Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 497
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1109972056
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3473ad77aa89b51273a7cded14b2b8241ddf64e0e810389185e72273b33db738
|
| 3 |
size 1109972056
|
training_log.csv
CHANGED
|
@@ -495,3 +495,4 @@ epoch,eval_f1_macro,eval_f1_micro,eval_loss,eval_runtime,eval_samples_per_second
|
|
| 495 |
494.0,0.3496544754668595,0.7232710651828299,0.14678241312503815,14.3182,179.701,5.657,0.2615623785464438,325052
|
| 496 |
495.0,0.3497476971884754,0.7235901509134234,0.14680397510528564,14.4248,178.374,5.615,0.26039642440730665,325710
|
| 497 |
496.0,0.3500768425714955,0.7239908757314292,0.1468363255262375,14.2729,180.271,5.675,0.26039642440730665,326368
|
|
|
|
|
|
| 495 |
494.0,0.3496544754668595,0.7232710651828299,0.14678241312503815,14.3182,179.701,5.657,0.2615623785464438,325052
|
| 496 |
495.0,0.3497476971884754,0.7235901509134234,0.14680397510528564,14.4248,178.374,5.615,0.26039642440730665,325710
|
| 497 |
496.0,0.3500768425714955,0.7239908757314292,0.1468363255262375,14.2729,180.271,5.675,0.26039642440730665,326368
|
| 498 |
+
497.0,0.34985561345308214,0.7235667526284467,0.14682678878307343,14.4859,177.621,5.592,0.2592304702681695,327026
|