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 | ['generated_from_trainer'] | false | all-roberta-large-v1-auto_and_commute-2-16-5-oos 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: 2.2614 - Accuracy: 0.4289 | 108ffc9115e9be49d749af09f1891e73 |
openrail | [] | false | hairornament，purple eyes，bangs，flower， silver hair，pointy ears，breasts,solo,hair flower,ribbon,looking at viewer,hair ribbon，medium breasts，braid，cleavagewide_sleeves，bare shoulders,smile,pleated skirt,frilled sleeves,girl,breasts,boy,nipples,hetero,open_mouth,jewelry,blush,sex,sex from behind,bangs,earrings,detached c... | 2e19a0050132af61f17e9cc4ad96bb16 |
mit | ['deberta-v1', 'fill-mask'] | false | DeBERTa: Decoding-enhanced BERT with Disentangled Attention [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data. Please check the [official repositor... | d9f44dc7450d626d50b2f75770bb110d |
mit | ['deberta-v1', 'fill-mask'] | false | Fine-tuning on NLU tasks We present the dev results on SQuAD 1.1/2.0 and MNLI tasks. | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m | |-------------------|-----------|-----------|--------| | RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 | | XLNet-Large | -/- | -/80.2 | 86.8 | | **DeBERTa-... | 017ec1b8c36aceaf16dbfce2e04efba7 |
mit | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | S2T-SMALL-LIBRISPEECH-ASR `s2t-small-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR). The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speec... | 7a2866621b0c2569b3f13e89a1cbec4d |
mit | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | How to use As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the transcripts by passing the speech features to the model. *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the filter bank features. Make sure... | a73263de3404a1f2f0fa9aaceafa6a91 |
mit | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | Evaluation on LibriSpeech Test The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset. ```python from datasets import load_dataset, load_metric from transformers import Speech2TextForConditionalGeneration, Speec... | 698c82ae288f422f369af4d796c49151 |
mit | ['speech', 'audio', 'automatic-speech-recognition', 'hf-asr-leaderboard'] | false | change to "other" for other test dataset wer = load_metric("wer") model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr").to("cuda") processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr", do_upper_case=True) librispeech_eval = librispeech_eval... | 9fc1c3cd68354f6bdcb999365bee192c |
apache-2.0 | ['generated_from_trainer'] | false | t5-base-gec-combine_data This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5624 - Rouge1: 76.0801 - Rouge2: 65.3291 - Rougel: 75.4097 - Rougelsum: 75.4189 - Gen Len: 16.8811 | d3a44287803e6ce715edc4c5d243862e |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP | 9d4bbf7bd33385d0ab3340737b84a67b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.8413 | 0.45 | 500 | 0.6549 | 74.1413 | 62.083 | 73.4159 | 73.4206 | 16... | fd3f4d8e5a3e9167c354a892cca57b12 |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-wikisql-sql-nl-nl-sql 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: 0.1931 - Bleu: 41.8507 - Gen Len: 16.5973 | c8d91d33de0be1082aebe89956a06b49 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.2649 | 1.0 | 8097 | 0.2251 | 39.6277 | 16.6655 | | 0.2387 | 2.0 | 16194 | 0.2063 | 40.9063 | 16.6415 | | 0.2217 ... | 37710c570dd32dc4f382a9c64eb9b0d8 |
mit | ['text generation', 'pytorch', 'causal-lm', 'gpt_neox'] | false | Model Description ProofGPT-v0.1 is a 1.3B parameter language model based on the GPT-NeoX architecture and trained on the [proof-pile](https://huggingface.co/datasets/hoskinson-center/proof-pile) (v1.1). The model is initialized with [pythia-1.3b](https://huggingface.co/EleutherAI/pythia-1.3b) weights. ProofGPT-v0.1's... | b8666a6cce9ec5bb99c2fd85cddddb5c |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_vp-100k_accent_us-5_england-5_s878 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 (en)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using th... | 59dea76033e9e71449e70b7efebef5ac |
apache-2.0 | ['generated_from_trainer'] | false | flan-t5-large-da-multiwoz_500 This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3826 - Accuracy: 37.4297 - Num: 3689 - Gen Len: 16.4142 | 173eed2c58a52c2d6149fb09913929e7 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----:|:-------:| | 1.3527 | 0.47 | 200 | 0.5645 | 25.0872 | 3689 | 12.6606 | | 0.6276 | 0.93 | 400 | 0.4722 | 31.0261 | 36... | d29a29a55bf6f7c9cbe82df8d030b60e |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4519 - Wer: 32.0113 | f2640f1a3e5164198a84e5e691a5dbfa |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precis... | 97eb5847ece1e3deccb2985d43a9f83d |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1011 | 2.44 | 1000 | 0.3075 | 34.6313 | | 0.0264 | 4.89 | 2000 | 0.3558 | 33.1288 | | 0.0025 | 7.33 | 3000 | 0.4214 | 32.591... | 4d5a566c39f0a916c05650778655f708 |
mit | ['generated_from_trainer'] | false | bart-cnn-science-v3-e5 This model is a fine-tuned version of [theojolliffe/bart-cnn-science](https://huggingface.co/theojolliffe/bart-cnn-science) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8090 - Rouge1: 54.0053 - Rouge2: 35.5018 - Rougel: 37.3204 - Rougelsum: 51.5456 -... | f4de8e96bd02dc12e4ea36a618d541a8 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| | No log | 1.0 | 398 | 0.9935 | 51.9669 | 31.8139 | 34.4748 | 49.5311 | ... | 6ed31250e5c8234b97f5d5a92172aa76 |
apache-2.0 | ['masked-lm'] | false | Transformer language model for Croatian and Serbian Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps). Leipzig, OSCAR and srWac datasets | Model | | 50fde572647a0c7a0c3a1ef78e36c6c3 |
apache-2.0 | ['masked-lm'] | false | params | Arch. | Training data | |--------------------------------|--------------------------------|-------|-----------------------------------| | `Andrija/SRoBERTa-L` | 80M | Third | Leipzig Corpus, OSCAR and srWac (6 GB of text) | | 08e0015d86be13de159cdde1cadca19a |
apache-2.0 | ['generated_from_trainer'] | false | Bert-finetuned-Sarc This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7396 - Accuracy: 0.8447 | 66816a5700767f03f15ec84268b47aa2 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.7355 | 1.0 | 16664 | 0.7112 | 0.8292 | | 0.6394 | 2.0 | 33328 | 0.7396 | 0.8447 | | 31d3f0ad3b12307298243f657b1057c4 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-Test This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2833 - Accuracy: 0.902 - F1: 0.9037 | 3e5804b2cc8b6183f35303a2ab3bb1d3 |
mit | [] | false | **PEGASUS-ClaimsKG** PEGASUS-LARGE fine-tuned on the full [ClaimsKG](https://data.gesis.org/claimskg/) dataset. - BERTScore: F1 score: 0.871 || Precision score: 0.881 || Recall score: 0.864 -Rouge-1 Score(precision=0.781, recall=0.737, fmeasure=0.743) -Rouge-2 Score(precision=0.660, recall=0.626, fmeasure=0.631) ... | a4f8eb62d52d9e139031013c333ad980 |
apache-2.0 | ['distilbert', 'seq2seq', 'text-classification'] | false | Example 1 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions") text = ''' Customer portion will have the dependency of , you know , fifty five probably has to be on XGEVA before we can start that track , but we can at least start the enablement t... | 0c1421d5cc01d4a817234440f71bcb9d |
apache-2.0 | ['distilbert', 'seq2seq', 'text-classification'] | false | Example 2 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions") text = ''' India, officially the Republic of India, is a country in South Asia. ''' summarizer(text) ``` | dab34cbc612cd91bad3050c258e6413a |
apache-2.0 | ['distilbert', 'seq2seq', 'text-classification'] | false | Example 3 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Decisions") text = ''' We have been running the business successfully for over a decade now. ''' summarizer(text) ``` | 9d8fab3f9f927d4e00646210b663c00d |
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: 0.8486 - Matthews Correlation: 0.5209 | 32d597b7e9f1461b23236bf434372b94 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5265 | 1.0 | 535 | 0.5479 | 0.4049 | | 0.3571 | 2.0 | 1070 | 0.5002 | 0.5164 | | 0.2... | 91cf0429a3ac79f4745811382ca0caf9 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-uncased-label_category 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: - Loss: 0.5520 - F1: 0.8027 | 2eb3caad52e7717d80e8cfe0709d488e |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.1642 | 0.59 | 100 | 1.0930 | 0.2505 | | 1.071 | 1.18 | 200 | 0.9768 | 0.3991 | | 0.9616 | 1.78 | 300 | 0.8551 | 0.5597 | |... | 76f88f074c6f0bd0bc7c49d2577136b1 |
apache-2.0 | [] | false | distilbert-base-it-cased We are sharing smaller versions of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) that handle a custom number of languages. Our versions give exactly the same representations produced by the original model which preserves the original accuracy... | c7dea6f57c2a57353a93c4dbc87bec52 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/distilbert-base-it-cased") model = AutoModel.from_pretrained("Geotrend/distilbert-base-it-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github r... | 6957f9efc490b0953f6a64f388b55d96 |
mit | [] | false | Rail Scene Style on Stable Diffusion This is the `<rail-pov>` 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 ... | ac1650a6eeb2627dfc0a06c75c8b407f |
apache-2.0 | [] | false | bert-base-en-it-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the ... | 4ecb1e2ee4f799a43d50e691557b560d |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-it-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-it-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](h... | 8776eb0602f24f98407464ddb2005c92 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-large-xls-r-53h-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.4135 - Wer: 0.3247 | ae421e9d91ac56003604ea68817b9b1c |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.4875 | 0.92 | 100 | 3.5328 | 1.0 | | 3.1866 | 1.83 | 200 | 3.0955 | 1.0 | | 2.027 | 2.75 | 300 | 0.9002 | 0.7685 | |... | 0e206680547af509a83129fbbcad1c9f |
apache-2.0 | ['translation'] | false | opus-mt-fr-pag * source languages: fr * target languages: pag * OPUS readme: [fr-pag](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/fr-pag/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-20.zip](http... | 7a55bcc971ebe86985f8153140b88fa4 |
mit | ['generated_from_trainer'] | false | Model description Custom data generated labeling text according to these three categories. These three categories are the subcategories of Pump - essentially when a user asks a question and expects an answer in response - Value: a slot value or a calculation - Clarification: Asking for further information on a previ... | c7d054d76ad68b2c5ef404aaf4fdc841 |
mit | ['generated_from_trainer'] | false | Intended uses & limitations from transformers import pipeline classifier = pipeline("text-classification",model="mp6kv/pump_intent_test") output = classifier("What is the value of the length of the blue object?") score = output[0]['score'] label = output[0]['label'] | de82c281a424dbfafffbdfbb7377ab6e |
apache-2.0 | ['NER'] | false | Model description **mbert-base-uncased-pcm** is a model based on the fine-tuned Multilingual BERT base uncased model. It has been trained to recognize four types of entities: - dates & time (DATE) - Location (LOC) - Organizations (ORG) - Person (PER) | 13a24909bbbcf0fdd5f23db2eebc3eac |
apache-2.0 | ['NER'] | false | Training Data This model was fine-tuned on the Nigerian Pidgin corpus **(pcm)** of the [MasakhaNER](https://github.com/masakhane-io/masakhane-ner) dataset. However, we thresholded the number of entity groups per sentence in this dataset to 10 entity groups. | 277733abf5d6ede55f41c7510fac560a |
apache-2.0 | ['NER'] | false | Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("arnolfokam/mbert-base-uncased-pcm") model = AutoModelForTokenClassification.from_pretrained("arnolfokam/mbert-base-uncased-pcm") nlp = pipeline("ner", m... | 4721c0f63829ea681d046186ca3cfbc6 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-ks 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: 0.5766 - Accuracy: 0.8308 | 5036d9f917e0e46252bae19fc0840c1d |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_... | 94a4f7564dc57dbbe6238bbc5cca3cb0 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 7 | 0.7247 | 0.7462 | | No log | 2.0 | 14 | 0.6844 | 0.7615 | | 0.4279 | 3.0 | 21 | 0.7254 | 0.... | da2cc1563466af353150cf75329723b5 |
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2180
- Accuracy: 0.9255
- F1: 0.9256
| ad2b98d772ba1188c80c65a76343f584 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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: linear
- num_epochs: 2
| ea339ee7714694f8ccefa3f2ee99a38c |
apache-2.0 | ['generated_from_trainer'] | false | Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8092 | 1.0 | 250 | 0.3066 | 0.904 | 0.9012 |
| 0.244 | 2.0 | 500 | 0.2180 | 0.9255 | 0.9256 |
| 25b0926814b7fb3876181b09efa3b340 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-5000-samples 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: - Loss: 1.0701 - Accuracy: 0.758 - F1: 0.7580 | fc488c6a39cb3fda2f601d90abc54dc3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 313 | 1.0216 | 0.744 | 0.744 | | 0.2263 | 2.0 | 626 | 1.0701 | 0.758 | 0.7580 | | 0.2263 |... | c5849c23389bbeba7671bdb19bec6937 |
apache-2.0 | ['translation'] | false | glg-spa * source group: Galician * target group: Spanish * OPUS readme: [glg-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/glg-spa/README.md) * model: transformer-align * source language(s): glg * target language(s): spa * model: transformer-align * pre-processing: normalization + Sen... | 64042a3ba7f80681be7a645edb18e622 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: glg-spa - source_languages: glg - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/glg-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['gl', 'es'] - src_constituents: {'glg'} - tgt_const... | 72b9c86bead7e5ac15c84817c05aacde |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆ Recipe このモデルは、以下の 2 つのモデルを**単純**にマージして生成されたモデルです。 <dl> <dt><a href="https://huggingface.co/andite/pastel-mix">andite/pastel-mix</a></dt> <dd>└ pastel-mix</dd> <dt><a href="https://huggingface.co/WarriorMama777/OrangeMixs">WarriorMama777/OrangeMixs</a></dt> <dd>└ AbyssOrangeMix2_sfw (AOM2s)</dd> </dl> | M... | 7169919183c2fd58e8ffa5da7b8223a5 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | ◆ Licence This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the out... | dee331eb4a61cfd7d6cec9431cda757a |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | 【和訳】 このモデルはオープンアクセスであり、すべての人が利用できます。CreativeML OpenRAIL-M ライセンスにより、権利と使用方法がさらに規定されています。CreativeML OpenRAIL ライセンスでは、次のことが規定されています。 1. モデルを使用して、違法または有害な出力またはコンテンツを意図的に作成または共有することはできません。 2. 作成者は、あなたが生成した出力に対していかなる権利も主張しません。あなたはそれらを自由に使用でき、ライセンスに設定された規定に違反してはならない使用について説明責任を負います。 3. 重みを再配布し、モデルを商用および/またはサービスとして使用することがで... | 4498041a1dec7ea2d29788a1db0bf276 |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | NMKD SD-GUI-1.8.1-NoMdl - VAE: orangemix.vae.pt  ``` Positive: (best quality)+,(masterpiece)++,(ultra detailed)++,cute girl, Negative: (low quality, worst quality)1.4, (bad anatomy)+, (inaccurate limb)1.3,bad composition, i... | f49619f2c93ccf44ad3c530564df10ef |
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | stable-diffusion-webui - VAE: orangemix.vae.pt  ``` Positive: (best quality)+,(masterpiece)++,(ultra detailed)++,cute girl,school uniform Negative: (low quality, worst quality)1.4, (bad anatomy)+, (inaccurate limb)1.3,bad ... | eb52e221bafac438ed21e83c98b4326d |
cc-by-sa-4.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | sec-bert-finetuned-finance-classification This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financ... | 5829d0f5e5094aeb3d6cba84b5f17bec |
cc-by-sa-4.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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: linear - num_epochs: 8 - mixed_precision_training: Native AMP | 2aab488c7c9a3a8f0949b3d3ea1adff3 |
cc-by-sa-4.0 | ['financial-sentiment-analysis', 'sentiment-analysis', 'sentence_50agree', 'generated_from_trainer', 'sentiment', 'finance'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.6005 | 0.99 | 71 | 0.3702 | 0.8478 | 0.8465 | 0.8491 | 0.8478 | | 0.3226 | 1.97 |... | 0d4071518ff04f9f0bfca78b8c269ed1 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 | b4aa2f797dcd5b7db05978818cfbe977 |
gpl-3.0 | [] | false | Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 100 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://pubm... | e2cf8f62ee70b8c5a00a679420328fa6 |
openrail | [] | false | это файнтюн sberai ruGPT3 small (125 млн параметров) на отредактированных пупах, сделанных из нуждиков (фить хах, джунгли, жуждики; всего около 30 минут, транскрибированные через openai whisper large). размер блока при файнтюне 1024, 25 эпох. все скрипты по инференсу модели тут https://github.com/ai-forever/ru-gpts, че... | 17d7a7dcb428e0c6474e10a4709f1443 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Whisper Small Tr - Abdallah Elbohy This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2317 - Wer: 20.8341 | 9d06860e673b23dd4e609ca643a02498 |
apache-2.0 | ['hf-asr-leaderboard', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.1786 | 0.44 | 1000 | 0.2812 | 24.5580 | | 0.1477 | 0.89 | 2000 | 0.2467 | 22.2584 | | 0.0715 | 1.33 | 3000 | 0.2399 | 21.563... | c584df1da2d347b97b3bc5a59c0b8b0f |
apache-2.0 | ['generated_from_trainer'] | false | bert-keyword-discriminator This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1310 - Precision: 0.8522 - Recall: 0.8868 - Accuracy: 0.9732 - F1: 0.8692 - Ent/precision: 0.8874 - Ent/a... | 1405be4cf77b67664fcd4438aadf121d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:----... | c0971e2a8ec97aa8affb4424b74be5eb |
mit | ['generated_from_trainer'] | false | cola_roberta-base_144_v2 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.6479 - Matthews Correlation: 0.6182 | c3bf84e8390bfe1419d2060ac9241b99 |
apache-2.0 | ['generated_from_trainer'] | false | finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3131 - Accuracy: 0.8733 - F1: 0.8766 | b5e3fd1dadf1025da0fff756a2f254fc |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad-colab This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1662 | e76e12a0c7f2847f391f4026c8ff3098 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2123 | 1.0 | 5533 | 1.1550 | | 0.95 | 2.0 | 11066 | 1.1163 | | 0.7539 | 3.0 | 16599 | 1.1662 | | c9f02bffd325caf07689866c528d6cda |
apache-2.0 | ['summarization'] | false | Metrics for model | Model Name | MM Params | Inference Time (MS) | Speedup | Rouge 2 | Rouge-L | |:---------------------------|------------:|----------------------:|----------:|----------:|----------:| | distilbart-xsum-12-1 | 222 | 90 | 2.54 | 18.31... | 23960b1c81a7ccbb430e8e3470ebde72 |
mit | ['generated_from_trainer'] | false | output_mlm This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2024 | ab030d3200631f97b03cab12470d3c0f |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epo... | fce503c1b5b607f13cef357c7e422517 |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:------:|:---------------:| | 1.5832 | 0.19 | 15000 | 1.4992 | | 1.5325 | 0.39 | 30000 | 1.4653 | | 1.4979 | 0.58 | 45000 | 1.4359 | | 1.4715 | 0.77 | 60000 | 1... | bbb6d7fe287a556408ac06827ba9a6eb |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_logit_kd_wnli_96 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3441 - Accuracy: 0.5634 | 5fbcf89cbad1b4e7a875a8cf431eff24 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.348 | 1.0 | 3 | 0.3451 | 0.5634 | | 0.3477 | 2.0 | 6 | 0.3447 | 0.5634 | | 0.3467 | 3.0 | 9 | 0.3445 | 0.... | 460c8a788fcadd8d93e86115a4e2d0d6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased_Up_Sampling_Sub_Category_SPEECH_TEXT_DISPLAY_v1 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: 4.9368 - Accuracy: 0.6114 - F1: 0.6028 | 3adf8e59c09e2e3864c73614f878192a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 0.9716 | 1.0 | 12171 | 2.5228 | 0.5722 | 0.5740 | | 0.2857 | 2.0 | 24342 | 3.0558 | 0.5947 | 0.5923 | | 0.1438 ... | 93e73ca0ffa6b368ad0bcf9a5878136f |
apache-2.0 | ['translation'] | false | nor-fin * source group: Norwegian * target group: Finnish * OPUS readme: [nor-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fin/README.md) * model: transformer-align * source language(s): nno nob * target language(s): fin * model: transformer-align * pre-processing: normalization ... | 7a62676507b278b46fe0d4dc3f541ff6 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: nor-fin - source_languages: nor - target_languages: fin - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fin/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['no', 'fi'] - src_constituents: {'nob', 'nno'} - tg... | c15aceed6ac015d80ca536aa3eb5d02a |
mit | [] | false | model by Bitset This your the Stable Diffusion model fine-tuned the person concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks person** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.researc... | 1c75fe153c6a7276f25f77529010accc |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | No log | 1.0 | 27 | 1.8564 | 35.5763 | 12.1495 | 24.0011 | 32.6505 | 93... | 676b4c740b57b0037dcdc03520306b97 |
apache-2.0 | ['generated_from_trainer'] | false | my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.7576 - Rouge1: 0.1327 - Rouge2: 0.0444 - Rougel: 0.1111 - Rougelsum: 0.1111 - Gen Len: 19.0 | 746fb683892a7e8dbb26919ddb76fb22 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 3.0485 | 0.1269 | 0.0387 | 0.1064 | 0.1065 | 19.0 | |... | eaefe56ecd53f10ad6015e30a717c511 |
creativeml-openrail-m | ['pytorch', 'diffusers', 'stable-diffusion', 'text-to-image', 'diffusion-models-class', 'dreambooth-hackathon', 'animal'] | false | DreamBooth model for the britazzleshorg concept trained by Nlpeva on the Nlpeva/British_shorthair dataset. This is a Stable Diffusion model fine-tuned on the britazzleshorg concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of britazzleshorg cat** This model was created as part of ... | 90d828f3d7e582863285b8455c33fbac |
apache-2.0 | [] | false | bert-base-en-fr-da-ja-vi-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exa... | 487a19b5626eaafb922dbc5542bf76f1 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-en-fr-da-ja-vi-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-en-fr-da-ja-vi-cased") ``` To generate other smaller versions of multilingual transformers please visit [... | f8d9bbb7967003f49c576416f9695a4b |
cc-by-4.0 | [] | false | Pat2Vec Fro a description of the framework and model, see our publication: <https://preprints.jmir.org/preprint/40755/> It is trained using the amazing gensim package version 4 and parameters were optimized with Bayesian optimization (using another amazing package, optuna). Unfortunately, this gensim model cannot be... | c00c38c2bd3d865e13e207cb6db0acbd |
cc-by-4.0 | [] | false | quick start to use the model in Python: ``` from gensim.models.doc2vec import Doc2Vec pat2vec_model = Doc2Vec.load('pat2vec_dim10.model') pat2vec_model.infer_vector(["M54.1", "J06.9", "I10.90", "R51"]) ``` | 9c5882316a8283553364597b41d84acb |
creativeml-openrail-m | ['text-to-image'] | false | Sample pictures of: sdcid (use that on your prompt)  on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2887 - Precision: 0.5703 - Recall: 0.6028 - F1: 0.5861 - Accuracy: 0.9216 | 61aebc6502cf738bc64d8dc829d30f2d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.7989 | 1.0 | 515 | 0.4610 | 0.4365 | 0.3851 | 0.4091 | 0.8867 | | 0.4088 | 2.0 |... | 037c39c8dead1c7468f647cfbbe6fe0e |
apache-2.0 | ['generated_from_keras_callback'] | false | Sounak/distilbert-finetuned This model is a fine-tuned version of [distilbert-base-uncased-distilled-squad](https://huggingface.co/distilbert-base-uncased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0422 - Validation Loss: 1.7343 - Epoch: 2 | e55e7e75317151ccb357a2c814c303e3 |
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