File size: 1,941 Bytes
3513e26 7cb2eb0 3513e26 7cb2eb0 3513e26 7cb2eb0 3513e26 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
---
tags:
- spacy
- token-classification
language:
- fr
widget:
- text: "La fouille du \"Petit Bois\" a mis au jour plusieurs tombes riches en mobilier (à l'instar de vases ornés d'animaux ou de bracelets en schiste). Des ossements de poules (Gallus gallus domesticus), d'oies (Anser anser) et de bœufs (Bos Taurus) sont également à signaler."
- text: "Château-Gaillard est un château fort édifié au XIIe siècle dans l'Eure par Richard Coeur de Lion."
model-index:
- name: fr_arches_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6778376222
- name: NER Recall
type: recall
value: 0.7156697557
- name: NER F Score
type: f_score
value: 0.6962401393
---
French model trained to recognize named entities from archaeological reports.
| Feature | Description |
| --- | --- |
| **Name** | `fr_arches_ner` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner`, `entity_punctuation_removal` |
| **Components** | `tok2vec`, `ner`, `entity_punctuation_removal` |
| **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
| **Sources** | 21 archaeological reports from the [Inrap](https://www.inrap.fr/). |
| **License** | `cc-by-nc 2.0` |
| **Author** | [Institut national de recherches archéologiques préventives](https://www.inrap.fr/) |
### Label Scheme
<details>
<summary>View label scheme (15 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `CHRONOLOGIE`, `DECOR`, `EDIFICE`, `ESPECE`, `GPE`, `ID`, `LIEUDIT_SITE`, `LOC`, `MATERIAU`, `MOBILIER`, `ORG`, `PERSONNE`, `PEUPLE_CULTURE`, `STRUCTURE`, `TECHNIQUE_STYLE` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 69.62 |
| `ENTS_P` | 67.78 |
| `ENTS_R` | 71.57 |
| `TOK2VEC_LOSS` | 63436.09 |
| `NER_LOSS` | 246059.83 | |