license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1
class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
mit | [] | false | xmod-base X-MOD is a multilingual masked language model trained on filtered CommonCrawl data containing 81 languages. It was introduced in the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) (Pfeiffer et al., NAACL 2022) and first re... | 10087fc523b53eda46b8a9c5ff2afb5d |
mit | [] | false | Tokenizer This model reuses the tokenizer of [XLM-R](https://huggingface.co/xlm-roberta-base), so you can load the tokenizer as follows: ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base") ``` | 9a03cee46fd11452b128ad829eae3060 |
mit | [] | false | Input Language Because this model uses language adapters, you need to specify the language of your input so that the correct adapter can be activated: ```python from transformers import XmodModel model = XmodModel.from_pretrained("jvamvas/xmod-base") model.set_default_language("en_XX") ``` A directory of the langua... | 136cfbe2e769d836e3c842c49910c188 |
mit | [] | false | Fine-tuning In the experiments in the original paper, the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided in the code: ```python model.freeze_embeddings_and_language_adapters() | 469747a35f7647d6de90b16997337189 |
mit | [] | false | Bias, Risks, and Limitations Please refer to the model card of [XLM-R](https://huggingface.co/xlm-roberta-base), because X-MOD has a similar architecture and has been trained on similar training data. | 8a1f25a00474c5269ed0d034b6c3b75a |
mit | [] | false | Citation **BibTeX:** ```bibtex @inproceedings{pfeiffer-etal-2022-lifting, title = "Lifting the Curse of Multilinguality by Pre-training Modular Transformers", author = "Pfeiffer, Jonas and Goyal, Naman and Lin, Xi and Li, Xian and Cross, James and Riedel, Sebastian and ... | 508696f68585180f1ef60960d4c1fe46 |
mit | [] | false | Languages This model contains the following language adapters: | lang_id (Adapter index) | Language code | Language | |-------------------------|---------------|-----------------------| | 0 | en_XX | English | | 1 | id_ID | Indone... | 5e87081e343cd0757ff931e37b38fc87 |
apache-2.0 | ['translation'] | false | opus-mt-en-ha * source languages: en * target languages: ha * OPUS readme: [en-ha](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/en-ha/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://... | 7e1a6f190fc4a902d04d51922bd18840 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-ner_only_actions 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: 0.0931 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9844 | ceefab12cd7e90c63a3140c8ad86982a |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 15 | 0.0949 | 0.0 | 0.0 | 0.0 | 0.9844 | | No log | 2.0 | 30 | 0... | 9c4d05d83e9d00a710411219c6fb43c5 |
mit | ['spacy', 'token-classification'] | false | --- tags: - spacy - token-classification language: - en model-index: - name: en_ner_fashion results: - task: name: NER type: token-classification metrics: - name: Precision type: precision value: 0.0 - name: Recall type: recall value: 0.0 - name: F... | 95d25c0322c1730a75c6e20ba4db58a7 |
afl-3.0 | [] | false | A MacBERTh model fine-tuned on SQuAD_v2. Hopefully, this will allow the model to perform well on QA tasks on historical texts.
Finetune parameters:
```
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
learning_rate=3e-5,
per_device_train_ba... | 9d0630a328b3d42877619246bfa5097a |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | If you want to use dreamlike models on your website/app/etc., check the license at the bottom first! Use the same prompts as you would for SD 1.5. Add **dreamlikeart** if the artstyle is too weak. Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a 2:3 or a 9:16 aspe... | 5fa8d8ef405db086c7764d2f4d7486df |
other | ['stable-diffusion', 'stable-diffusion-diffusers', 'text-to-image', 'art', 'artistic', 'diffusers'] | false | We've just released Dreamlike Photoreal 2.0, check it out! [https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0) <img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 400px;" width="10... | 7c53bdd1b8d52cd9e003bfbbe85db109 |
mit | ['generated_from_trainer'] | false | xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2772 - F1: 0.8368 | 497cc75869294c1e5bd670e1b60e283d |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.581 | 1.0 | 191 | 0.3798 | 0.7573 | | 0.2625 | 2.0 | 382 | 0.2806 | 0.8260 | | 0.1748 | 3.0 | 573 | 0.2772 | 0.8368 | ... | 2188e4594ff0b4ae2f7fb70fcb57a2cb |
mit | ['generated_from_trainer'] | false | codeparrot-ds-sample-gpt-small-10epoch This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0943 | 4df350a7432117ad9ca5afc3c459f87b |
mit | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 4.29 | 0.94 | 1000 | 2.8452 | | 2.3155 | 1.88 | 2000 | 2.3659 | | 1.8817 | 2.82 | 3000 | 2.2085 | | 1.6245 | 3.77 | 4000 | 2.1260 ... | 8a9608688e275236959e20b10bead08c |
apache-2.0 | [] | false | This model is used to detect **Offensive Content** in **Tamil Code-Mixed language**. The mono in the name refers to the monolingual setting, where the model is trained using only Tamil(pure and code-mixed) data. The weights are initialized from pretrained XLM-Roberta-Base and pretrained using Masked Language Modelling ... | 2853f2b40f88dc58e2e0d2c034da9c99 |
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: 0.3062 - Accuracy: 0.8833 - F1: 0.8852 | 3c2e96fb2d2cf9846c85c7a4753a06e7 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aimersd2-5 Dreambooth model trained by Allenbv 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-di... | bce8b9fb9f5795deee374f1080f76c9a |
mit | [] | false | Model Details **Model Description:** `openai-gpt` is a transformer-based language model created and released by OpenAI. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies. - **Developed by:** Alec Radford, Karthik Narasimhan, Tim Sali... | 9d074d0b34703f49d43e88a5b952465f |
mit | [] | false | How to Get Started with the Model Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: ```python >>> from transformers import pipeline, set_seed >>> generator = pipeli... | eb53c64f5cba31b1df55706a5c30036c |
mit | [] | false | Downstream Use Potential downstream uses of this model include tasks that leverage language models. In the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf), the model developers discuss evaluations of the model for tasks including natural language infer... | 20c821eeb6e1d1135171aa40b1395e44 |
mit | [] | false | Misuse and Out-of-scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. | 15c5ce032d748f21453f8a5432ea4518 |
mit | [] | false | Biases **CONTENT WARNING: Readers should be aware that language generated by this model can be disturbing or offensive to some and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology... | 49c655d2ab5c0035e36b91abec38a7bb |
mit | [] | false | Risks and Limitations The model developers also wrote in a [blog post](https://openai.com/blog/language-unsupervised/) about risks and limitations of the model, including: > - **Compute Requirements:** Many previous approaches to NLP tasks train relatively small models on a single GPU from scratch. Our approach req... | 7fdcac8867c2e937b9fbca726bc08615 |
mit | [] | false | Training Data The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf): > We use the BooksCorpus dataset ([Zhu et al., 2015](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zhu_Aligning_Books_and_ICCV_2015_paper.pdf)) for training ... | 258ede76ee1ac8fe39f2411e5441b83e |
mit | [] | false | Training Procedure The model developers [write](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf): > Our model largely follows the original transformer work [62]. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and ... | 9481329227891c35ffe39bf3412fa8e9 |
mit | [] | false | Evaluation The following evaluation information is extracted from the [associated blog post](https://openai.com/blog/language-unsupervised/). See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for further details. | 3658044e1baf2cbc2bee6a3961efd97e |
mit | [] | false | Testing Data, Factors and Metrics The model developers report that the model was evaluated on the following tasks and datasets using the listed metrics: - **Task:** Textual Entailment - **Datasets:** [SNLI](https://huggingface.co/datasets/snli), [MNLI Matched](https://huggingface.co/datasets/glue), [MNLI Mismatch... | c909e7ac2974a3b273aff92bdef71c65 |
mit | [] | false | Results The model achieves the following results without any fine-tuning (zero-shot): | Task | TE | TE | TE |TE | TE | TE | SS | SS | SS | RC | CR | CR | SA | LA | MTB | |:--------:|:--:|:----------:|:-------------:|:-----:|:----:|:---:|:---:|:---:|:--:|:----:|:--------... | f0f5b98c0507428fa0ca704a12f106e0 |
mit | [] | false | Environmental Impact The model developers [report that](https://openai.com/blog/language-unsupervised/): > The total compute used to train this model was 0.96 petaflop days (pfs-days). > 8 P600 GPU's * 30 days * 12 TFLOPS/GPU * 0.33 utilization = .96 pfs-days Carbon emissions can be estimated using the [Machine L... | 108f298c33d8fe99bbd0de13ed61d594 |
mit | [] | false | compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** 8 P600 GPUs - **Hours used:** 720 hours (30 days) - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown | bad83151fb1a84400051b01f4229cccc |
mit | [] | false | Technical Specifications See the [associated paper](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) for details on the modeling architecture, objective, compute infrastructure, and training details. | b02dcd4f56b78f5e778f7cc82a6318a7 |
mit | [] | false | Citation Information ```bibtex @article{radford2018improving, title={Improving language understanding by generative pre-training}, author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya and others}, year={2018}, publisher={OpenAI} } ``` APA: *Radford, A., Narasimhan, K., Saliman... | efd14988f8dbf95e6db1ec3504346fcb |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Portuguese-Bosque | Feature | Description | | --- | --- | | **Name** | `pt_udv25_portuguesebosque_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `exper... | 524717dbbcbe52e6cfc064386c473f2c |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (2079 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `ADJ`, `ADP`, `ADP_ADV`, `ADP_DET`, `ADP_NUM`, `ADP_PRON`, `ADP_PROPN`, `ADV`, `ADV_PRON`, ... | ed49b2eae1889ff38bb5bc6e6c0a05de |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.92 | | `TOKEN_P` | 99.93 | | `TOKEN_R` | 99.91 | | `TOKEN_ACC` | 99.99 | | `SENTS_F` | 95.82 | | `SENTS_P` | 95.40 | | `SENTS_R` | 96.25 | | `TAG_ACC` | 98.09 | | `POS_ACC` | 98.14 | | `MORPH_ACC` | 97.34 | | `DEP_UAS` | 93.85 | | `DEP_LAS` | 91.19 | | `LEMMA_A... | f0a779fbfce7b63f36328c9df1b7a60f |
mit | [] | false | model by martinma This your the Stable Diffusion model fine-tuned the hockey player concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks hockey** You can also train your own concepts and upload them to the library by using [this notebook](https://cola... | b1bc4e81dc5b0d17e88fd0b431896e88 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2_murad This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the cvbn dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2006 - eval_wer: 0.2084 - eval_runtime: 556.4634 - eval_samples_per_second: 8.985 - eval... | a5dbe0d538553da9eee9c63c3b2203b8 |
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: 0.2206 - Accuracy: 0.9255 - F1: 0.9254 | d2a86e875aef1395f2f07153db8ecc54 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8523 | 1.0 | 250 | 0.3186 | 0.908 | 0.9064 | | 0.247 | 2.0 | 500 | 0.2206 | 0.9255 | 0.9254 | | c8ce967ddf95f8978dafca0ad155ba04 |
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: 0.3045 - Accuracy: 0.88 - F1: 0.8831 | 36a8fc0db0f89f1deb0b29aea725c6dd |
cc-by-4.0 | ['question generation'] | false | Model Card of `research-backup/t5-small-squadshifts-vanilla-nyt-qg` This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (dataset_name: nyt) via [`lmqg`](https://github.com/asahi417... | ceecdd0102f2c332ab0e1b7411f3dff3 |
cc-by-4.0 | ['question generation'] | false | Overview - **Language model:** [t5-small](https://huggingface.co/t5-small) - **Language:** en - **Training data:** [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) (nyt) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-questi... | ab3354ebaed0e19dd65023b5be9dc605 |
cc-by-4.0 | ['question generation'] | false | model prediction questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-small-squadshi... | 497c623e5d7db4847b75b1009e8c6181 |
cc-by-4.0 | ['question generation'] | false | Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-small-squadshifts-vanilla-nyt-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | Score | Type | Dataset ... | b2823035c837401b732cdf8897da3418 |
cc-by-4.0 | ['question generation'] | false | Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squadshifts - dataset_name: nyt - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: ['qg'] - model: t5-small - max_length: 512 - max_length_output: 32 - epoch: 1 - batch:... | 327566553f68c6d73aabbdd045336efc |
apache-2.0 | ['generated_from_trainer'] | false | t5-small-finetuned-en-to-it This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the ccmatrix dataset. It achieves the following results on the evaluation set: - Loss: 2.2698 - Bleu: 7.3298 - Gen Len: 62.3753 | c9ad2c22cd5b505fa7d7bbc3fbdf6edf |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP | 091d2a08de0f049ed8a3d02d90130c83 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 125 | 3.0010 | 2.7294 | 56.4513 | | No log | 2.0 | 250 | 2.8999 | 2.3228 | 81.4993 | | No log | 3.0... | 51055764461071245bf0f65dfbedb6f8 |
apache-2.0 | ['grammatical error correction', 'text2text', 't5'] | false | This model is an implementation of the paper [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC). We implement the version with the T5-small with the reported F_0... | 803e21a04f45d4c0dbc24ee0eb6efd57 |
apache-2.0 | ['setfit', 'sentence-transformers', 'text-classification'] | false | fathyshalab/massive_social-roberta-large-v1-2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrast... | 8c7d35a37c38f50acfb0323892a32538 |
apache-2.0 | ['AnimeGanv2'] | false | Model Description Transforming photos of real-world scenes into anime style images is a meaningful and challenging task in terms of computer vision and artistic style transfer. AnimeGANv2_Paprika Made by Asher Chan. The official code in [here](https://github.com/TachibanaYoshino/AnimeGANv2) | 3fc4a85187a23c36d5786832896ca139 |
cc-by-4.0 | ['espnet', 'image-to-text', 'ocr', 'handwriting-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 2169367022b8939d22005e8cf45a65bb20bc0768 pip install -e . cd egs2/iam/ocr1 ./run.sh --skip_data_prep false --skip_train true --... | aae1b9b012a20ae4c73a7527c3c53e7e |
cc-by-4.0 | ['espnet', 'image-to-text', 'ocr', 'handwriting-recognition'] | false | Environments - date: `Mon Nov 7 13:40:17 EST 2022` - python version: `3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]` - espnet version: `espnet 202209` - pytorch version: `pytorch 1.10.0` - Git hash: `2169367022b8939d22005e8cf45a65bb20bc0768` - Commit date: `Thu Nov 3 20:38:03 2022 -0400` | 686c2578a447f47c334f602bfecfae56 |
cc-by-4.0 | ['espnet', 'image-to-text', 'ocr', 'handwriting-recognition'] | false | ASR config <details><summary>expand</summary> ``` config: conf/train_asr_conformer.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_conformer_extracted_en_char ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_... | 0c35d706dc0741a3b5793e3be49b5cf8 |
cc-by-4.0 | ['espnet', 'image-to-text', 'ocr', 'handwriting-recognition'] | false | ' - '&' - '7' - / - '*' - Q - X - Z - + - <sos/eos> init: xavier_uniform input_size: 100 ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null clean... | 3b9aec3849d12bbe0197657134b956cd |
other | [] | false | Tile and Grout Cleaning Richardson TX https://carpetcleaning-richardson.com/tile-and-grout-cleaning.html (972) 454-9815 We have a Cheap Tile Cleaning service that brightens your floor and gives your home a clean look if you've been putting off cleaning your tiles because of the cost.Carpet cleaning in Richardson, Texa... | 0d32ea38f1078b6de6558586093c65cb |
apache-2.0 | ['speech'] | false | Wav2Vec2-Base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as i... | 9214c6d0d92df21b208f5ba5917a9aff |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Pashto - Augmented This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the google/fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.7901 - Wer: 59.6482 - Cer: 27.0947 | ecfa1679aebe2d48f5af505aff0ca087 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_sche... | ac8ff79c6263c60176a8e5a8f4b842e1 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 1.1215 | 2.38 | 100 | 0.9444 | 68.3354 | 30.2694 | | 0.8268 | 4.75 | 200 | 0.8267 | 63.2440 | 28.2636 | | 0.6912 |... | 530a491db9b9936a28b2a1ab16c469d9 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-17jan-1 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6637 - Rouge1: 8.3942 - Rouge2: 0.8333 - Rougel: 8.2847 - Rougelsum: 8.3183 | f230a38f96a37f27969fde29645e1670 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 | 471c40d78b7dce992b37423bebfae045 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 11.5311 | 1.0 | 60 | 3.3693 | 3.5755 | 0.6 | 3.6 | 3.5118 | | 4.9804 | 2.0 | 120 ... | 1eaad733559fe4d0868f5bee892c2054 |
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': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6145, 'end_learning_ra... | 26067036ffa26ccf1a2a92f57741fe12 |
other | [] | false | Training data The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset). Most parts of the dataset have been prepended using the following text: `[Genre: <genre1>, <genre2>]` This dataset has been cleaned in th... | 64fef09f71bd8032a7f727b67e25a2e4 |
other | [] | false | How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='KoboldAI/OPT-2.7B-Nerys-v2') >>> generator("Welcome Captain Janeway, I apologize... | 0ccdd645758ce7ccf4405cc614ae1860 |
apache-2.0 | ['summarisation', 'generated_from_trainer'] | false | distilbart-xsum-6-6-finetuned-bbc-news This model is a fine-tuned version of [sshleifer/distilbart-xsum-6-6](https://huggingface.co/sshleifer/distilbart-xsum-6-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2624 - Rouge1: 62.1927 - Rouge2: 54.4754 - Rougel: 55.868 - Rouge... | ec2d71192437af3cfbd5b178c1ccfefe |
apache-2.0 | ['summarisation', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-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 - num_epochs: 8 | 8b5445c35611dfe70066dfaee7778afa |
apache-2.0 | ['summarisation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:| | 0.4213 | 1.0 | 445 | 0.2005 | 59.4886 | 51.7791 | 53.5126 | 58.3405 | | 0.1355 | 2.0 ... | 0d1c0c9e12d439d6e6b844d80ac9b2ba |
cc-by-4.0 | ['seq2seq'] | false | 🇳🇴 Norwegian T5 Base model Trained on the NCC🇳🇴 This is a Norwegian T5-base model trained on the Norwegian Colossal Corpus (NCC) on a TPU v3-8. It needs to be finetuned on a specific task before being used for anything. The following setting were used in training: ```bash ./run_t5_mlm_flax_streaming.py \ ... | 8648c7de347a15bccdcf244a824330fb |
afl-3.0 | [] | false | This model is used detecting **abusive speech** in **Bengali**. It is finetuned on MuRIL model using bengali abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABEL_1 :-> Abusive | 031d752e3cd008929dfe85f1bb61188d |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-finetuned-cifar10 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 cifar10 dataset. It achieves the following results on the evaluation set: - Loss: 0.2564 - Accuracy: 0.9788 | 8940d4467de32a0f039ad8cff624b51b |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4291 | 1.0 | 390 | 0.2564 | 0.9788 | | fee5bce1a9431c251f49b505d30c8f36 |
openrail | [] | false | Generating unprompted oracle characters using the oracle dataset. , senter, ner, attribute_ruler. | Feature | Description | | --- | --- | | **Name** | `ro_core_news_md` | | **Version** | `3.5.0` | | **spaCy** | `>=3.5.0,<3.6.0` | | **Default Pipeline** | `tok2v... | 6cca6bb64e353ebffaa087a45d3218ba |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_ACC` | 99.80 | | `TOKEN_P` | 99.67 | | `TOKEN_R` | 99.57 | | `TOKEN_F` | 99.59 | | `TAG_ACC` | 96.29 | | `SENTS_P` | 96.14 | | `SENTS_R` | 96.01 | | `SENTS_F` | 96.07 | | `DEP_UAS` | 88.56 | | `DEP_LAS` | 83.41 | | `LEMMA_ACC` | 95.32 | | `POS_ACC` | 93.68 | | `MORPH_A... | 361836d68de6a94a0e214a0288b4e727 |
apache-2.0 | ['generated_from_trainer'] | false | cvt-13-384-22k-fv-finetuned-memes This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5761 - Accuracy: 0.8315 - Precision: 0.8302 - Recall: 0.8315 - F1: 0.8292 | bb6c158d6bca29b69aafdfa19206bfc1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.3821 | 0.99 | 20 | 1.2780 | 0.4969 | 0.5083 | 0.4969 | 0.4458 | | 1.0785 | 1.99 |... | 2087242c47b9856920b708318310c57b |
apache-2.0 | ['bert-large-portuguese-cased', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Model description This model is the [`neuralmind/bert-large-portuguese-cased`](https://huggingface.co/neuralmind/bert-large-portuguese-cased) fine-tuned first on the Universal Dependencies Portuguese dataset and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following model... | 4a4577a059ab2db54a4b7fdce207b91e |
apache-2.0 | ['bert-large-portuguese-cased', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | How to use To use the transformers portion of this model: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("liaad/ud_srl-pt_bertimbau-large") model = AutoModel.from_pretrained("liaad/ud_srl-pt_bertimbau-large") ``` To use the full SRL model (transformers portion ... | a7930e465f37300bb390843911465469 |
apache-2.0 | ['bert-large-portuguese-cased', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Training procedure The model was trained on the Universal Dependencies Portuguese dataset; then on the CoNLL formatted OntoNotes v5.0; then on Portuguese semantic role labeling data (PropBank.Br) using 10-fold Cross-Validation. The 10 resulting models were tested on the folds as well as on a smaller opinion dataset "... | 61b243644e778bda4554ae9594738a8d |
apache-2.0 | ['bert-large-portuguese-cased', 'semantic role labeling', 'finetuned', 'dependency parsing'] | false | Eval results | Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | | --------------- | ------ | ----- | | `srl-pt_bertimbau-base` | 76.30 | 73.33 | | `srl-pt_bertimbau-large` | 77.42 | 74.85 | | `srl-pt_xlmr-base` | 75.22 | 72.82 | | `srl-pt_xlmr-large` | 77.59 | 73.84 ... | 97398747a7213ebf5342b21cfa981b3e |
mit | [] | false | canary cap on Stable Diffusion This is the `<canary-cap>` 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... | b2c8b7d3c4a68d8327ad7eb179e3a6c8 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2r_en_xls-r_accent_us-5_england-5_s69 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 tha... | 3e01b99753eb7a92427c76f77e27cfd4 |
apache-2.0 | ['generated_from_trainer'] | false | trialzz This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1097 | 381fd74aa522c628ab9947a7d1beb544 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 113 | 2.2090 | | No log | 2.0 | 226 | 2.1168 | | No log | 3.0 | 339 | 2.1097 | | 8da9ebb9ba2f7ecfa09a81cd75b22538 |
mit | [] | false | huayecai820 greyscale on Stable Diffusion This is the `<huayecaigreyscale-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... | 06bd56b5844c889bcf592b35a0c93569 |
mit | [] | false | model by kellempxt This your the Stable Diffusion model fine-tuned the evangelion mech unit 01 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **rendering of sks evangelion mech** You can also train your own concepts and upload them to the library by using [this ... | 5258185bc7c32bf58615163638d7883a |
apache-2.0 | ['generated_from_trainer'] | false | HateXplain-top10-majority-annotator 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: 2.2282 - Accuracy: 0.6493 | 8a749ff3a3eca2c142fb6e03a3582454 |
apache-2.0 | [] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 8 - eval_batch_size: 4 - gradient_accumulation_steps: 20 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gam... | 9fb5eabc20102b208e8e23228f09d89d |
mit | ['translation', 'generated_from_trainer'] | false | m2m100_418M-fr This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.7021 - Bleu: 51.1340 | 28675cddbe0689f2e5f2530d2fc35267 |
mit | ['translation', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | |:-------------:|:-----:|:-----:|:---------------:|:-------:| | 0.749 | 1.0 | 23645 | 0.7021 | 51.1344 | | 1f96bf4cfb8935d984c6792e3e028a81 |
apache-2.0 | ['generated_from_keras_callback'] | false | georgivelkov/bert-finetuned-squad_v2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the Squad_v2 dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5716 - Validation Loss: 0.0 - Epoch: 4 | a2e76fec8729bf25a592f26f745b9cfc |
apache-2.0 | ['generated_from_keras_callback'] | false | Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 20585, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay'... | 1f2d34697128de3010244bc55040bc95 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.3480 | 0.0 | 0 | | 0.8160 | 0.0 | 1 | | 0.6012 | 0.0 | 2 | | 0.5722 | 0.0 | 3 | | 0.5716 | 0.0 | 4 | | 2ed26fbe5fb3073f01f73c965cb1b46d |
mit | [] | false | KOJIMA Ayami on Stable Diffusion This is the `<KOJIMA>` 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 t... | 37d6b2e91c4b72a76366112169847c92 |
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