generated_from_trainer'] | false | rubert-tiny2-war-posts-finetuned This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7097 | 49e6e2dc0b0a36e7c2d74e3c78d6e882 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 | c5f967290aa3959b3c54e73a8da71fe5 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.3638 | 1.0 | 1011 | 3.9762 | | 4.1361 | 2.0 | 2022 | 3.8631 | | 4.036 | 3.0 | 3033 | 3.7991 | | 3.9467 | 4.0 | 4044 | 3.7706 ... | ab53a530456278aa51b87fa7bd719ae3 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | En-Nso_update3 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4218 - Bleu: 24.5765 | 91b4d007615943f57e27143fb1ff7a46 |
apache-2.0 | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 3.6568 | 1.0 | 867 | 3.0185 | 18.4004 | | 2.7574 | 2.0 | 1734 | 2.7774 | 20.3167 | | 2.4522 | 3.0 | 2601 | 2.6436 | 2... | a3b0d121d8675d117c09ecd73d6eff25 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-it-lrs This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1483 - Bleu: 10.4962 - Gen Len: 51.8247 | b2e55b92fb665319de639ba0ddf5d10b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.9618 | 1.0 | 1125 | 2.8717 | 4.6688 | 66.512 | | 1.7256 | 2.0 | 2250 | 2.7638 | 6.5673 | 56.7267 | | 1.6133 ... | 5fb692789c67005150d8f0d6b3cffadd |
other | ['stable-diffusion', 'text-to-image'] | false | ProjectTurn8 <img src="https://i.imgur.com/WiS93wx.png" width="1000" height=""> ●What is this? We are submitting a variety of merge models that are well done. ●How to use Put the downloaded model file into stable-diffusion-webui\models\Stable-diffusion It is recommended to use bad_prompt_version2 of TextualInver... | 31709b07be2949aa5969458b8a3dacb7 |
other | ['stable-diffusion', 'text-to-image'] | false | ProjectTurn8-Jupiter ●What is this? This model is a merge of Stella and basil_mix using the extension sdweb-merge-block-weighted-gui. Compared to Earth, the skin and clothing textures are more realistic and improved. However, if you do not use Hires. fix, the look will be lost. ●Recommended setting CFG Scale : 9±... | 7f5956ccc2f7236c836716724aec0c68 |
other | ['stable-diffusion', 'text-to-image'] | false | ProjectTurn8-Stella <img src="https://i.imgur.com/qUTbReP.png" width="1000" height=""> ●What is this? This is a merged model based on anything+everything ver2. It is mainly suited for writing 2D cute girls. Basically, other models are created based on this model. ●Recommended setting CFG Scale : 8±3 Clip skip : ... | 771b6559c861086a3bd907a7fd212f45 |
other | ['stable-diffusion', 'text-to-image'] | false | ProjectTurn8-Earth <img src="https://i.imgur.com/efIyvTu.png" width="1000" height=""> ●What is this? This model was created using the extension sdweb-merge-block-weighted-gui. It is possible to create more realistic illustrations compared to Stella. ●Recommended setting CFG Scale : 6±1 Sampling method : DPM++ SDE... | 6109cf3d34088fa0b3eb034e0a91c873 |
other | ['stable-diffusion', 'text-to-image'] | false | ProjectTurn8-Luna <img src="https://i.imgur.com/pnVSdat.png" width="1000" height=""> ●What is this? This model is a cross between Earth and Stella. ●Recommended setting CFG Scale : 6±1 Sampling method : DPM++ SDE Karras | 29bc124b9d3bda9f05fc6d5b0ca5a319 |
apache-2.0 | ['translation'] | false | opus-mt-fr-niu * source languages: fr * target languages: niu * OPUS readme: [fr-niu](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-niu/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](http... | e7f9799c46573af6b05ae289a3f032e2 |
apache-2.0 | ['generated_from_trainer'] | false | Vin10-P3 This model is a fine-tuned version of [HuyenNguyen/Vin9-P3](https://huggingface.co/HuyenNguyen/Vin9-P3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2151 - Wer: 11.4787 | 8123802c25a1847281eae64b671c4eac |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1822 | 0.1 | 200 | 0.2155 | 11.0880 | | 0.1687 | 0.21 | 400 | 0.2222 | 12.0311 | | 0.1688 | 0.31 | 600 | 0.2151 | 11.478... | 3d68a1f4f154e53bd5554b4f4bcbe893 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | wav2vec2-xls-r-300m-Br-small This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.0573 - Wer: 0.6675 | 712f642981b1b46dd2f116a878610a35 |
apache-2.0 | ['generated_from_trainer', 'robust-speech-event', 'hf-asr-leaderboard'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.7464 | 2.79 | 400 | 1.7474 | 1.1018 | | 1.1117 | 5.59 | 800 | 0.9434 | 0.8697 | | 0.6481 | 8.39 | 1200 | 0.9251 | 0.7910 | |... | abb9417945018cea192091c0ff0e4495 |
apache-2.0 | ['generated_from_trainer'] | false | hate_trained_final This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.5543 - F1: 0.7698 | 8a3f23560ac561c6cdafee87cc427510 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.460503761236833e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 | 23ec1560678a2497213ba14215739fc8 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.463 | 1.0 | 1125 | 0.5213 | 0.7384 | | 0.3943 | 2.0 | 2250 | 0.5134 | 0.7534 | | 0.3407 | 3.0 | 3375 | 0.5400 | 0.7666 | |... | 0b0b48ccf07d0ce8e8561f89d572a3c7 |
apache-2.0 | ['automatic-speech-recognition', 'es'] | false | exp_w2v2t_es_xlsr-53_s377 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition using the train split of [Common Voice 7.0 (es)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech... | c7a4978ccb6e5c89dadc8a19b11a9567 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-SARC_withcontext 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: 0.4736 - Accuracy: 0.7732 | f7d6ebf594fd6cb6cd892df99417ce1b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.4749 | 1.0 | 50539 | 0.4736 | 0.7732 | | 9d9bb19291d9f0427b418d243e1e56eb |
apache-2.0 | ['automatic-speech-recognition', 'AI_Light_Dance.py', 'generated_from_trainer'] | false | ai-light-dance_singing_ft_wav2vec2-large-lv60 This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the AI_LIGHT_DANCE.PY - ONSET-SINGING dataset. It achieves the following results on the evaluation set: - Loss: 0.4542 - Wer: 0.2088 | 54d00b494768f2e97eafa9647ce0caf0 |
apache-2.0 | ['automatic-speech-recognition', 'AI_Light_Dance.py', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10.0 - mixed_precision_tr... | d308a994473e3ccecc66c71599a0def2 |
apache-2.0 | ['automatic-speech-recognition', 'AI_Light_Dance.py', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.7432 | 1.0 | 4422 | 0.8939 | 0.6323 | | 0.5484 | 2.0 | 8844 | 0.6393 | 0.3557 | | 0.3919 | 3.0 | 13266 | 0.5315 | 0.283... | 11c790341e3094f5f0ddab36b7b1bb33 |
mit | ['luxembourgish', 'lëtzebuergesch', 'text generation', 'transfer learning'] | false | LuxGPT-2 based GER GPT-2 model for Text Generation in luxembourgish language, trained on 711 MB of text data, consisting of RTL.lu news articles, comments, parlament speeches, the luxembourgish Wikipedia, Newscrawl, Webcrawl and subtitles. Created via transfer learning with an English base model, feature space mapping... | 078848d0ff6d71bab5cdfb5a430fddf6 |
mit | ['luxembourgish', 'lëtzebuergesch', 'text generation', 'transfer learning'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("laurabernardy/LuxGPT2-basedEN") model = AutoModelForCausalLM.from_pretrained("laurabernardy/LuxGPT2-basedEN") ``` | 54e1480dd9e55734051044fbbc249469 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.6893 | cbb8f4b622d52bdf5667e160566fd07c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.099 | 1.0 | 5 | 2.6076 | | 2.7996 | 2.0 | 10 | 2.5412 | | 2.7876 | 3.0 | 15 | 2.6641 | | b5683beadb0a1788f0873b690e9ae1f0 |
mit | [] | false | wlop-style on Stable Diffusion This is the `<wlop-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also... | 8022ffed4f6f04c0ca1433e7c4f1c574 |
gpl-3.0 | [] | false | Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 300 dimensions and were trained using the fasttext algorithm on published clinical case reports found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/) . See the paper here: https://pub... | 641441cd00a512a2ebd1c092e6d31f76 |
other | ['vision', 'image-segmentation'] | false | MaskFormer MaskFormer model trained on ADE20k semantic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch... | 4c26e91fb082b56b140791893006a9ce |
other | ['vision', 'image-segmentation'] | false | How to use Here is how to use this model: ```python from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation from PIL import Image import requests url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" image = Image.open(requests.g... | 7c5d28d95bbc1a7d46f365c5ffbd28b2 |
other | ['vision', 'image-segmentation'] | false | we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transform... | 3ed8c2ea89f153f8aceebf6925f96bf4 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7767 - Matthews Correlation: 0.5492 | fc53d48d3ce195a3489c2db75eee37b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5244 | 1.0 | 535 | 0.5349 | 0.4240 | | 0.3471 | 2.0 | 1070 | 0.5087 | 0.5079 | | 0.2... | f19df8013b3e539c0fe9f580d155df11 |
creativeml-openrail-m | ['text-to-image'] | false | 826ebfd7-60b9-4372-96dc-10d7e8202157 Dreambooth model trained by tzvc with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github... | 8ab718f193a7818422c8824047ed20e2 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Croatian-SET | Feature | Description | | --- | --- | | **Name** | `hr_udv25_croatianset_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_ed... | 609a686424d83abc140a311464b06856 |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (3855 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `Agcfpay`, `Agcfpdy`, `Agcfpgy`, `Agcfpiy`, `Agcfply`, `Agcfpny`, `Agcfsay`, `Agcfsdy`, `Ag... | cf3aa4ac6252f75f4d28656f848e42dc |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.97 | | `TOKEN_P` | 99.97 | | `TOKEN_R` | 99.96 | | `TOKEN_ACC` | 99.99 | | `SENTS_F` | 98.90 | | `SENTS_P` | 99.06 | | `SENTS_R` | 98.75 | | `TAG_ACC` | 96.40 | | `POS_ACC` | 98.50 | | `MORPH_ACC` | 96.78 | | `DEP_UAS` | 92.41 | | `DEP_LAS` | 87.03 | | `LEMMA_A... | 00d67608ccbb32f6043b426557d3cff6 |
cc-by-4.0 | [] | false | MalayalamBERT-Scratch MalayalamBERT is a Malayalam BERT model trained on publicly available Malayalam monolingual datasets from scratch. Preliminary details on the dataset, models, and baseline results can be found in our [<a href='https://arxiv.org/abs/2211.11418'> paper </a>]. Citing: ``` @article{joshi2022l3cube... | 28f3b17859312c41a9197e634e43d5e0 |
mit | [] | false | model by BenjiKan This your the Stable Diffusion model fine-tuned the Magikarp pokemon concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks pokemon** You can also train your own concepts and upload them to the library by using [this notebook](https://... | ac7435f2a29387440e819e816c252434 |
apache-2.0 | ['generated_from_keras_callback'] | false | Haakf/allsides_right_text_headline_conc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.2228 - Validation Loss: 2.1132 - Epoch: 5 | dead7ab3056e0b18cedd784924b0cddb |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'Polynomia... | d47d4ede355db23623a09718e6257087 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.3212 | 2.1756 | 0 | | 2.3138 | 2.1886 | 1 | | 2.3149 | 2.1993 | 2 | | 2.2860 | 2.1089 | 3 | | 2.2580 | 2.1514 | 4 | | 2.2228 |... | 4649d89ec3db5b4865b15149edb509e1 |
apache-2.0 | ['generated_from_trainer'] | false | tiny-mlm-wikitext-custom-tokenizer This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.4940 | 615150c56803b587bfc7db693d14e0e4 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 8.1543 | 0.4 | 500 | 7.6501 | | 7.4342 | 0.8 | 1000 | 7.5531 | | 7.3656 | 1.2 | 1500 | nan | | 7.2844 | 1.6 | 2000 | 7.4543 ... | 95c0297f18e244b5779b50721ac852f9 |
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