Instructions to use opsbr/eye-grep-electra-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use opsbr/eye-grep-electra-small with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'opsbr/eye-grep-electra-small');
| license: apache-2.0 | |
| library_name: transformers.js | |
| pipeline_tag: token-classification | |
| base_model: google/electra-small-discriminator | |
| base_model_relation: finetune | |
| language: | |
| - en | |
| tags: | |
| - eye-grep | |
| - log-analysis | |
| - onnx | |
| # eye-grep tagger β electra-small (distilled) | |
| The small, browser-friendly tagger for **eye-grep**, a log colorizer that highlights | |
| ids, timestamps, IPs and repeated strings in server logs. It is a **token classifier** | |
| that labels each content token of a log line with one of 11 tags. | |
| A **google/electra-small-discriminator** student (WordPiece), **knowledge-distilled** | |
| from the [opsbr/eye-grep-deberta-v3-small](https://huggingface.co/opsbr/eye-grep-deberta-v3-small) | |
| teacher (the two have different tokenizers, so distillation is done on aligned | |
| per-content-token logits). At ~14 MB int8 it is ideal for an in-browser WebGPU/WASM | |
| build β the eye-grep web demo and `eye-grep/react` use it by default. For maximum | |
| accuracy (CLI / server) use the deberta teacher. | |
| ## Tag schema (11 classes) | |
| `PUNCT WORD NUM RAND IP DURATION SIZE TIMESTAMP LEVEL URL PATH` | |
| `RAND` is a high-entropy id (uuid / hash / token); `NUM`, `SIZE`, `DURATION` are | |
| numeric values; `TIMESTAMP`, `IP`, `URL`, `PATH`, `LEVEL` are self-explanatory; | |
| `WORD`/`PUNCT` are ordinary text. | |
| ## Files | |
| ONNX in the transformers.js layout β `onnx/model.onnx` (fp32, for WebGPU) and | |
| `onnx/model_quantized.onnx` (int8, for WASM) β plus the WordPiece tokenizer. | |
| ## Usage | |
| eye-grep React component: | |
| ```tsx | |
| import { LogView } from 'eye-grep/react'; | |
| <LogView text={logs} model="opsbr/eye-grep-electra-small" device="webgpu" /> | |
| ``` | |
| transformers.js (browser): | |
| ```js | |
| import { AutoTokenizer, AutoModelForTokenClassification } from '@huggingface/transformers'; | |
| const tokenizer = await AutoTokenizer.from_pretrained('opsbr/eye-grep-electra-small'); | |
| const model = await AutoModelForTokenClassification.from_pretrained( | |
| 'opsbr/eye-grep-electra-small', { dtype: 'q8', device: 'webgpu' }); | |
| ``` | |
| ## Training data | |
| Distilled from the deberta teacher on the synthetic, fully-owned | |
| [opsbr/eye-grep](https://huggingface.co/datasets/opsbr/eye-grep) gold set | |
| (Apache-2.0) β no third-party log data. | |
| ## Notes | |
| - A distilled student: smaller and faster than the deberta teacher at some accuracy cost. | |
| - Same 11-tag schema and tokenizer-alignment contract as the teacher. | |